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149845
%%EOF
Luminescence/tests/ 0000755 0001762 0000144 00000000000 13125227601 014025 5 ustar ligges users Luminescence/tests/testthat.R 0000644 0001762 0000144 00000000104 13041732307 016004 0 ustar ligges users library(testthat)
library(Luminescence)
test_check("Luminescence")
Luminescence/tests/testthat/ 0000755 0001762 0000144 00000000000 13125301541 015661 5 ustar ligges users Luminescence/tests/testthat/test_analyse_pIRIRSequence.R 0000644 0001762 0000144 00000002677 13047374234 023225 0 ustar ligges users context("analyse_pIRIRSequence")
set.seed(1)
data(ExampleData.BINfileData, envir = environment())
object <- Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos = 1)
object <- get_RLum(object, record.id = c(-29, -30))
sequence.structure <- c(1, 2, 2, 3, 4, 4)
sequence.structure <-
as.vector(sapply(seq(0, length(object) - 1, by = 4),
function(x) {
sequence.structure + x
}))
object <- sapply(1:length(sequence.structure), function(x) {
object[[sequence.structure[x]]]
})
object <-
set_RLum(class = "RLum.Analysis",
records = object,
protocol = "pIRIR")
results <- analyse_pIRIRSequence(
object,
signal.integral.min = 1,
signal.integral.max = 2,
background.integral.min = 900,
background.integral.max = 1000,
fit.method = "EXP",
sequence.structure = c("TL", "pseudoIRSL1", "pseudoIRSL2"),
main = "Pseudo pIRIR data set based on quartz OSL",
plot = FALSE,
plot.single = TRUE,
verbose = FALSE
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(results, "RLum.Results")
expect_equal(length(results), 4)
expect_is(results$LnLxTnTx.table, "data.frame")
expect_is(results$rejection.criteria, "data.frame")
})
test_that("check output", {
testthat::skip_on_cran()
expect_equal(round(sum(results$data[1:2, 1:4]), 2),7582.62)
expect_equal(round(sum(results$rejection.criteria$Value), 2),3338.69)
})
Luminescence/tests/testthat/test_calc_gSGC.R 0000644 0001762 0000144 00000001315 13047374234 020624 0 ustar ligges users context("calc_gSGC")
set.seed(seed = 1)
temp <- calc_gSGC(data = data.frame(
LnTn = 2.361, LnTn.error = 0.087,
Lr1Tr1 = 2.744, Lr1Tr1.error = 0.091,
Dr1 = 34.4),
plot = FALSE,
verbose = FALSE
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(temp, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp$De, class = "data.frame", info = NULL, label = NULL)
expect_is(temp$De.MC, class = "list", info = NULL, label = NULL)
expect_equal(length(temp), 3)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(round(sum(temp$De), digits = 2), 30.39)
expect_equal(round(sum(temp$De.MC[[1]]), 0), 10848)
})
Luminescence/tests/testthat/test_calc_FuchsLang2001.R 0000755 0001762 0000144 00000001360 13047374234 022221 0 ustar ligges users context("calc_FuchsLang2001")
data(ExampleData.DeValues, envir = environment())
temp <- calc_FuchsLang2001(ExampleData.DeValues$BT998,
cvThreshold = 5,
plot = FALSE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 5)
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$de, 2866.11)
expect_equal(results$de_err, 157.35)
expect_equal(results$de_weighted, 2846.66)
expect_equal(results$de_weighted_err, 20.58)
expect_equal(results$n.usedDeValues, 22)
})
Luminescence/tests/testthat/test_write_RLum2CSV.R 0000644 0001762 0000144 00000000724 13050140637 021601 0 ustar ligges users context("write_RLumCSV")
test_that("test errors and general export function", {
testthat::skip_on_cran()
##test error
expect_error(write_RLum2CSV(object = "", export = FALSE),
regexp = "[write_RLum2CSV()] Object needs to be a member of the object class RLum!",
fixed = TRUE)
##test export
data("ExampleData.portableOSL", envir = environment())
expect_is(write_RLum2CSV(ExampleData.portableOSL, export = FALSE), "list")
})
Luminescence/tests/testthat/test_plot_AbanicoPlot.R 0000644 0001762 0000144 00000014077 13050334730 022310 0 ustar ligges users context("plot_AbanicoPlot()")
test_that("Test examples from the example page", {
testthat::skip_on_cran()
## load example data and recalculate to Gray
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues <- ExampleData.DeValues$CA1
## plot the example data straightforward
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues))
## now with linear z-scale
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
log.z = FALSE))
## now with output of the plot parameters
expect_is(plot_AbanicoPlot(data = ExampleData.DeValues,
output = TRUE), "list")
## now with adjusted z-scale limits
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
zlim = c(10, 200)))
## now with adjusted x-scale limits
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
xlim = c(0, 20)))
## now with rug to indicate individual values in KDE part
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
rug = TRUE))
## now with a smaller bandwidth for the KDE plot
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
bw = 0.04))
## now with a histogram instead of the KDE plot
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
hist = TRUE,
kde = FALSE))
## now with a KDE plot and histogram with manual number of bins
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
hist = TRUE,
breaks = 20))
## now with a KDE plot and a dot plot
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
dots = TRUE))
## now with user-defined plot ratio
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
plot.ratio = 0.5))
## now with user-defined central value
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = 70))
## now with median as central value
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = "median"))
## now with the 17-83 percentile range as definition of scatter
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
z.0 = "median",
dispersion = "p17"))
## now with user-defined green line for minimum age model
CAM <- calc_CentralDose(ExampleData.DeValues,
plot = FALSE)
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
line = CAM,
line.col = "darkgreen",
line.label = "CAM"))
## now create plot with legend, colour, different points and smaller scale
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
legend = "Sample 1",
col = "tomato4",
bar.col = "peachpuff",
pch = "R",
cex = 0.8))
## now without 2-sigma bar, polygon, grid lines and central value line
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
bar.col = FALSE,
polygon.col = FALSE,
grid.col = FALSE,
y.axis = FALSE,
lwd = 0))
## now with direct display of De errors, without 2-sigma bar
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
bar.col = FALSE,
ylab = "",
y.axis = FALSE,
error.bars = TRUE))
## now with user-defined axes labels
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
xlab = c("Data error (%)",
"Data precision"),
ylab = "Scatter",
zlab = "Equivalent dose [Gy]"))
## now with minimum, maximum and median value indicated
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
stats = c("min", "max", "median")))
## now with a brief statistical summary as subheader
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
summary = c("n", "in.2s")))
## now with another statistical summary
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
summary = c("mean.weighted", "median"),
summary.pos = "topleft"))
## now a plot with two 2-sigma bars for one data set
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
bar = c(30, 100)))
## now the data set is split into sub-groups, one is manipulated
data.1 <- ExampleData.DeValues[1:30,]
data.2 <- ExampleData.DeValues[31:62,] * 1.3
data.3 <- list(data.1, data.2)
## now the two data sets are plotted in one plot
expect_silent(plot_AbanicoPlot(data = data.3))
## now with some graphical modification
expect_silent(plot_AbanicoPlot(data = data.3,
z.0 = "median",
col = c("steelblue4", "orange4"),
bar.col = c("steelblue3", "orange3"),
polygon.col = c("steelblue1", "orange1"),
pch = c(2, 6),
angle = c(30, 50),
summary = c("n", "in.2s", "median")))
## create Abanico plot with predefined layout definition
expect_silent(plot_AbanicoPlot(data = ExampleData.DeValues,
layout = "journal"))
## now with predefined layout definition and further modifications
expect_silent(plot_AbanicoPlot(data = data.3,
z.0 = "median",
layout = "journal",
col = c("steelblue4", "orange4"),
bar.col = adjustcolor(c("steelblue3", "orange3"),
alpha.f = 0.5),
polygon.col = c("steelblue3", "orange3")))
## for further information on layout definitions see documentation
## of function get_Layout()
## now with manually added plot content
## create empty plot with numeric output
expect_is(plot_AbanicoPlot(data = ExampleData.DeValues,
pch = NA,
output = TRUE), "list")
})
Luminescence/tests/testthat/test_RisoeBINfileData-class.R 0000644 0001762 0000144 00000000632 13050144537 023222 0 ustar ligges users context("RisoeBINfileData Class Tests")
test_that("Check the example and the numerical values", {
testthat::skip_on_cran()
##construct empty object
temp <-
set_Risoe.BINfileData(METADATA = data.frame(), DATA = list(), .RESERVED = list())
##get function and check whether we get NULL
expect_null(get_Risoe.BINfileData(temp))
##check object
expect_is(temp, class = "Risoe.BINfileData")
})
Luminescence/tests/testthat/test_smooth_RLum.R 0000644 0001762 0000144 00000001271 13047374234 021330 0 ustar ligges users context("smooth_RLum")
data(ExampleData.CW_OSL_Curve, envir = environment())
temp <-
set_RLum(
class = "RLum.Data.Curve",
recordType = "OSL",
data = as.matrix(ExampleData.CW_OSL_Curve)
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(temp, class = "RLum.Data.Curve", info = NULL, label = NULL)
expect_is(smooth_RLum(temp), class = "RLum.Data.Curve", info = NULL, label = NULL)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equivalent(round(mean(smooth_RLum(temp, k = 5)[,2], na.rm = TRUE), 0), 100)
expect_equivalent(round(mean(smooth_RLum(temp, k = 10)[,2], na.rm = TRUE), 0), 85)
})
Luminescence/tests/testthat/test_calc_CommonDose.R 0000644 0001762 0000144 00000001154 13047374234 022105 0 ustar ligges users context("calc_CommonDose")
data(ExampleData.DeValues, envir = environment())
temp <- calc_CommonDose(ExampleData.DeValues$CA1, plot = FALSE, verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 4)
})
test_that("check values from output", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$de, digits = 5), 62.15999)
expect_equal(round(results$de_err, digits = 7), 0.7815117)
expect_true(temp@data$args$log)
expect_equal(temp@data$args$sigmab, 0)
})
Luminescence/tests/testthat/test_calc_HomogeneityTest.R 0000755 0001762 0000144 00000001130 13047374234 023166 0 ustar ligges users context("calc_HomogeneityTest")
data(ExampleData.DeValues, envir = environment())
temp <- calc_HomogeneityTest(ExampleData.DeValues$BT998,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 4)
})
test_that("check values from output example", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$n, 25)
expect_equal(results$g.value, 0.008687915)
expect_equal(results$df, 24)
expect_equal(results$P.value, 1)
})
Luminescence/tests/testthat/test_zzz.R 0000644 0001762 0000144 00000000461 13050144537 017710 0 ustar ligges users context("zzz")
test_that("Test zzz functions ... they should still work", {
testthat::skip_on_cran()
##get right answer
expect_equal(get_rightAnswer(), 46)
expect_equal(get_rightAnswer("test"), 46)
##get quote
expect_silent(get_Quote())
##tune data
expect_warning(tune_Data(1:10))
})
Luminescence/tests/testthat/test_CW2pX.R 0000644 0001762 0000144 00000005165 13047374234 017771 0 ustar ligges users context("CW2X Conversion Tests")
##load data
data(ExampleData.CW_OSL_Curve, envir = environment())
values <- CW_Curve.BosWallinga2012
test_that("Check the example and the numerical values", {
testthat::skip_on_cran()
values_pLM <- CW2pLM(values)
values_pLMi <- CW2pLMi(values, P = 1/20)
values_pLMi_alt <- CW2pLMi(values)
values_pHMi <- CW2pHMi(values, delta = 40)
values_pHMi_alt <- CW2pHMi(values)
values_pHMi_alt1 <- CW2pHMi(values, delta = 2)
values_pPMi <- CW2pPMi(values, P = 1/10)
##check conversion sum values
expect_equal(round(sum(values_pLM), digits = 0),90089)
expect_equal(round(sum(values_pLMi[,1:2]), digits = 0),197522)
expect_equal(round(sum(values_pLMi_alt[,1:2]), digits = 0),197522)
expect_equal(round(sum(values_pHMi[,1:2]), digits = 0),217431)
expect_equal(round(sum(values_pHMi_alt[,1:2]), digits = 0),217519)
expect_equal(round(sum(values_pHMi_alt1[,1:2]), digits = 0), 221083)
expect_equal(round(sum(values_pPMi[,1:2]), digits = 0),196150)
})
test_that("Test RLum.Types", {
testthat::skip_on_cran()
##load CW-OSL curve data
data(ExampleData.CW_OSL_Curve, envir = environment())
object <-
set_RLum(
class = "RLum.Data.Curve",
data = as.matrix(ExampleData.CW_OSL_Curve),
curveType = "measured",
recordType = "OSL"
)
##transform values
expect_is(CW2pLM(object), class = "RLum.Data.Curve")
expect_is(CW2pLMi(object), class = "RLum.Data.Curve")
expect_is(CW2pHMi(object), class = "RLum.Data.Curve")
expect_is(CW2pPMi(object), class = "RLum.Data.Curve")
##test error handling
expect_error(CW2pLMi(values, P = 0), regexp = "[CW2pLMi] P has to be > 0!", fixed = TRUE)
expect_warning(CW2pLMi(values, P = 10))
expect_error(object = CW2pLM(values = matrix(0, 2)))
expect_error(object = CW2pLMi(values = matrix(0, 2)))
expect_error(object = CW2pHMi(values = matrix(0, 2)))
expect_error(object = CW2pPMi(values = matrix(0, 2)))
object@recordType <- "RF"
expect_error(object = CW2pLM(values = object),
regexp = "[CW2pLM()] recordType RF is not allowed for the transformation!",
fixed = TRUE)
expect_error(object = CW2pLMi(values = object),
regexp = "[CW2pLMi()] recordType RF is not allowed for the transformation!",
fixed = TRUE)
expect_error(object = CW2pHMi(values = object),
regexp = "[CW2pHMi()] recordType RF is not allowed for the transformation!",
fixed = TRUE)
expect_error(object = CW2pPMi(values = object),
regexp = "[CW2pPMi()] recordType RF is not allowed for the transformation!",
fixed = TRUE)
})
Luminescence/tests/testthat/test_calc_Statistics.R 0000644 0001762 0000144 00000006072 13047374234 022200 0 ustar ligges users context("calc_Statistics")
## load example data
data(ExampleData.DeValues, envir = environment())
## calculate statistics and show output
set.seed(1)
temp <- calc_Statistics(ExampleData.DeValues$BT998, n.MCM = 1000)
temp_alt1 <- calc_Statistics(ExampleData.DeValues$BT998, n.MCM = 1000, digits = 2)
temp_alt2 <- calc_Statistics(ExampleData.DeValues$BT998, n.MCM = 1000, digits = NULL)
temp_RLum <- set_RLum(class = "RLum.Results", data = list(data = ExampleData.DeValues$BT998))
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("list", "vector"))
expect_equal(length(temp), 3)
})
test_that("Test certain input scenarios", {
expect_is(calc_Statistics(temp_RLum), "list")
df <- ExampleData.DeValues$BT998
df[,2] <- 0
expect_warning(calc_Statistics(df))
})
test_that("check error messages", {
testthat::skip_on_cran()
expect_error(calc_Statistics(data = matrix(0,2)),
regexp = "[calc_Statistics()] Input data is neither of type 'data.frame' nor 'RLum.Results'",
fixed = TRUE)
expect_error(calc_Statistics(data = df, weight.calc = "test"))
})
test_that("check weighted values from output", {
testthat::skip_on_cran()
expect_equal(temp$weighted$n, 25)
expect_equal(sum(unlist(temp_alt1)),24535.72)
expect_equal(sum(unlist(temp_alt2)),24534.1)
expect_equal(round(temp$weighted$mean, digits = 3), 2896.036)
expect_equal(round(temp$weighted$median, digits = 2), 2884.46)
expect_equal(round(temp$weighted$sd.abs, digits = 4), 240.2228)
expect_equal(round(temp$weighted$sd.rel, digits = 6), 8.294885)
expect_equal(round(temp$weighted$se.abs, digits = 5), 48.04457)
expect_equal(round(temp$weighted$se.rel, digits = 6), 1.658977)
expect_equal(round(temp$weighted$skewness, digits = 6), 1.342018)
expect_equal(round(temp$weighted$kurtosis, digits = 6), 4.387913)
})
test_that("check unweighted values from output", {
testthat::skip_on_cran()
expect_equal(temp$weighted$n, 25)
expect_equal(round(temp$unweighted$mean, digits = 3), 2950.818)
expect_equal(round(temp$unweighted$median, digits = 2), 2884.46)
expect_equal(round(temp$unweighted$sd.abs, digits = 4), 281.6433)
expect_equal(round(temp$unweighted$sd.rel, digits = 6), 9.544584)
expect_equal(round(temp$unweighted$se.abs, digits = 5), 56.32866)
expect_equal(round(temp$unweighted$se.rel, digits = 6), 1.908917)
expect_equal(round(temp$unweighted$skewness, digits = 6), 1.342018)
expect_equal(round(temp$unweighted$kurtosis, digits = 6), 4.387913)
})
test_that("check MCM values from output", {
expect_equal(temp$MCM$n, 25)
expect_equal(round(temp$MCM$mean, digits = 3), 2950.992)
expect_equal(round(temp$MCM$median, digits = 3), 2885.622)
expect_equal(round(temp$MCM$sd.abs, digits = 4), 295.0737)
expect_equal(round(temp$MCM$sd.rel, digits = 6), 9.999137)
expect_equal(round(temp$MCM$se.abs, digits = 5), 59.01474)
expect_equal(round(temp$MCM$se.rel, digits = 6), 1.999827)
expect_equal(round(temp$MCM$skewness, digits = 3), 1286.082)
expect_equal(round(temp$MCM$kurtosis, digits = 3), 4757.097)
})
Luminescence/tests/testthat/test_calc_FastRatio.R 0000644 0001762 0000144 00000002670 13047374234 021742 0 ustar ligges users context("calc_FastRatio")
data("ExampleData.CW_OSL_Curve")
temp <- calc_FastRatio(ExampleData.CW_OSL_Curve, plot = FALSE, verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 5)
})
test_that("check values from output", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$fast.ratio, digits = 3), 405.122)
expect_equal(round(results$fast.ratio.se, digits = 4), 119.7442)
expect_equal(round(results$fast.ratio.rse, digits = 5), 29.55756)
expect_equal(results$channels, 1000)
expect_equal(round(results$channel.width, digits = 2), 0.04)
expect_equal(results$dead.channels.start, 0)
expect_equal(results$dead.channels.end, 0)
expect_equal(results$sigmaF, 2.6e-17)
expect_equal(results$sigmaM, 4.28e-18)
expect_equal(results$stimulation.power, 30.6)
expect_equal(results$wavelength, 470)
expect_equal(results$t_L1, 0)
expect_equal(round(results$t_L2, digits = 6), 2.446413)
expect_equal(round(results$t_L3_start, digits = 5), 14.86139)
expect_equal(round(results$t_L3_end, digits = 5), 22.29208)
expect_equal(results$Ch_L1, 1)
expect_equal(results$Ch_L2, 62)
expect_equal(results$Ch_L3_start, 373)
expect_equal(results$Ch_L3_end, 558)
expect_equal(results$Cts_L1, 11111)
expect_equal(results$Cts_L2, 65)
expect_equal(round(results$Cts_L3, digits = 5), 37.66667)
})
Luminescence/tests/testthat/test_calc_AverageDose.R 0000644 0001762 0000144 00000001271 13071664642 022231 0 ustar ligges users context("calc_AverageDose")
data(ExampleData.DeValues, envir = environment())
temp <- calc_AverageDose(ExampleData.DeValues$CA1[1:56,],
sigma_m = 0.1,
plot = FALSE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 3)
})
test_that("check summary output", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$AVERAGE_DOSE, digits = 4), 65.3597)
expect_equal(round(results$SIGMA_D, digits = 4), 0.3092)
expect_equal(round(results$L_MAX, digits = 5), -19.25096)
})
Luminescence/tests/testthat/test_replicate_RLum.R 0000644 0001762 0000144 00000000376 13047374234 021774 0 ustar ligges users context("replicate_RLum")
test_that("Test replication of RLum-objects", {
skip_on_cran()
data(ExampleData.RLum.Analysis, envir = environment())
expect_silent(results <- rep(IRSAR.RF.Data[[1]], 5))
##check
expect_equal(length(results),5)
})
Luminescence/tests/testthat/test_analyse_SARCWOSL.R 0000644 0001762 0000144 00000002725 13047374234 022036 0 ustar ligges users context("analyse_SAR.CWOSL")
set.seed(1)
data(ExampleData.BINfileData, envir = environment())
object <- Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos=1)
##perform SAR analysis and set rejection criteria
results <- analyse_SAR.CWOSL(
object = object,
signal.integral.min = 1,
signal.integral.max = 2,
background.integral.min = 900,
background.integral.max = 1000,
log = "x",
fit.method = "EXP",
rejection.criteria = list(
recycling.ratio = 10,
recuperation.rate = 10,
testdose.error = 10,
palaeodose.error = 10,
exceed.max.regpoint = TRUE),
plot = FALSE,
verbose = FALSE
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(results, "RLum.Results")
expect_equal(length(results), 4)
expect_is(results$data, "data.frame")
expect_is(results$LnLxTnTx.table, "data.frame")
expect_is(results$rejection.criteria, "data.frame")
expect_is(results$Formula, "expression")
})
test_that("check De values", {
testthat::skip_on_cran()
expect_equal(object = round(sum(results$data[1:2]), digits = 2), 1717.47)
})
test_that("check LxTx table", {
testthat::skip_on_cran()
expect_equal(object = round(sum(results$LnLxTnTx.table$LxTx), digits = 5), 20.92051)
expect_equal(object = round(sum(results$LnLxTnTx.table$LxTx.Error), digits = 2), 0.34)
})
test_that("check rejection criteria", {
testthat::skip_on_cran()
expect_equal(object = round(sum(results$rejection.criteria$Value), digits = 3), 1669.348)
})
Luminescence/tests/testthat/test_fit_LMCurve.R 0000644 0001762 0000144 00000003123 13047374234 021235 0 ustar ligges users context("fit_LWCurve")
## Test 1 with NLS
data(ExampleData.FittingLM, envir = environment())
fit <- fit_LMCurve(values = values.curve,
values.bg = values.curveBG,
n.components = 3,
log = "x",
start_values = data.frame(Im = c(170,25,400), xm = c(56,200,1500)),
plot = FALSE)
test_that("check class and length of output", {
expect_equal(is(fit), c("RLum.Results", "RLum"))
expect_equal(length(fit), 3)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(fit$data$n.components, 3)
expect_equal(round(fit$data$Im1, digits = 3), 169.44)
expect_equal(round(fit$data$xm1, digits = 5), 49.00643)
expect_equal(round(fit$data$b1, digits = 5), 1.66554)
expect_equal(round(fit$data$`pseudo-R^2`, digits = 4), 0.9437)
})
## Test 2 with LM
data(ExampleData.FittingLM, envir = environment())
fit <- fit_LMCurve(values = values.curve,
values.bg = values.curveBG,
n.components = 3,
log = "x",
fit.method = "LM",
plot = FALSE)
test_that("check class and length of output", {
expect_equal(is(fit), c("RLum.Results", "RLum"))
expect_equal(length(fit), 3)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(fit$data$n.components, 3)
expect_equal(round(fit$data$Im1, digits = 3), 169.437)
expect_equal(round(fit$data$xm1, digits = 5), 49.00509)
expect_equal(round(fit$data$b1, digits = 5), 1.66563)
expect_equal(round(fit$data$`pseudo-R^2`, digits = 4), 0.9437)
})
Luminescence/tests/testthat/test_calc_ThermalLifetime.R 0000644 0001762 0000144 00000004621 13047374234 023117 0 ustar ligges users context("calc_ThermalLifetime")
##EXAMPLE 1
##calculation for two trap-depths with similar frequency factor for different temperatures
E <- c(1.66, 1.70)
s <- 1e+13
T <- 10:20
set.seed(1)
temp <- calc_ThermalLifetime(
E = E,
s = s,
T = T,
output_unit = "Ma",
verbose = FALSE
)
test_that("check class and length of output example 1", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 2)
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
expect_equal(is(temp$lifetimes), c("array", "structure", "vector"))
expect_equal(dim(temp$lifetimes), c(1, 2, 11))
##check results for 10 °C
results <- lapply(1:length(T), function(x){
temp$lifetimes[,,x]
})
expect_equal(round(results[[1]], digits = 3), c("1.66" = 1115.541, "1.7" = 5747.042))
expect_equal(round(results[[2]], digits = 4), c("1.66" = 878.0196, "1.7" = 4497.3585))
expect_equal(round(results[[3]], digits = 4), c("1.66" = 692.2329, "1.7" = 3525.4738))
expect_equal(round(results[[4]], digits = 4), c("1.66" = 546.6658, "1.7" = 2768.3216))
expect_equal(round(results[[5]], digits = 4), c("1.66" = 432.4199, "1.7" = 2177.4436))
expect_equal(round(results[[6]], digits = 4), c("1.66" = 342.6069, "1.7" = 1715.5406))
expect_equal(round(results[[7]], digits = 4), c("1.66" = 271.8854, "1.7" = 1353.8523))
expect_equal(round(results[[8]], digits = 4), c("1.66" = 216.1065, "1.7" = 1070.1642))
expect_equal(round(results[[9]], digits = 4), c("1.66" = 172.0421, "1.7" = 847.2879))
expect_equal(round(results[[10]], digits = 4), c("1.66" = 137.1765, "1.7" = 671.9020))
expect_equal(round(results[[11]], digits = 4), c("1.66" = 109.5458, "1.7" = 533.6641))
})
##EXAMPLE 2
##profiling of thermal life time for E and s and their standard error
E <- c(1.600, 0.003)
s <- c(1e+13,1e+011)
T <- 20
set.seed(1)
temp <- calc_ThermalLifetime(
E = E,
s = s,
T = T,
profiling = TRUE,
output_unit = "Ma",
verbose = FALSE,
plot = FALSE
)
test_that("check class and length of output example 2", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 2)
})
test_that("check values from output example 2", {
testthat::skip_on_cran()
expect_equal(is(temp$lifetimes), c("numeric", "vector"))
expect_equal(length(temp$lifetimes), 1000)
expect_equal(dim(temp$profiling_matrix), c(1000, 4))
})
Luminescence/tests/testthat/test_convert_X2CSV.R 0000644 0001762 0000144 00000001650 13050140636 021455 0 ustar ligges users context("convert_X2CSV")
test_that("test convert functions", {
testthat::skip_on_cran()
##test for errors
expect_error(convert_BIN2CSV(file = "", export = FALSE),
regexp = "[read_BIN2R()] File does not exist!",
fixed = TRUE)
expect_error(convert_Daybreak2CSV(file = "", export = FALSE),
regexp = "[read_Daybreak2R()] file name does not seem to exist.",
fixed = TRUE)
#expect_error(convert_PSL2CSV(file = "", export = FALSE))
expect_error(suppressWarnings(convert_XSYG2CSV(file = "", export = FALSE)))
##test conversion itself
##BIN2CSV
data(ExampleData.BINfileData, envir = environment())
expect_is(convert_BIN2CSV(subset(CWOSL.SAR.Data, POSITION == 1), export = FALSE), "list")
##XSYG2CSV
data(ExampleData.XSYG, envir = environment())
expect_is(convert_XSYG2CSV(OSL.SARMeasurement$Sequence.Object[1:10], export = FALSE), "list")
})
Luminescence/tests/testthat/test_Second2Gray.R 0000644 0001762 0000144 00000001362 13047374234 021201 0 ustar ligges users context("Second2Gray")
data(ExampleData.DeValues, envir = environment())
results <- Second2Gray(ExampleData.DeValues$BT998, c(0.2,0.01))
results_alt1 <- Second2Gray(ExampleData.DeValues$BT998, c(0.2,0.01), error.propagation = "gaussian")
results_alt2 <- Second2Gray(ExampleData.DeValues$BT998, c(0.2,0.01), error.propagation = "absolute")
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(results, class = "data.frame", info = NULL, label = NULL)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(sum(results[[1]]), 14754.09)
expect_equal(sum(results[[2]]), 507.692)
expect_equal(sum(results_alt1[[2]]), 895.911)
expect_equal(sum(results_alt2[[2]]), 1245.398)
})
Luminescence/tests/testthat/test_analyse_baSAR.R 0000644 0001762 0000144 00000002534 13047374234 021527 0 ustar ligges users context("analyse_baSAR")
##Full check
test_that("Full check of analyse_baSAR function", {
skip_on_cran()
set.seed(1)
##(1) load package test data set
data(ExampleData.BINfileData, envir = environment())
##(2) selecting relevant curves, and limit dataset
CWOSL.SAR.Data <- subset(CWOSL.SAR.Data,
subset = POSITION %in% c(1:3) & LTYPE == "OSL")
##(3) run analysis
##please not that the here selected parameters are
##choosen for performance, not for reliability
results <- analyse_baSAR(
object = CWOSL.SAR.Data,
source_doserate = c(0.04, 0.001),
signal.integral = c(1:2),
background.integral = c(80:100),
fit.method = "EXP",
method_control = list(inits = list(
list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 1),
list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 2),
list(.RNG.name = "base::Wichmann-Hill", .RNG.seed = 3)
)),
plot = FALSE,
verbose = FALSE,
n.MCMC = 1000,
txtProgressBar = FALSE
)
expect_is(
results,
class = "RLum.Results",
info = NULL,
label = NULL
)
expect_is(results$summary, "data.frame")
expect_is(results$mcmc, "mcmc.list")
expect_is(results$models, "list")
expect_equal(round(sum(results$summary[, c(6:9)]), 2), 504.69)
})
Luminescence/tests/testthat/test_calc_MinDose.R 0000755 0001762 0000144 00000002074 13047374234 021405 0 ustar ligges users context("calc_MinDose")
data(ExampleData.DeValues, envir = environment())
temp <- calc_MinDose(data = ExampleData.DeValues$CA1,
sigmab = 0.1,
verbose = FALSE,
plot = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 9)
})
test_that("check values from output example", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$de, digits = 5), 34.31834)
expect_equal(round(results$de_err, digits = 6), 2.550964)
expect_equal(results$ci_level, 0.95)
expect_equal(round(results$ci_lower, digits = 5), 29.37526)
expect_equal(round(results$ci_upper, digits = 5), 39.37503)
expect_equal(results$par, 3)
expect_equal(round(results$sig, digits = 7), 0.7287325)
expect_equal(round(results$p0, digits = 8), 0.01053938)
expect_equal(results$mu, NA)
expect_equal(round(results$Lmax, digits = 5), -43.57969)
expect_equal(round(results$BIC, digits = 4), 106.4405)
})
Luminescence/tests/testthat/test_calc_FiniteMixture.R 0000755 0001762 0000144 00000001360 13047374234 022640 0 ustar ligges users context("calc_FiniteMixture")
data(ExampleData.DeValues, envir = environment())
temp <- calc_FiniteMixture(
ExampleData.DeValues$CA1,
sigmab = 0.2,
n.components = 2,
grain.probability = TRUE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 10)
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$de[1], 31.5299)
expect_equal(results$de[2], 72.0333)
expect_equal(results$de_err[1], 3.6387)
expect_equal(results$de_err[2], 2.4082)
expect_equal(results$proportion[1], 0.1096)
expect_equal(results$proportion[2], 0.8904)
})
Luminescence/tests/testthat/test_calc_CentralDose.R 0000644 0001762 0000144 00000001320 13047374234 022240 0 ustar ligges users context("calc_CentralDose")
data(ExampleData.DeValues, envir = environment())
temp <- calc_CentralDose(
ExampleData.DeValues$CA1,
plot = FALSE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 5)
})
test_that("check summary output", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$de, digits = 5), 65.70929)
expect_equal(round(results$de_err, digits = 6), 3.053443)
expect_equal(round(results$OD, digits = 5), 34.69061)
expect_equal(round(results$OD_err, digits = 6), 3.458774)
expect_equal(round(results$Lmax, digits = 5), 31.85046)
})
Luminescence/tests/testthat/test_read_BIN2R.R 0000644 0001762 0000144 00000003332 13050325753 020663 0 ustar ligges users context("read_BIN2R")
test_that("test the import of various BIN-file versions", {
testthat::skip_on_cran()
##test for various erros
expect_error(read_BIN2R(file = ""), "[read_BIN2R()] File does not exist!", fixed = TRUE)
##this test need an internet connect ... test for it
if(!httr::http_error("https://github.com/R-Lum/Luminescence/tree/master/tests/testdata")){
##try to import every format by using the files on GitHub
##V3
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V3.bin",
txtProgressBar = FALSE), class = "Risoe.BINfileData")
##V4
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V4.bin",
txtProgressBar = FALSE), class = "Risoe.BINfileData")
##V6
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V6.binx",
txtProgressBar = FALSE), class = "Risoe.BINfileData")
##V7
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V7.binx",
txtProgressBar = FALSE), class = "Risoe.BINfileData")
##V8
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V8.binx",
txtProgressBar = FALSE), class = "Risoe.BINfileData")
##test further options
##n.records and fastForward
expect_is(
read_BIN2R(file = "https://github.com/R-Lum/Luminescence/raw/master/tests/testdata/BINfile_V4.bin",
txtProgressBar = FALSE, n.records = 1, fastForward = TRUE, verbose = FALSE), class = "list")
}
})
Luminescence/tests/testthat/test_calc_IEU.R 0000755 0001762 0000144 00000001133 13047374234 020464 0 ustar ligges users context("calc_IEU")
data(ExampleData.DeValues, envir = environment())
temp <- calc_IEU(ExampleData.DeValues$CA1,
a = 0.2,
b = 1.9,
interval = 1, verbose = FALSE, plot = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 5)
})
test_that("check values from output example", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$de, 46.67)
expect_equal(results$de_err, 2.55)
expect_equal(results$n, 24)
})
Luminescence/tests/testthat/test_calc_AliquotSize.R 0000644 0001762 0000144 00000002257 13047374234 022320 0 ustar ligges users context("calc_AliquotSize")
set.seed(1)
temp <- calc_AliquotSize(
grain.size = c(100,150),
sample.diameter = 1,
MC.iter = 100,
plot = FALSE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 2)
expect_is(temp$summary, "data.frame")
expect_is(temp$MC, "list")
})
test_that("check summary output", {
testthat::skip_on_cran()
result <- get_RLum(temp)
expect_equal(result$grain.size, 125)
expect_equal(result$sample.diameter, 1)
expect_equal(result$packing.density, 0.65)
expect_equal(result$n.grains, 42)
expect_equal(result$grains.counted, NA)
})
test_that("check MC run", {
testthat::skip_on_cran()
expect_equal(round(temp$MC$statistics$n), 100)
expect_equal(round(temp$MC$statistics$mean), 43)
expect_equal(round(temp$MC$statistics$median), 39)
expect_equal(round(temp$MC$statistics$sd.abs), 20)
expect_equal(round(temp$MC$statistics$sd.rel), 45)
expect_equal(round(temp$MC$statistics$se.abs), 2)
expect_equal(round(temp$MC$statistics$se.rel), 5)
expect_length(temp$MC$kde$x, 10000)
expect_length(temp$MC$kde$y, 10000)
})
Luminescence/tests/testthat/test_analyse_portableOSL.R 0000644 0001762 0000144 00000001200 13047374234 022752 0 ustar ligges users context("analyse_portableOSL")
data("ExampleData.portableOSL", envir = environment())
merged <- merge_RLum(ExampleData.portableOSL)
results <-
analyse_portableOSL(
merged,
signal.integral = 1:5,
invert = FALSE,
normalise = TRUE,
plot = FALSE
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(results, "RLum.Results")
expect_equal(length(results), 3)
expect_is(results$summary, "data.frame")
expect_is(results$data, "RLum.Analysis")
})
test_that("check output", {
testthat::skip_on_cran()
expect_equal(round(sum(results$summary), digits = 2), 70.44)
})
Luminescence/tests/testthat/test_plot_GrowthCurve.R 0000644 0001762 0000144 00000004436 13125226556 022403 0 ustar ligges users context("plot_GrowthCurve")
set.seed(1)
data(ExampleData.LxTxData, envir = environment())
temp_EXP <-
plot_GrowthCurve(
LxTxData,
fit.method = "EXP",
output.plot = FALSE,
verbose = FALSE,
NumberIterations.MC = 10
)
temp_LIN <-
plot_GrowthCurve(
LxTxData,
fit.method = "LIN",
output.plot = FALSE,
verbose = FALSE,
NumberIterations.MC = 10
)
temp_EXPLIN <-
plot_GrowthCurve(
LxTxData,
fit.method = "EXP+LIN",
output.plot = FALSE,
verbose = FALSE,
NumberIterations.MC = 10
)
temp_EXPEXP <-
plot_GrowthCurve(
LxTxData,
fit.method = "EXP+EXP",
output.plot = FALSE,
verbose = FALSE,
NumberIterations.MC = 10
)
temp_QDR <-
plot_GrowthCurve(
LxTxData,
fit.method = "QDR",
output.plot = FALSE,
verbose = FALSE,
NumberIterations.MC = 10
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(temp_EXP, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp_EXP$Fit, class = "nls")
expect_is(temp_LIN, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp_LIN$Fit, class = "lm")
expect_is(temp_EXPLIN, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp_EXPLIN$Fit, class = "nls")
expect_is(temp_EXPEXP, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp_EXPEXP$Fit, class = "nls")
expect_is(temp_QDR, class = "RLum.Results", info = NULL, label = NULL)
expect_is(temp_QDR$Fit, class = "lm")
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equivalent(round(temp_EXP$De[[1]], digits = 2), 1737.88)
expect_equal(round(sum(temp_EXP$De.MC, na.rm = TRUE), digits = 2), 17440.55)
expect_equivalent(round(temp_LIN$De[[1]], digits = 2), 1811.33)
expect_equal(round(sum(temp_LIN$De.MC, na.rm = TRUE), digits = 2),18238.02)
expect_equivalent(round(temp_EXPLIN$De[[1]], digits = 2), 1791.53)
expect_equal(round(sum(temp_EXPLIN$De.MC, na.rm = TRUE), digits = 2),17474.29)
expect_equivalent(round(temp_EXPEXP$De[[1]], digits = 2), 1787.15)
expect_equal(round(sum(temp_EXPEXP$De.MC, na.rm = TRUE), digits = 0), 7316)
expect_equivalent(round(temp_QDR$De[[1]], digits = 2), 1666.2)
expect_equal(round(sum(temp_QDR$De.MC, na.rm = TRUE), digits = 2), 14936.76)
})
Luminescence/tests/testthat/test_calc_MaxDose.R 0000755 0001762 0000144 00000002122 13047374234 021401 0 ustar ligges users context("calc_MaxDose")
data(ExampleData.DeValues, envir = environment())
temp <- calc_MaxDose(ExampleData.DeValues$CA1,
sigmab = 0.2,
par = 3,
plot = FALSE,
verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 9)
})
test_that("check values from output example", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$de, digits = 5), 76.57571)
expect_equal(round(results$de_err, digits = 6), 7.569908)
expect_equal(results$ci_level, 0.95)
expect_equal(round(results$ci_lower, digits = 5), 69.65358)
expect_equal(round(results$ci_upper, digits = 5), 99.32762)
expect_equal(results$par, 3)
expect_equal(round(results$sig, digits = 7), 0.5376628)
expect_equal(round(results$p0, digits = 7), 0.6482137)
expect_equal(results$mu, NA)
expect_equal(round(results$Lmax, digits = 5), -19.79245)
expect_equal(round(results$BIC, digits = 5), 58.86603)
})
Luminescence/tests/testthat/test_calc_CosmicDoseRate.R 0000644 0001762 0000144 00000003442 13047374234 022710 0 ustar ligges users context("calc_CosmicDoseRate")
temp <- calc_CosmicDoseRate(depth = 2.78, density = 1.7,
latitude = 38.06451, longitude = 1.49646,
altitude = 364, error = 10)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 3)
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$depth, 2.78)
expect_equal(results$density, 1.7)
expect_equal(results$latitude, 38.06451)
expect_equal(results$longitude, 1.49646)
expect_equal(results$altitude, 364)
expect_equal(round(results$total_absorber.gcm2, digits = 0), 473)
expect_equal(round(results$d0, digits = 3), 0.152)
expect_equal(round(results$geom_lat, digits = 1), 41.1)
expect_equal(round(results$dc, digits = 3), 0.161)
})
test_that("check values from output example 2b", {
testthat::skip_on_cran()
temp <- calc_CosmicDoseRate(depth = c(5.0, 2.78), density = c(2.65, 1.7),
latitude = 12.04332, longitude = 4.43243,
altitude = 364, corr.fieldChanges = TRUE,
est.age = 67, error = 15)
results <- get_RLum(temp)
expect_equal(results$depth.1, 5)
expect_equal(results$depth.2, 2.78)
expect_equal(results$density.1, 2.65)
expect_equal(results$density.2, 1.7)
expect_equal(results$latitude, 12.04332)
expect_equal(results$longitude, 4.43243)
expect_equal(results$altitude, 364)
expect_equal(round(results$total_absorber.gcm2, digits = 0), 1798)
expect_equal(round(results$d0, digits = 4), 0.0705)
expect_equal(round(results$geom_lat, digits = 1), 15.1)
expect_equal(round(results$dc, digits = 3), 0.072)
})
Luminescence/tests/testthat/test_write_R2BIN.R 0000644 0001762 0000144 00000003673 13125226556 021116 0 ustar ligges users context("write_R2BIN")
# Unit test for write_BIN2R() function
#
# Problem: the tests are not allowed to write on the file system, therefore, we have to run this
# manually, but we can test for some errors
#
# # Uncomment only to create new test data sets on the file system (for read_BIN2R())
# data(ExampleData.BINfileData, envir = environment())
#
# ##empty RisoeBINfileData object
# empty <- set_Risoe.BINfileData()
#
# ##replace the raw by numeric
# CWOSL.SAR.Data@METADATA$VERSION <- as.numeric(CWOSL.SAR.Data@METADATA$VERSION)
# CWOSL.SAR.Data@METADATA[] <- lapply(CWOSL.SAR.Data@METADATA, function(x){
# if(is.factor(x)){
# as.character(x)
# }else{
# x
# }
# })
#
# ##combing with existing BIN-file object
# new <- as.data.frame(
# data.table::rbindlist(l = list(empty@METADATA,CWOSL.SAR.Data@METADATA),fill = TRUE),
# stringsAsFactors = FALSE)
#
# ##new object
# new <- set_Risoe.BINfileData(METADATA = new, DATA = CWOSL.SAR.Data@DATA)
#
# ##replace NA values
# new@METADATA[is.na(new@METADATA)] <- 0
#
# ##replace RECTYPE
# new@METADATA$RECTYPE <- 1
#
# ##reduce files size considerably down to two records
# new <- subset(new, ID == 1:2)
#
# ##create files
# path <- "tests/testdata/"
# write_R2BIN(object = new, file = paste0(path, "BINfile_V3.bin"), version = "03")
# write_R2BIN(object = new, file = paste0(path, "BINfile_V4.bin"), version = "04")
# write_R2BIN(object = new, file = paste0(path, "BINfile_V6.binx"), version = "06")
# write_R2BIN(object = new, file = paste0(path, "BINfile_V7.binx"), version = "07")
# write_R2BIN(object = new, file = paste0(path, "BINfile_V8.binx"), version = "08")
test_that("write to empty connection", {
testthat::skip_on_cran()
##catch errors
expect_error(write_R2BIN(object = "a"), "[write_R2BIN()] Input object is not of type Risoe.BINfileData!", fixed = TRUE)
expect_error(write_R2BIN(object = set_Risoe.BINfileData(), file = ""))
})
Luminescence/tests/testthat/test_calc_FadingCorr.R 0000644 0001762 0000144 00000002302 13050146416 022045 0 ustar ligges users context("calc_FadingCorr")
set.seed(1)
temp <- calc_FadingCorr(
age.faded = c(0.1,0),
g_value = c(5.0, 1.0),
tc = 2592000,
tc.g_value = 172800,
n.MC = 100, verbose = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 2)
##check the verbose mode
expect_is(calc_FadingCorr(
age.faded = c(0.1,0),
g_value = c(5.0, 1.0),
tc = 2592000,
tc.g_value = 172800,
n.MC = 1, verbose = TRUE), class = "RLum.Results")
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$AGE, 0.1168)
expect_equal(results$AGE.ERROR, 0.0035)
expect_equal(results$AGE_FADED, 0.1)
expect_equal(results$AGE_FADED.ERROR, 0)
expect_equal(results$G_VALUE, 5.312393)
expect_equal(round(results$G_VALUE.ERROR, 5), 1.01190)
expect_equal(results$KAPPA, 0.02307143)
expect_equal(results$KAPPA.ERROR, 0.00439463)
expect_equal(results$TC, 8.213721e-05)
expect_equal(results$TC.G_VALUE, 5.475814e-06)
expect_equal(results$n.MC, 100)
expect_equal(results$OBSERVATIONS, 100)
expect_equal(results$SEED, NA)
})
Luminescence/tests/testthat/test_merge_RLumDataCurve.R 0000644 0001762 0000144 00000001264 13050144540 022704 0 ustar ligges users context("merge_RLum.Data.Curve")
test_that("Merge tests", {
testthat::skip_on_cran()
##load example data
data(ExampleData.XSYG, envir = environment())
TL.curves <- get_RLum(OSL.SARMeasurement$Sequence.Object, recordType = "TL (UVVIS)")
TL.curve.1 <- TL.curves[[1]]
TL.curve.3 <- TL.curves[[3]]
##check for error
expect_error(merge_RLum.Data.Curve("", merge.method = "/"))
##check various operations
expect_is(TL.curve.1 + TL.curve.3, "RLum.Data.Curve")
expect_is(TL.curve.1 - TL.curve.3, "RLum.Data.Curve")
expect_is(TL.curve.3 / TL.curve.1, "RLum.Data.Curve")
expect_warning(TL.curve.3 / TL.curve.1)
expect_is(TL.curve.1 * TL.curve.3, "RLum.Data.Curve")
})
Luminescence/tests/testthat/test_Analyse_SAROSLdata.R 0000644 0001762 0000144 00000001007 13047374234 022366 0 ustar ligges users context("Test old Analyse_SAROSLdata()")
test_that("full example test", {
testthat::skip_on_cran()
data(ExampleData.BINfileData, envir = environment())
output <- Analyse_SAR.OSLdata(input.data = CWOSL.SAR.Data,
signal.integral = c(1:5),
background.integral = c(900:1000),
position = c(1:1),
output.plot = FALSE)
##checks
expect_is(output, "list")
expect_length(output, 3)
})
Luminescence/tests/testthat/test_names_RLum.R 0000644 0001762 0000144 00000000372 13050144537 021116 0 ustar ligges users context("names_RLum")
test_that("Test whether function works", {
testthat::skip_on_cran()
data(ExampleData.RLum.Analysis, envir = environment())
expect_silent(names_RLum(IRSAR.RF.Data))
expect_is(names_RLum(IRSAR.RF.Data), "character")
})
Luminescence/tests/testthat/test_PSL2RisoeBINfiledata.R 0000644 0001762 0000144 00000000504 13047374234 022623 0 ustar ligges users context("Test PSL2Risoe.BINfileData")
test_that("simple test", {
testthat::skip_on_cran()
data("ExampleData.portableOSL", envir = environment())
merged <- merge_RLum(ExampleData.portableOSL)
bin <- PSL2Risoe.BINfileData(merged)
##checks
expect_is(bin, "Risoe.BINfileData")
expect_equal(length(bin), 70)
})
Luminescence/tests/testthat/test_plot_Functions.R 0000644 0001762 0000144 00000011341 13060567611 022063 0 ustar ligges users context("Test Various Plot Functions")
test_that("test pure success of the plotting without warning or error", {
testthat::skip_on_cran()
##distribution plots
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues <- ExampleData.DeValues$CA1
expect_silent(plot_RadialPlot(ExampleData.DeValues))
expect_silent(plot_KDE(ExampleData.DeValues))
expect_silent(plot_Histogram(ExampleData.DeValues))
expect_silent(plot_ViolinPlot(ExampleData.DeValues))
##plot NRT
data("ExampleData.BINfileData", envir = environment())
data <- Risoe.BINfileData2RLum.Analysis(object = CWOSL.SAR.Data, pos = 8, ltype = "OSL")
allCurves <- get_RLum(data)
pos <- seq(1, 9, 2)
curves <- allCurves[pos]
expect_silent(plot_NRt(curves))
##filter combinations
filter1 <- density(rnorm(100, mean = 450, sd = 20))
filter1 <- matrix(c(filter1$x, filter1$y/max(filter1$y)), ncol = 2)
filter2 <- matrix(c(200:799,rep(c(0,0.8,0),each = 200)), ncol = 2)
expect_silent(plot_FilterCombinations(filters = list(filter1, filter2)))
##plot_Det
data(ExampleData.BINfileData, envir = environment())
object <- Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos=1)
expect_is(
plot_DetPlot(
object,
signal.integral.min = 1,
signal.integral.max = 3,
background.integral.min = 900,
background.integral.max = 1000,
n.channels = 5,
),
"RLum.Results"
)
##plot DRT
data(ExampleData.DeValues, envir = environment())
expect_silent(plot_DRTResults(values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800, mtext = "Example data"))
##plot RisoeBINFileData
data(ExampleData.BINfileData, envir = environment())
expect_silent(plot_Risoe.BINfileData(CWOSL.SAR.Data,position = 1))
##various RLum plots
##RLum.Data.Curve
data(ExampleData.CW_OSL_Curve, envir = environment())
temp <- as(ExampleData.CW_OSL_Curve, "RLum.Data.Curve")
expect_silent(plot(temp))
##RLum.Data.Image
data(ExampleData.RLum.Data.Image, envir = environment())
expect_silent(plot(ExampleData.RLum.Data.Image))
##RLum.Data.Spectrum -------
data(ExampleData.XSYG, envir = environment())
expect_silent(plot(TL.Spectrum,
plot.type="contour",
xlim = c(310,750),
ylim = c(0,300)))
expect_silent(suppressWarnings(plot_RLum.Data.Spectrum(TL.Spectrum,
plot.type="persp",
xlim = c(310,750),
ylim = c(0,100),
bin.rows=10,
bin.cols = 1)))
expect_silent(suppressWarnings(plot_RLum.Data.Spectrum(TL.Spectrum,
plot.type="multiple.lines",
xlim = c(310,750),
ylim = c(0,100),
bin.rows=10,
bin.cols = 1)))
expect_silent(suppressWarnings(plot_RLum.Data.Spectrum(TL.Spectrum, plot.type="interactive",
xlim = c(310,750), ylim = c(0,300), bin.rows=10,
bin.cols = 1)))
expect_silent(suppressWarnings(plot_RLum.Data.Spectrum(TL.Spectrum, plot.type="interactive",
xlim = c(310,750), ylim = c(0,300), bin.rows=10,
bin.cols = 1,
type = "heatmap",
showscale = TRUE)))
expect_silent(suppressWarnings(plot_RLum.Data.Spectrum(TL.Spectrum, plot.type="interactive",
xlim = c(310,750), ylim = c(0,300), bin.rows=10,
bin.cols = 1,
type = "contour",
showscale = TRUE)))
expect_error(plot(TL.Spectrum,
plot.type="contour",
xlim = c(310,750),
ylim = c(0,300), bin.cols = 0))
##RLum.Analysis
data(ExampleData.BINfileData, envir = environment())
temp <- Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos=1)
expect_silent(plot(
temp,
subset = list(recordType = "TL"),
combine = TRUE,
norm = TRUE,
abline = list(v = c(110))
))
##RLum.Results
grains<- calc_AliquotSize(grain.size = c(100,150), sample.diameter = 1, plot = FALSE, MC.iter = 100)
expect_silent(plot_RLum.Results(grains))
})
test_that("test for return values, if any", {
testthat::skip_on_cran()
data(ExampleData.DeValues, envir = environment())
output <- plot_AbanicoPlot(ExampleData.DeValues, output = TRUE)
expect_is(output, "list")
expect_length(output, 10)
})
Luminescence/tests/testthat/test_get_RLum.R 0000644 0001762 0000144 00000001643 13047374234 020601 0 ustar ligges users context("get_RLum")
data(ExampleData.DeValues, envir = environment())
temp <- calc_CentralDose(ExampleData.DeValues$CA1, plot = FALSE, verbose = FALSE)
temp_RLumDataCurve <- set_RLum(class = "RLum.Data.Curve")
temp_RLumDataImage <- set_RLum(class = "RLum.Data.Image")
temp_RLumDataSpectrum <- set_RLum(class = "RLum.Data.Spectrum")
temp_RLumAnalysis <- set_RLum(class = "RLum.Analysis")
temp_RLumResults <- set_RLum(class = "RLum.Results")
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(get_RLum(temp), class = "data.frame")
expect_is(get_RLum(temp, data.object = "args"), class = "list")
##test objects
expect_is(get_RLum(temp_RLumDataCurve), class = "matrix")
expect_is(get_RLum(temp_RLumDataImage), class = "RasterBrick")
expect_is(get_RLum(temp_RLumDataSpectrum), class = "matrix")
expect_null(get_RLum(temp_RLumAnalysis))
expect_null(get_RLum(temp_RLumResults))
})
Luminescence/tests/testthat/test_calc_SourceDoseRate.R 0000755 0001762 0000144 00000001311 13047374234 022727 0 ustar ligges users context("calc_SourceDoseRate")
temp <- calc_SourceDoseRate(measurement.date = "2012-01-27",
calib.date = "2014-12-19",
calib.dose.rate = 0.0438,
calib.error = 0.0019)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 3)
})
test_that("check values from output example 1", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(round(results$dose.rate, digits = 8), 0.04695031)
expect_equal(round(results$dose.rate.error, digits = 9), 0.002036657)
expect_equal(results$date, as.Date("2012-01-27"))
})
Luminescence/tests/testthat/test_merge_RisoeBINfileData.R 0000644 0001762 0000144 00000001216 13050144537 023275 0 ustar ligges users context("merge_RisoeBINfileData")
##Full check
test_that("Test merging", {
skip_on_cran()
##expect error
expect_error(merge_Risoe.BINfileData(input.objects = "data"))
expect_error(merge_Risoe.BINfileData(input.objects = c("data", "data2")))
expect_error(merge_Risoe.BINfileData(input.objects = list("data", "data2")), regexp = "[merge_Risoe.BINfileData()] Input list does not contain Risoe.BINfileData objects!", fixed = TRUE)
##expect success
data(ExampleData.BINfileData, envir = environment())
object1 <- CWOSL.SAR.Data
object2 <- CWOSL.SAR.Data
expect_is(merge_Risoe.BINfileData(c(object1, object2)), "Risoe.BINfileData")
})
Luminescence/tests/testthat/test_fit_CWCurve.R 0000644 0001762 0000144 00000001351 13047374234 021237 0 ustar ligges users context("fit_CWCurve")
data(ExampleData.CW_OSL_Curve, envir = environment())
fit <- fit_CWCurve(values = ExampleData.CW_OSL_Curve,
main = "CW Curve Fit",
n.components.max = 4,
log = "x",
plot = FALSE)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(fit), c("RLum.Results", "RLum"))
expect_equal(length(fit), 3)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(fit$data$n.components, 3)
expect_equal(round(fit$data$I01, digits = 3), 2387.617)
expect_equal(round(fit$data$lambda1, digits = 5), 4.59054)
expect_equal(round(fit$data$`pseudo-R^2`, digits = 4), 0.9995)
})
Luminescence/tests/testthat/test_template_DRAC.R 0000644 0001762 0000144 00000000450 13050146416 021453 0 ustar ligges users context("template_DRAC")
##Full check
test_that("Check template creation ", {
skip_on_cran()
##test success
expect_is(template_DRAC(), "DRAC.list")
expect_is(template_DRAC(notification = FALSE), "DRAC.list")
expect_is(template_DRAC(nrow = 10, notification = FALSE), "DRAC.list")
})
Luminescence/tests/testthat/test_verify_SingleGrainData.R 0000644 0001762 0000144 00000000514 13047374234 023437 0 ustar ligges users context("Test verify_SingleGrainData")
test_that("Various function test", {
testthat::skip_on_cran()
data(ExampleData.XSYG, envir = environment())
output <- verify_SingleGrainData(OSL.SARMeasurement$Sequence.Object)
##return value
expect_is(output, "RLum.Results")
expect_is(output$selection_full, "data.frame")
})
Luminescence/tests/testthat/test_calc_Kars2008.R 0000644 0001762 0000144 00000003504 13047374234 021255 0 ustar ligges users context("calc_Kars2008")
set.seed(1)
data("ExampleData.Fading", envir = environment())
fading_data <- ExampleData.Fading$fading.data$IR50
data <- ExampleData.Fading$equivalentDose.data$IR50
ddot <- c(7.00, 0.004)
readerDdot <- c(0.134, 0.0067)
rhop <-
analyse_FadingMeasurement(fading_data,
plot = FALSE,
verbose = FALSE,
n.MC = 10)
kars <- calc_Kars2008(
data = data,
rhop = rhop,
ddot = ddot,
readerDdot = readerDdot,
n.MC = 50,
plot = FALSE
)
test_that("check class and length of output", {
testthat::skip_on_cran()
##rhop
expect_is(rhop, class = "RLum.Results", info = NULL, label = NULL)
expect_is(rhop$fading_results, "data.frame")
expect_is(rhop$fit, "lm")
expect_is(rhop$rho_prime, "data.frame")
##kars
expect_is(kars, class = "RLum.Results", info = NULL, label = NULL)
expect_is(kars$results, class = "data.frame", info = NULL, label = NULL)
expect_is(kars$data, class = "data.frame", info = NULL, label = NULL)
expect_is(kars$Ln, class = "numeric", info = NULL, label = NULL)
expect_is(kars$fits, class = "list", info = NULL, label = NULL)
})
test_that("check values from analyse_FadingMeasurement()", {
expect_equal(round(sum(rhop$fading_results[,1:9]),0),415)
expect_equal(round(sum(rhop$rho_prime),5),2e-05)
expect_equal(round(sum(rhop$irr.times)), 2673108)
})
test_that("check values from calc_Kars2008()", {
testthat::skip_on_cran()
expect_equal(round(sum(kars$results),0), 2417)
expect_equal(round(sum(kars$data),0), 191530)
expect_equal(round(sum(kars$Ln),4), 0.1585)
expect_equal(round(sum(residuals(kars$fits$simulated)),4), 1.2386)
expect_equal(round(sum(residuals(kars$fits$measured)),4), 0.1894)
expect_equal(round(sum(residuals(kars$fits$unfaded)),4), 1.6293)
})
Luminescence/tests/testthat/test_analyse_IRSARRF.R 0000644 0001762 0000144 00000003221 13047374234 021701 0 ustar ligges users context("analyse_IRSAR.RF")
test_that("check class and length of output", {
testthat::skip_on_cran()
set.seed(1)
data(ExampleData.RLum.Analysis, envir = environment())
results_fit <- analyse_IRSAR.RF(object = IRSAR.RF.Data, plot = FALSE, method = "FIT")
results_slide <- analyse_IRSAR.RF(object = IRSAR.RF.Data, plot = FALSE, method = "SLIDE", n.MC = NULL)
results_slide_alt <-
analyse_IRSAR.RF(
object = IRSAR.RF.Data,
plot = FALSE,
method = "SLIDE",
n.MC = 10,
method.control = list(vslide_range = 'auto', trace_vslide = TRUE),
txtProgressBar = FALSE
)
expect_equal(is(results_fit), c("RLum.Results", "RLum"))
expect_equal(length(results_fit), 5)
expect_equal(length(results_slide), 5)
expect_is(results_fit$fit, class = "nls", info = NULL, label = NULL)
expect_is(results_slide$fit, class = "nls", info = NULL, label = NULL)
expect_length(results_slide$slide, 10)
expect_equal(results_fit$data$DE, 623.25)
expect_equal(results_fit$data$DE.LOWER, 600.63)
expect_equal(results_slide$data$DE, 610.17)
expect_equal(round(results_slide_alt$data$DE, digits = 0), 384)
})
test_that("test controlled chrash conditions", {
testthat::skip_on_cran()
##the sliding range should not exceed a certrain value ... test it
data(ExampleData.RLum.Analysis, envir = environment())
expect_error(
analyse_IRSAR.RF(
object = IRSAR.RF.Data,
plot = FALSE,
method = "SLIDE",
n.MC = 10,
method.control = list(vslide_range = c(0,1e+08)),
txtProgressBar = FALSE
), regexp = "[:::.analyse_IRSAR_SRS()] 'vslide_range' exceeded maximum size (1e+08)!", fixed = TRUE)
})
Luminescence/tests/testthat/test_calc_OSLLxTxRatio.R 0000755 0001762 0000144 00000001715 13047374234 022324 0 ustar ligges users context("calc_OSLLxTxRatio")
data(ExampleData.LxTxOSLData, envir = environment())
temp <- calc_OSLLxTxRatio(
Lx.data = Lx.data,
Tx.data = Tx.data,
signal.integral = c(1:2),
background.integral = c(85:100))
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 2)
})
test_that("check values from output example", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(results$LnLx, 81709)
expect_equal(results$LnLx.BG, 530)
expect_equal(results$TnTx, 7403)
expect_equal(results$TnTx.BG, 513)
expect_equal(results$Net_LnLx, 81179)
expect_equal(round(results$Net_LnLx.Error, digits = 4), 286.5461)
expect_equal(results$Net_TnTx, 6890)
expect_equal(round(results$Net_TnTx.Error, digits = 5), 88.53581)
expect_equal(round(results$LxTx, digits = 5), 11.78215)
expect_equal(round(results$LxTx.Error, digits = 7), 0.1570077)
})
Luminescence/tests/testthat/test_analyse_SARTL.R 0000644 0001762 0000144 00000001171 13050151227 021445 0 ustar ligges users context("analyse_SAR.TL")
##Full check
test_that("Test examples", {
skip_on_cran()
##load data
data(ExampleData.BINfileData, envir = environment())
##transform the values from the first position in a RLum.Analysis object
object <- Risoe.BINfileData2RLum.Analysis(TL.SAR.Data, pos=3)
##perform analysis
##TODO ... there is a warning
expect_is(analyse_SAR.TL(object,
signal.integral.min = 210,
signal.integral.max = 220,
log = "y",
fit.method = "EXP OR LIN",
sequence.structure = c("SIGNAL", "BACKGROUND")), "RLum.Results")
})
Luminescence/tests/testthat/test_bin_RLumData.R 0000644 0001762 0000144 00000001165 13047374234 021363 0 ustar ligges users context("bin_RLum.Data")
data(ExampleData.CW_OSL_Curve, envir = environment())
curve <-
set_RLum(
class = "RLum.Data.Curve",
recordType = "OSL",
data = as.matrix(ExampleData.CW_OSL_Curve)
)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_is(bin_RLum.Data(curve), class = "RLum.Data.Curve", info = NULL, label = NULL)
expect_length(bin_RLum.Data(curve)[,1], 500)
})
test_that("check values from output example", {
testthat::skip_on_cran()
expect_equal(sum(bin_RLum.Data(curve)[,2]), 119200)
expect_equal(sum(bin_RLum.Data(curve, bin = 5)[1,2]), 41146)
})
Luminescence/tests/testthat/test_calc_TLLxTxRatio.R 0000644 0001762 0000144 00000002700 13047374234 022176 0 ustar ligges users context("calc_TLLxTxRatio")
##load package example data
data(ExampleData.BINfileData, envir = environment())
##convert Risoe.BINfileData into a curve object
temp <- Risoe.BINfileData2RLum.Analysis(TL.SAR.Data, pos = 3)
Lx.data.signal <- get_RLum(temp, record.id=1)
Lx.data.background <- get_RLum(temp, record.id=2)
Tx.data.signal <- get_RLum(temp, record.id=3)
Tx.data.background <- get_RLum(temp, record.id=4)
signal.integral.min <- 210
signal.integral.max <- 230
temp <- calc_TLLxTxRatio(Lx.data.signal,
Lx.data.background,
Tx.data.signal, Tx.data.background,
signal.integral.min, signal.integral.max)
test_that("check class and length of output", {
testthat::skip_on_cran()
expect_equal(is(temp), c("RLum.Results", "RLum"))
expect_equal(length(temp), 1)
})
test_that("check values from output", {
testthat::skip_on_cran()
results <- get_RLum(temp)
expect_equal(length(results), 10)
expect_equal(results$LnLx, 257042)
expect_equal(results$LnLx.BG, 4068)
expect_equal(results$TnTx, 82298)
expect_equal(results$TnTx.BG, 2943)
expect_equal(results$net_LnLx, 252974)
expect_equal(round(results$net_LnLx.Error, digits = 2), 49468.92)
expect_equal(results$net_TnTx, 79355)
expect_equal(round(results$net_TnTx.Error,2), 21449.72)
expect_equal(round(results$LxTx, digits = 6), 3.187877)
expect_equal(round(results$LxTx.Error, digits = 6), 1.485073)
})
Luminescence/src/ 0000755 0001762 0000144 00000000000 13125227600 013451 5 ustar ligges users Luminescence/src/analyse_IRSARRF_SRS.cpp 0000644 0001762 0000144 00000014464 13125227601 017542 0 ustar ligges users //analyse_IRSARRF_SRS.cpp
//author: Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux Montaigne (France)
//version: 0.3.5 [2017-02-06]
//Function calculates the squared residuals for the R function analyse_IRSAR.RF()
//including MC runs for the obtained minimum. The function allows a horizontal and
//a vertical sliding of the curve
//
#include
#include
// [[Rcpp::depends(RcppArmadillo)]]
using namespace Rcpp;
// [[Rcpp::export(".analyse_IRSARRF_SRS")]]
RcppExport SEXP analyse_IRSARRF_SRS(NumericVector values_regenerated_limited,
NumericVector values_natural_limited,
NumericVector vslide_range,
int n_MC,
bool trace = false
){
//check for the vslide_range()
if(vslide_range.length() > 1e+08){
stop("[:::.analyse_IRSAR_SRS()] 'vslide_range' exceeded maximum size (1e+08)!");
}
//pre-define variables
NumericVector residuals = values_natural_limited.length();
NumericVector results = values_regenerated_limited.size() - values_natural_limited.size();
NumericVector results_vector_min_MC = n_MC;
//variables for the algorithm
int v_length;
int v_index;
NumericVector v_leftright(2); //the virtual vector
NumericVector t_leftright(2); //the test points
NumericVector c_leftright(2); //the calculation
//(1) calculate sum of the squared residuals
// this will be used to find the best fit of the curves (which is the minimum)
//initialise values
v_length = vslide_range.length();
v_index = 0;
v_leftright[0] = 0;
v_leftright[1] = vslide_range.length() - 1;
if(v_length == 1){
t_leftright[0] = 0;
t_leftright[1] = 0;
}else{
t_leftright[0] = v_length/3;
t_leftright[1] = 2 * v_length/3;
}
//***TRACE****
if(trace == true){
Rcout << "\n\n [:::.analyse_IRSAR_SRS()]";
Rcout << "\n\n--- Inititalisation --- \n ";
Rcout << "\n >> v_leftright: " << v_leftright;
Rcout << "\n >> t_leftright: " << t_leftright;
Rcout << "\n\n --- Optimisation --- \n ";
Rcout << "\n ---------------------------------------------------------------------------------------------------------";
Rcout << "\n v_length \t\t v_leftright \t\t c_leftright \t\t\t\t absolute offset";
Rcout << "\n ---------------------------------------------------------------------------------------------------------";
}
//start loop
do {
for (int t=0;t c_leftright[1]){
v_index = v_leftright[1]; //set index to right test index
//update vector window (the right remains the same this time)
v_leftright[0] = t_leftright[0];
//update window length
v_length = v_leftright[1] - v_leftright[0];
}else{
v_length = 1;
}
//update test point index
t_leftright[0] = v_leftright[0] + v_length/3;
t_leftright[1] = v_leftright[0] + (2 * (v_length/3));
//***TRACE****
if(trace == true){
Rcout << "\n " << v_length << " \t\t\t " << v_leftright << " \t\t " << c_leftright << " \t\t\t " << vslide_range[v_index];
}
} while (v_length > 1);
//***TRACE****
if(trace == true){
Rcout << "\n ---------------------------------------------------------------------------------------------------------";
Rcout << "\n >> SRS minimum: \t\t " << c_leftright[0];
Rcout << "\n >> Vertical offset index: \t " << v_index + 1;
Rcout << "\n >> Vertical offset absolute: \t " << vslide_range[v_index] << "\n\n";
}
//(2) error calculation
//use this values to bootstrap and find minimum values and to account for the variation
//that may result from this method itself (the minimum lays within a valley of minima)
//
//using the obtained sliding vector and the function RcppArmadillo::sample() (which equals the
//function sample() in R, but faster)
//http://gallery.rcpp.org/articles/using-the-Rcpp-based-sample-implementation
//this follows the way described in Frouin et al., 2017 ... still ...
for (int i=0; i
#include
using namespace Rcpp;
// [[Rcpp::export(".create_UID")]]
CharacterVector create_UID() {
//define variables
CharacterVector random;
time_t rawtime;
struct tm * timeinfo;
char timestamp [80];
//set date + timestamp (code snippet taken from C++ reference page)
time (&rawtime);
timeinfo = localtime (&rawtime);
strftime (timestamp,80,"%Y-%m-%d-%I:%M.",timeinfo);
//get time information and add a random number
//according to the CRAN policy the standard C-function, rand(), even sufficient here, is not allowed
random = runif(1);
//combine and return results
return timestamp + Rcpp::as(random);
}
Luminescence/src/Luminescence_init.c 0000644 0001762 0000144 00000001662 13125227601 017260 0 ustar ligges users #include
#include
#include // for NULL
#include
/* FIXME:
Check these declarations against the C/Fortran source code.
*/
/* .Call calls */
extern SEXP Luminescence_analyse_IRSARRF_SRS(SEXP, SEXP, SEXP, SEXP, SEXP);
extern SEXP Luminescence_create_RLumDataCurve_matrix(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
extern SEXP Luminescence_create_UID();
static const R_CallMethodDef CallEntries[] = {
{"Luminescence_analyse_IRSARRF_SRS", (DL_FUNC) &Luminescence_analyse_IRSARRF_SRS, 5},
{"Luminescence_create_RLumDataCurve_matrix", (DL_FUNC) &Luminescence_create_RLumDataCurve_matrix, 10},
{"Luminescence_create_UID", (DL_FUNC) &Luminescence_create_UID, 0},
{NULL, NULL, 0}
};
void R_init_Luminescence(DllInfo *dll)
{
R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
R_useDynamicSymbols(dll, FALSE);
}
Luminescence/src/create_RLumDataCurve_matrix.cpp 0000644 0001762 0000144 00000005266 13125227601 021554 0 ustar ligges users //create_RLumDataCurve_matrix.cpp
//author: Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux Montaigne (France)
//version: 0.1.0 [2016-06-28]
//Function to create the RLum.Data.Curve() matrix ... faster than in R itself
#include
using namespace Rcpp;
// -----------------------------------------------------------------------------------------------
// Define own function to create a sequence for the x-axis
// .. but we do not export them to avoid side effects, as this function is not the same as the
// .. base R function seq()
// ..no export
NumericVector seq(int from, int to, double length_out) {
//set variables
NumericVector sequence = length_out;
double by = (to - from) / (length_out - 1);
//loop and create sequence
for (int i=0; i < length_out; ++i){
if(i == 0){
sequence[i] = from;
}else{
sequence[i] = sequence[i-1] + by;
}
}
return sequence;
}
// -----------------------------------------------------------------------------------------------
// The function we want to export
// [[Rcpp::export(".create_RLumDataCurve_matrix")]]
NumericMatrix create_RLumDataCurve_matrix(
NumericVector DATA,
int VERSION,
int NPOINTS,
String LTYPE,
int LOW,
int HIGH,
int AN_TEMP,
int TOLDELAY,
int TOLON,
int TOLOFF
){
//generate X vectors
if(NPOINTS > 0){
//set needed vectors and predefine matrix
NumericVector X = NPOINTS;
NumericMatrix curve_matrix(NPOINTS, 2);
//fill x column for the case we have a TL curve
if(LTYPE == "TL" && VERSION >= 4){
//the heating curve consists of three vectors that needed to
//be combined
//
//(A) - the start ramping
NumericVector heat_ramp_start = seq(LOW,AN_TEMP,TOLDELAY);
//
//(B) - the plateau
//B is simply TOLON
//
//(C) - the end ramping
NumericVector heat_ramp_end = seq(AN_TEMP, HIGH, TOLOFF);
//set index counters
int c = 0;
//fill vector for temperature
for(int i = 0; i < X.length(); i++){
if(i < heat_ramp_start.length()){
X[i] = heat_ramp_start[i];
}else if(i >= heat_ramp_start.length() && i < heat_ramp_start.length() + TOLON){
X[i] = AN_TEMP;
}else if(i >= heat_ramp_start.length() + TOLON){
X[i] = heat_ramp_end[c];
c++;
}
}
}else{
X = seq(LOW, HIGH, NPOINTS);
}
//set final matrix
curve_matrix.column(0) = X;
curve_matrix.column(1) = DATA;
return(curve_matrix);
}else{
//set final matrix
NumericMatrix curve_matrix(1, 2);
curve_matrix(0,0) = NumericVector::get_na();
curve_matrix(0,1) = NumericVector::get_na();
return(curve_matrix);
}
}
Luminescence/src/RcppExports.cpp 0000644 0001762 0000144 00000005510 13125227601 016450 0 ustar ligges users // Generated by using Rcpp::compileAttributes() -> do not edit by hand
// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#include
#include
using namespace Rcpp;
// analyse_IRSARRF_SRS
RcppExport SEXP analyse_IRSARRF_SRS(NumericVector values_regenerated_limited, NumericVector values_natural_limited, NumericVector vslide_range, int n_MC, bool trace);
RcppExport SEXP Luminescence_analyse_IRSARRF_SRS(SEXP values_regenerated_limitedSEXP, SEXP values_natural_limitedSEXP, SEXP vslide_rangeSEXP, SEXP n_MCSEXP, SEXP traceSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< NumericVector >::type values_regenerated_limited(values_regenerated_limitedSEXP);
Rcpp::traits::input_parameter< NumericVector >::type values_natural_limited(values_natural_limitedSEXP);
Rcpp::traits::input_parameter< NumericVector >::type vslide_range(vslide_rangeSEXP);
Rcpp::traits::input_parameter< int >::type n_MC(n_MCSEXP);
Rcpp::traits::input_parameter< bool >::type trace(traceSEXP);
rcpp_result_gen = Rcpp::wrap(analyse_IRSARRF_SRS(values_regenerated_limited, values_natural_limited, vslide_range, n_MC, trace));
return rcpp_result_gen;
END_RCPP
}
// create_RLumDataCurve_matrix
NumericMatrix create_RLumDataCurve_matrix(NumericVector DATA, int VERSION, int NPOINTS, String LTYPE, int LOW, int HIGH, int AN_TEMP, int TOLDELAY, int TOLON, int TOLOFF);
RcppExport SEXP Luminescence_create_RLumDataCurve_matrix(SEXP DATASEXP, SEXP VERSIONSEXP, SEXP NPOINTSSEXP, SEXP LTYPESEXP, SEXP LOWSEXP, SEXP HIGHSEXP, SEXP AN_TEMPSEXP, SEXP TOLDELAYSEXP, SEXP TOLONSEXP, SEXP TOLOFFSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< NumericVector >::type DATA(DATASEXP);
Rcpp::traits::input_parameter< int >::type VERSION(VERSIONSEXP);
Rcpp::traits::input_parameter< int >::type NPOINTS(NPOINTSSEXP);
Rcpp::traits::input_parameter< String >::type LTYPE(LTYPESEXP);
Rcpp::traits::input_parameter< int >::type LOW(LOWSEXP);
Rcpp::traits::input_parameter< int >::type HIGH(HIGHSEXP);
Rcpp::traits::input_parameter< int >::type AN_TEMP(AN_TEMPSEXP);
Rcpp::traits::input_parameter< int >::type TOLDELAY(TOLDELAYSEXP);
Rcpp::traits::input_parameter< int >::type TOLON(TOLONSEXP);
Rcpp::traits::input_parameter< int >::type TOLOFF(TOLOFFSEXP);
rcpp_result_gen = Rcpp::wrap(create_RLumDataCurve_matrix(DATA, VERSION, NPOINTS, LTYPE, LOW, HIGH, AN_TEMP, TOLDELAY, TOLON, TOLOFF));
return rcpp_result_gen;
END_RCPP
}
// create_UID
CharacterVector create_UID();
RcppExport SEXP Luminescence_create_UID() {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
rcpp_result_gen = Rcpp::wrap(create_UID());
return rcpp_result_gen;
END_RCPP
}
Luminescence/NAMESPACE 0000644 0001762 0000144 00000016322 13125227567 014121 0 ustar ligges users # Generated by roxygen2: do not edit by hand
S3method("$",RLum.Analysis)
S3method("$",RLum.Data.Curve)
S3method("$",RLum.Results)
S3method("$<-",DRAC.list)
S3method("*",RLum.Data.Curve)
S3method("+",RLum.Data.Curve)
S3method("-",RLum.Data.Curve)
S3method("/",RLum.Data.Curve)
S3method("[",RLum.Analysis)
S3method("[",RLum.Data.Curve)
S3method("[",RLum.Data.Image)
S3method("[",RLum.Data.Spectrum)
S3method("[",RLum.Results)
S3method("[<-",DRAC.list)
S3method("[<-",RLum.Data.Curve)
S3method("[[",RLum.Analysis)
S3method("[[",RLum.Results)
S3method("[[<-",DRAC.list)
S3method(as.data.frame,DRAC.list)
S3method(as.data.frame,RLum.Data.Curve)
S3method(as.data.frame,RLum.Data.Spectrum)
S3method(as.list,RLum.Analysis)
S3method(as.list,RLum.Data.Curve)
S3method(as.list,RLum.Results)
S3method(as.matrix,RLum.Data.Curve)
S3method(as.matrix,RLum.Data.Spectrum)
S3method(dim,RLum.Data.Curve)
S3method(dim,RLum.Data.Spectrum)
S3method(hist,RLum.Analysis)
S3method(hist,RLum.Data.Curve)
S3method(hist,RLum.Data.Image)
S3method(hist,RLum.Results)
S3method(length,RLum.Analysis)
S3method(length,RLum.Data.Curve)
S3method(length,RLum.Results)
S3method(length,Risoe.BINfileData)
S3method(merge,RLum)
S3method(names,RLum.Analysis)
S3method(names,RLum.Data.Curve)
S3method(names,RLum.Data.Image)
S3method(names,RLum.Data.Spectrum)
S3method(names,RLum.Results)
S3method(names,Risoe.BINfileData)
S3method(plot,RLum.Analysis)
S3method(plot,RLum.Data.Curve)
S3method(plot,RLum.Data.Image)
S3method(plot,RLum.Data.Spectrum)
S3method(plot,RLum.Results)
S3method(plot,Risoe.BINfileData)
S3method(plot,list)
S3method(print,DRAC.highlights)
S3method(print,DRAC.list)
S3method(rep,RLum)
S3method(row.names,RLum.Data.Spectrum)
S3method(subset,RLum.Analysis)
S3method(subset,Risoe.BINfileData)
S3method(summary,RLum.Analysis)
S3method(summary,RLum.Data.Curve)
S3method(summary,RLum.Data.Image)
S3method(summary,RLum.Results)
S3method(unlist,RLum.Analysis)
export(Analyse_SAR.OSLdata)
export(CW2pHMi)
export(CW2pLM)
export(CW2pLMi)
export(CW2pPMi)
export(PSL2Risoe.BINfileData)
export(Risoe.BINfileData2RLum.Analysis)
export(Second2Gray)
export(analyse_FadingMeasurement)
export(analyse_IRSAR.RF)
export(analyse_SAR.CWOSL)
export(analyse_SAR.TL)
export(analyse_baSAR)
export(analyse_pIRIRSequence)
export(analyse_portableOSL)
export(app_RLum)
export(apply_CosmicRayRemoval)
export(apply_EfficiencyCorrection)
export(bin.RLum.Data.Curve)
export(bin_RLum.Data)
export(calc_AliquotSize)
export(calc_AverageDose)
export(calc_CentralDose)
export(calc_CommonDose)
export(calc_CosmicDoseRate)
export(calc_FadingCorr)
export(calc_FastRatio)
export(calc_FiniteMixture)
export(calc_FuchsLang2001)
export(calc_HomogeneityTest)
export(calc_IEU)
export(calc_Kars2008)
export(calc_MaxDose)
export(calc_MinDose)
export(calc_OSLLxTxRatio)
export(calc_SourceDoseRate)
export(calc_Statistics)
export(calc_TLLxTxRatio)
export(calc_ThermalLifetime)
export(calc_gSGC)
export(convert_BIN2CSV)
export(convert_Daybreak2CSV)
export(convert_PSL2CSV)
export(convert_XSYG2CSV)
export(extract_IrradiationTimes)
export(fit_CWCurve)
export(fit_LMCurve)
export(get_Layout)
export(get_Quote)
export(get_RLum)
export(get_Risoe.BINfileData)
export(get_rightAnswer)
export(github_branches)
export(github_commits)
export(github_issues)
export(install_DevelopmentVersion)
export(is.RLum)
export(is.RLum.Analysis)
export(is.RLum.Data)
export(is.RLum.Data.Curve)
export(is.RLum.Data.Image)
export(is.RLum.Data.Spectrum)
export(is.RLum.Results)
export(length_RLum)
export(merge_RLum)
export(merge_RLum.Analysis)
export(merge_RLum.Data.Curve)
export(merge_RLum.Results)
export(merge_Risoe.BINfileData)
export(model_LuminescenceSignals)
export(names_RLum)
export(plot_AbanicoPlot)
export(plot_DRTResults)
export(plot_DetPlot)
export(plot_FilterCombinations)
export(plot_GrowthCurve)
export(plot_Histogram)
export(plot_KDE)
export(plot_NRt)
export(plot_RLum)
export(plot_RLum.Analysis)
export(plot_RLum.Data.Curve)
export(plot_RLum.Data.Image)
export(plot_RLum.Data.Spectrum)
export(plot_RLum.Results)
export(plot_RadialPlot)
export(plot_Risoe.BINfileData)
export(plot_ViolinPlot)
export(read_BIN2R)
export(read_Daybreak2R)
export(read_PSL2R)
export(read_SPE2R)
export(read_XSYG2R)
export(replicate_RLum)
export(report_RLum)
export(sTeve)
export(set_RLum)
export(set_Risoe.BINfileData)
export(smooth_RLum)
export(structure_RLum)
export(template_DRAC)
export(tune_Data)
export(use_DRAC)
export(verify_SingleGrainData)
export(write_R2BIN)
export(write_RLum2CSV)
exportClasses(RLum)
exportClasses(RLum.Analysis)
exportClasses(RLum.Data)
exportClasses(RLum.Data.Curve)
exportClasses(RLum.Data.Image)
exportClasses(RLum.Data.Spectrum)
exportClasses(RLum.Results)
exportClasses(Risoe.BINfileData)
exportMethods(bin_RLum.Data)
exportMethods(get_RLum)
exportMethods(get_Risoe.BINfileData)
exportMethods(length_RLum)
exportMethods(names_RLum)
exportMethods(replicate_RLum)
exportMethods(set_RLum)
exportMethods(set_Risoe.BINfileData)
exportMethods(show)
exportMethods(smooth_RLum)
exportMethods(structure_RLum)
import(data.table)
import(magrittr)
import(methods)
import(utils)
importClassesFrom(raster,RasterBrick)
importFrom(grDevices,adjustcolor)
importFrom(grDevices,axisTicks)
importFrom(grDevices,colorRampPalette)
importFrom(grDevices,dev.off)
importFrom(grDevices,gray.colors)
importFrom(grDevices,rgb)
importFrom(grDevices,topo.colors)
importFrom(grDevices,xy.coords)
importFrom(graphics,abline)
importFrom(graphics,arrows)
importFrom(graphics,axTicks)
importFrom(graphics,axis)
importFrom(graphics,barplot)
importFrom(graphics,box)
importFrom(graphics,boxplot)
importFrom(graphics,contour)
importFrom(graphics,curve)
importFrom(graphics,frame)
importFrom(graphics,grconvertX)
importFrom(graphics,grconvertY)
importFrom(graphics,grid)
importFrom(graphics,hist)
importFrom(graphics,layout)
importFrom(graphics,legend)
importFrom(graphics,lines)
importFrom(graphics,mtext)
importFrom(graphics,par)
importFrom(graphics,persp)
importFrom(graphics,plot)
importFrom(graphics,plot.default)
importFrom(graphics,points)
importFrom(graphics,polygon)
importFrom(graphics,rug)
importFrom(graphics,segments)
importFrom(graphics,text)
importFrom(graphics,title)
importFrom(httr,GET)
importFrom(httr,accept_json)
importFrom(httr,content)
importFrom(httr,status_code)
importFrom(parallel,makeCluster)
importFrom(parallel,parLapply)
importFrom(parallel,stopCluster)
importFrom(raster,brick)
importFrom(raster,contour)
importFrom(raster,nlayers)
importFrom(raster,plot)
importFrom(raster,plotRGB)
importFrom(raster,raster)
importFrom(stats,approx)
importFrom(stats,as.formula)
importFrom(stats,coef)
importFrom(stats,complete.cases)
importFrom(stats,confint)
importFrom(stats,density)
importFrom(stats,dnorm)
importFrom(stats,glm)
importFrom(stats,lm)
importFrom(stats,median)
importFrom(stats,na.exclude)
importFrom(stats,na.omit)
importFrom(stats,nls)
importFrom(stats,nls.control)
importFrom(stats,pchisq)
importFrom(stats,pnorm)
importFrom(stats,predict)
importFrom(stats,quantile)
importFrom(stats,residuals)
importFrom(stats,rnorm)
importFrom(stats,runif)
importFrom(stats,sd)
importFrom(stats,setNames)
importFrom(stats,smooth)
importFrom(stats,smooth.spline)
importFrom(stats,spline)
importFrom(stats,t.test)
importFrom(stats,uniroot)
importFrom(stats,update)
importFrom(stats,var)
importFrom(stats,weighted.mean)
useDynLib(Luminescence, .registration = TRUE)
Luminescence/NEWS 0000644 0001762 0000144 00000043544 13125227571 013402 0 ustar ligges users NEWS for the R Package Luminescence
Changes in version 0.7.5 (30th June, 2017):
Bugfixes and changes:
• ‘analyse_SAR.CWOSL()’
• If the signal integral was wrong, the default value was
not set correctly (#46).
• ‘calc_AverageDose()’
• Update documentation and add produced output,
• unify data.frame return output arguments (all capital
letters).
• ‘calc_FastRatio()’
• Update slot names, which led to an output error.
• ‘extract_IrradiationTimes()’
• The exported BINX-file now works with the Analyst and
the g-value can be calculated therein (thanks to Geoff
Duller).
• ‘plot_FilterCombinations()’
• Calculate optical density and return it,
• fix calclation of transmission window,
• improve plot output.
• ‘plot_RadialPlot()’
• Fix error which occasionally occurred if a list of
‘data.frame’s are provided (thanks to Christina Neudorf
for spotting the bug).
• ‘read_BIN2R()’
• Improve error messages for corrupted BIN/BINX-files,
• ensure that the file connection is closed sufficiently.
• ‘RisoeBINfileData2RLum.Analysis()’
• The grain selection was not accepted and caused a
constant error (#45).
• ‘use_DRAC()’
• The DRAC URL had changed; fixed.
Miscellaneous:
• Fix package welcome message.
Changes in version 0.7.4 (31st March, 2017):
Changes in S4-classes and methods:
• ‘get_RLum’ for ‘RLum.Analysis’-objects now returns an error
and ‘NULL’ if the ‘record.id’ is not valid.
Bugfixes and changes:
• ‘analyse_baSAR()’
• The option to force the dose response curve trough the
origin was not correctly implemented; fixed.
• ‘analyse_FadingMeasurement()’
• The function returned unreliable results since the time
since irradiation had been doubled. This bug only
affected Lx/Tx data imported from an XSYG-file.
• ‘analyse_SAR.TL()’
• A test code snippet made it into the final package. With
this the Lx/Tx error was taken as fixed value (10/100)
from the Lx/Tx value itself. The calculated error was
not considered; corrected,
• function returns ‘NA’ for the error if the background
signals are similar and the error would become 0,
• new argument ‘integral_input’ added to allow for an
integral definition based on temperatures and not
channels.
• ‘calc_TLLxTxRatio()’
• Arguments ‘Lx.data.background’ and ‘Tx.data.background’
are now pre-set to ‘NULL’, i.e. the function does not
longer check for missing entries.
• ‘plot_KDE()’
• Further support for layout options as requested by
Christopher Luethgens.
• ‘plot_GrowthCurve)’
• Rename argument options for argument ‘mode’ to
‘'interpolation'’ and ‘'extrapolation'’ instead of
‘'regenerative'’ and ‘'additive'’.
• fix a rather rare bug using the combination
‘fit.force_through_origin = FALSE’ and ‘mode =
"extrapolation"’,
• the graphical representation for ‘mode =
"extrapolation"’ was not correct (#38).
• ‘plot_RLum.Data.Spectrum)’
• Fixwrong axtick labels for interactive plot option
(#39),
• correct manual.
• ‘plot_RLum.Analysis)’
• Add support for the argument 'type' of the argument
'combine = TRUE' is used.
• ‘read_BIN2R()’
• Correct minor bug while importing corrupt BIN-files,
• add support for internet connections,
• if a directory was provided the functions was trapped in
an endless loop (#36)
• ‘write_R2BIN()’
• Argument 'BL_UNIT' was not correctly exported; fixed,
• export behaviour for BIN-file version 08 improved.
Miscellaneous:
• BIN-file example data sets can now be exported without error
to BIN-files using ‘write_R2BIN()’.
Changes in version 0.7.3 (8th Feburary, 2017):
Bugfixes and changes:
• ‘Risoe.BINfileData()’
• Correct for mistakes in the manual.
• ‘write_R2BIN()’
• Correct for broken function (introduced with v0.7.0).
Miscellaneous:
• Correct wrong package date format.
• Add NEWS again to the package.
Changes in version 0.7.2 (7th February (evening), 2017):
• The CRAN check on the Solaris machines gave an error while
performing the (on all other platform sucessful) unit tests.
Consequently, and to reduce the load for the CRAN resources
all tests are skipped on CRAN.
• This version never made it on CRAN!
Changes in version 0.7.1 (6th February (evening), 2017):
• This release accounts for the CRAN check errors on the
Solaris machines by preventing the unfortunate overload of
the C++ function pow() with integer values.
Changes in version 0.7.0 (6th February (morning), 2017):
New functions:
• ‘analyse_FadingMeasurement()’: Analyse fading measurements
to calculate g-values and to estimate the density of
recombination centres.
• ‘analyse_portableOSL()’: The function analyses CW-OSL curve
data produced by a SUERC portable OSL reader and produces a
combined plot of OSL/IRSL signal intensities, OSL/IRSL
depletion ratios and the IRSL/OSL ratio.
• ‘calc_Kars2008()’: A function to calculate the expected
sample specific fraction of saturation following Kars et al.
(2008) and Huntley (2006).
• ‘calc_AverageDose()’: Function to calculate the average dose
and their extrinsic dispersion.
• ‘convert_BIN2R()’: wrapper function around the functions
‘read_BIN2R()’ and ‘write_RLum2CSV()’ to convert a BIN-file
to CSV-files; so far possible.
• ‘convert_Daybreak2R()’: wrapper function around the
functions ‘read_Daybreak2R()’ and ‘write_RLum2CSV()’ to
convert Daybreak measurement data (TXT-file, DATE-file) to
CSV-files; so far possible.
• ‘convert_PSL2R()’: wrapper function around the functions
‘read_PSL2R()’ and ‘write_RLum2CSV()’ to convert a PSL-file
(SUERC portable OSL reader file format) to CSV-files; so far
possible.
• ‘convert_XSYG2R()’: wrapper function around the functions
‘read_XSYG2R()’ and ‘write_RLum2CSV()’ to convert XSYG-file
to CSV-files; so far possible.
• ‘github_branches(), github_commits(), github_issues()’: R
Interface to the GitHub API v3. These functions can be used
to query a specific repository hosted on GitHub.
• ‘install_DevelopmentVersion()’: This function is a
convenient method for installing the development version of
the R package 'Luminescence' directly from GitHub.
• ‘PSL2Risoe.BINfileData()’: Converts an ‘RLum.Analysis’
object produced by the function ‘read_PSL2R()’ to an
‘Risoe.BINfileData’ object.
• ‘read_PSL2R()’: Imports PSL files produced by a SUERC
portable OSL reader into R.
• ‘smooth_RLum()’: wrapper function to call the corresponding
methods to smooth data based on the function ‘zoo:rollmean’.
• ‘write_RLum2CSV()’: Exports ‘RLum’-objects to CSV-files to
improve the compatibility to other software. Supported are
only numerical values, i.e., ‘data.frame’, ‘matrix’ and
‘numeric’.
New example data:
• ‘ExampleData.fading’: Example data set for fading
measurements of the IR50, IR100, IR150 and IR225 feldspar
signals of sample UNIL/NB123. It further contains regular
equivalent dose measurement data of the same sample, which
can be used to apply a fading correction to. These data were
kindly provided by Georgina King.
Changes in S4-classes and methods:
• Method ‘get_RLum’ for ‘RLum.Analysis’-objects did not
respect ‘.pid’, fixed.
• Method ‘get_RLum’ for ‘list’-objects now accepts lists with
all kinds of ‘RLum’-objects. Previously, only lists of
‘RLum.Analysis’-objects were allowed.
• ‘plot_RLum’ was not passing the argument ‘sub’, as it was
fetched by the partial argument matching; fixed.
• ‘set_RLum’ produced ‘NA’ as originator, if the function
calling the function ‘set_RLum()’ was called from outside of
the package using the double colons (e.g.,
‘Luminescence::function()’); fixed.
• ‘smooth_RLum’ add method support for ‘RLum.Data.Curve’,
‘RLum.Analysis’ and ‘list’ of this objects implemented.
Bugfixes and changes:
• ‘analyse_baSARL()’
• Due to a typo in the manual the ‘method_control’
parameter ‘variable.names’ was not working if correctly
typed as written in the manual (in the manual:
'variables.names', but correct is 'variable.names');
typo corrected fixed,
• minor improvements and error corrections.
• ‘analyse_IRSAR.RF()’
• Add option for a vertical sliding of the RF_nat
curve (‘method_control = list(vslide_range =
'auto')’). This feature has beta status and usage
for publication work is not recommended yet. By
default no vertical sliding is applied,
• allow a parallel processing of MC runs by using the
argument ‘method_control = list(cores = 'auto')’.
• ‘analyse_SAR.CWOSL()’
• Fix wrongly set threshold value for recuperation
rate (#26),
• fix a rare bug for the combination 'recyling.ratio =
NA' and more than one provided recyling point,
• a check has been implemented to refrain from using
wrong rejection criteria keywords.
• ‘calc_AliquotSize()’
• Console output can now be suppressed via 'verbose =
TRUE' (#24).
• ‘calc_CosmicDoseRate()’
• Console output can now be suppressed via 'verbose =
TRUE' (#24).
• ‘calc_FastRatio()’
• New arguments 'Ch_L2' and 'Ch_L3' to allow the user
to specify custom values for channels L2 and L3.
Feature requested by A. Versendaal (#29).
• ‘calc_FadingCorr()’
• Fixed a bug where the function would crash when
providing an ‘RLum.Results’ object for ‘g_value’,
• new argument ‘interval’ to control the age interval
for solving the equation via ‘uniroot’.
• ‘calc_FiniteMixture()’
• Fixed a bug where certain arguments where not passed
to `plot_RLum.Results` so that the plot was not as
customisable as intended. Thanks to Daniele
Questiaux for reporting the bug.
• ‘calc_MaxDose()’
• Fixed a bug in the console output, which provided
wrong values for the asymmetric error on gamma (Note
that the values in the output object were correct!).
Thankfully reported by Xue Rui.
• ‘calc_Statistics()’
• The argument ‘n.MC’ got a new value ‘NULL’ which is
now used as default. With this the basic statistical
measures are in accordance with the expectations
(e.g., the standard deviation is returned by default
in the conventional way and not calculated using an
MC simulation).
• ‘calc_OSLLxTxRatio()’
• Add argument ‘use_previousBG’ to use the background
of the Lx-curve to get the net signal of the
Tx-curve (request #15).
• ‘fit_CWCurve()’
• Change order of ‘RLum.Results’ output list elements,
• rename first element to ‘data’,
• add element slot 'info'.
• ‘fit_LWCurve()’
• Change order of ‘RLum.Results’ output list elements,
• rename first element to ‘data’,
• add element slot 'info'.
• ‘model_LuminescenceSignals()’
• Update function arguments to account for changes in
RLumModel version 0.2.0.
• ‘plot_DetPlot()’
• Correct negative y-axis, the minimum is now the real
minimum.
• ‘plot_GrowthCurve()’
• Reduce number of confusing warning,
• add new argument ‘mode’ to select the calculation
mode of the function. This allows in particular to
only fit data without calculating a De or
calculating a De assuming an additive dose response
curve,
• account for the very specifc case that all dose
points are similar. The function stops with an error
and returns NULL,
• under weird circumstances points on the growth curve
were not plotted correctly; fixed.
• ‘plot_RadialPlot()’
• Sometimes the function crashed with an out of bonds
error if more than one data set was provided; fixed,
• argument ‘negatives’ caused an error if not set to
‘'remove'’ and fix some errors around this option,
• De-adjustment for negative values optimised for
large scatter.
• ‘plot_RLum.Analysis()’
• The usage of the argument ‘smooth’ led to a crash;
fixed.
• ‘plot_RLum.Data.Curve()’
• Function will not stop anymore if the curve contains
‘NA’ values, but if the curve consists of only ‘NA’
values.
• ‘plot_RLum.Data.Spectrum()’
• The interactive plot option was broken with the last
update of the package 'plotly'; fixed.
• ‘plot_ViolinPlot()’
• The function erroneously produced a NA value
warning; fixed.
• ‘read_BIN2R()’
• If BIN-files are automatically imported the function
skipped non BIN-files without crashing if it is used
in combination with the argument ‘pattern’,
• add new argument ‘irgnore.RECTYPE’ to provide a
solution for broken BIN-files are BIN-files with non
documented entries. Furthermore the general
behaviour for such cases had been optimised.
• ‘read_Daybreak2R()’
• Add support for DAT-files produced by at 1100 reader
using the software (TLAPLLIC v.3.2). Thanks to
Antoine Zink,
• minor error corrections and adding example code.
• ‘template_DRAC()’
• Fixed a typo in the column names (#28).
• ‘use_DRAC()’
• Now supports DRAC v1.2 and the newly introduced CSV
input template. Older v1.1 excel sheet input
templates are still supported, but users are highly
encouraged to use the new CSV file.
• Columns in the output tables are now assigned proper
classes (#27).
Internals:
• The internal function converting BIN-file curves to
RLum.Data.Curve() objects had been optimised and, amongst
others, now uses a function written using Rcpp to create the
curve matrix. The conversion now works ca. two times
faster,
• add ‘`[<-]`’ method for ‘RLum.Data.Curve’ objects,
• a hint on how to cite a function is now added automatically
to every major function manual page,
• add 'magrittr' to the package dependencies (imports) to
further support the usage of this amazing pipe operator,
• thanks to Johannes Friedrich this release introduces regular
unit tests using the package 'testthat' to improve the code
quality and stability,
• add internal helper function ‘.smoothing’; no Rd entry.
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