Package 'gamma' reference manual

Title:	Dose Rate Estimation from in-Situ Gamma-Ray Spectrometry Measurements
Description:	Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.
Authors:	Nicolas Frerebeau [aut] , Brice Lebrun [aut] , Guilhem Paradol [aut] , Magali Rizza [ctb] , Christelle Lahaye [ctb] , Sebastian Kreutzer [aut] , Archéosciences Bordeaux [cre], Université Bordeaux Montaigne [cph, fnd], CNRS [fnd], LabEx Sciences archéologiques de Bordeaux [fnd], Idex Aix-Marseille [fnd]
Maintainer:	Archéosciences Bordeaux <[email protected]>
License:	GPL-3
Version:	1.1.0
Built:	2024-12-23 05:31:58 UTC
Source:	https://github.com/crp2a/gamma

CEREGE Calibration Curve (NaI)

Description

CEREGE Calibration Curve (NaI)

Usage

data(AIX_NaI_1)
data(AIX_NaI_1)

Format

An object of class CalibrationCurve.

Laboratory	CEREGE
Instrument	Canberra Inspector 1000
Detector	NaI
Authors	CEREGE Luminescence Team

Examples

## Load the curve
data(AIX_NaI_1, package = "gamma")
plot(AIX_NaI_1)
## Load the curve
data(AIX_NaI_1, package = "gamma")
plot(AIX_NaI_1)

Baseline Estimation and Removal

Description

Baseline Estimation and Removal

Usage

signal_baseline(object, ...)

signal_correct(object, ...)

baseline_snip(object, ...)

baseline_rubberband(object, ...)

baseline_linear(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectra'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectrum'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectra'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectrum'
baseline_linear(object, from = NULL, to = NULL)

## S4 method for signature 'GammaSpectra'
baseline_linear(object, from = NULL, to = NULL)

## S4 method for signature 'GammaSpectrum'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

## S4 method for signature 'GammaSpectra'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

## S4 method for signature 'GammaSpectrum'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)

## S4 method for signature 'GammaSpectra'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)
signal_baseline(object, ...)

signal_correct(object, ...)

baseline_snip(object, ...)

baseline_rubberband(object, ...)

baseline_linear(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectra'
signal_baseline(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectrum'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectra'
signal_correct(object, method = c("SNIP", "rubberband", "linear"), ...)

## S4 method for signature 'GammaSpectrum'
baseline_linear(object, from = NULL, to = NULL)

## S4 method for signature 'GammaSpectra'
baseline_linear(object, from = NULL, to = NULL)

## S4 method for signature 'GammaSpectrum'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

## S4 method for signature 'GammaSpectra'
baseline_rubberband(object, noise = 0, spline = TRUE, ...)

## S4 method for signature 'GammaSpectrum'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)

## S4 method for signature 'GammaSpectra'
baseline_snip(object, LLS = FALSE, decreasing = FALSE, n = 100, ...)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`...`	Extra parameters to be passed to further methods.
`method`	A `character` string specifying the method to be used for baseline estimation (see details). Any unambiguous substring can be given.
`from`	An `integer` giving the first channel to be used for linear interpolation. If `NULL` (the default), channel $1$ is used. Only used if `method` is "`linear`".
`to`	An `integer` giving the last channel to be used for linear interpolation. If `NULL` (the default), channel $max$ is used. Only used if `method` is "`linear`".
`noise`	A length-one `numeric` vector giving the noise level. Only used if `method` is "`rubberband`".
`spline`	A `logical` scalar: should spline interpolation through the support points be used instead of linear interpolation? Only used if `method` is "`rubberband`".
`LLS`	A `logical` scalar: should the LLS operator be applied on `x` before employing SNIP algorithm? Only used if `method` is "`SNIP`".
`decreasing`	A `logical` scalar: should a decreasing clipping window be used? Only used if `method` is "`SNIP`".
`n`	An `integer` value giving the number of iterations. Only used if `method` is "`SNIP`".

Details

The following methods are available for baseline estimation:

SNIP: Sensitive Nonlinear Iterative Peak clipping algorithm.
rubberband: A convex envelope of the spectrum is determined and the baseline is estimated as the part of the convex envelope lying below the spectrum. Note that the rubber band does not enter the concave regions (if any) of the spectrum.
linear: Linear baseline estimation.

Value

⁠baseline_*()⁠ returns a BaseLine object.
signal_correct() returns a corrected GammaSpectrum or GammaSpectra object (same as object).

Note

baseline_rubberband() is slightly modified from C. Beleites' hyperSpec::spc.rubberband().

Author(s)

N. Frerebeau

References

Liland, K. H. (2015). 4S Peak Filling - baseline estimation by iterative mean suppression. MethodsX, 2, 135-140. doi:10.1016/j.mex.2015.02.009.

Morháč, M., Kliman, J., Matoušek, V., Veselský, M. & Turzo, I. (1997). Background elimination methods for multidimensional gamma-ray spectra. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 401(1), p. 113-132. doi:10.1016/S0168-9002(97)01023-1

Morháč, M. & Matoušek, V. (2008). Peak Clipping Algorithms for Background Estimation in Spectroscopic Data. Applied Spectroscopy, 62(1), p. 91-106. doi:10.1366/000370208783412762

Ryan, C. G., Clayton, E., Griffin, W. L., Sie, S. H. & Cousens, D. R. (1988). SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 34(3), p. 396-402. doi:10.1016/0168-583X(88)90063-8

Examples

## Import a CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Remove the first 35 channels
spc <- signal_slice(spc, -c(1:35))

## Linear baseline
bsl_linear <- baseline_linear(spc, from = 250, to = 750)
plot(spc, bsl_linear)

## SNIP baseline
bsl_snip <- baseline_snip(spc, LLS = FALSE, decreasing = FALSE, n = 100)
plot(spc, bsl_snip)

## Rubberband baseline
bsl_rubber <- baseline_rubberband(spc)
plot(spc, bsl_rubber)

## Remove baseline
spc_clean1 <- signal_correct(spc)
spc_clean2 <- spc - bsl_snip
all(spc_clean1 == spc_clean2)

plot(spc_clean1)
## Import a CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Remove the first 35 channels
spc <- signal_slice(spc, -c(1:35))

## Linear baseline
bsl_linear <- baseline_linear(spc, from = 250, to = 750)
plot(spc, bsl_linear)

## SNIP baseline
bsl_snip <- baseline_snip(spc, LLS = FALSE, decreasing = FALSE, n = 100)
plot(spc, bsl_snip)

## Rubberband baseline
bsl_rubber <- baseline_rubberband(spc)
plot(spc, bsl_rubber)

## Remove baseline
spc_clean1 <- signal_correct(spc)
spc_clean2 <- spc - bsl_snip
all(spc_clean1 == spc_clean2)

plot(spc_clean1)

An S4 Class to Represent a Spectrum Baseline

Description

An S4 Class to Represent a Spectrum Baseline

Note

This class extends the GammaSpectrum class.

Author(s)

N. Frerebeau

Examples

## Import a CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Remove the first 35 channels
spc <- signal_slice(spc, -c(1:35))

## Linear baseline
bsl_linear <- baseline_linear(spc, from = 250, to = 750)
plot(spc, bsl_linear)

## SNIP baseline
bsl_snip <- baseline_snip(spc, LLS = FALSE, decreasing = FALSE, n = 100)
plot(spc, bsl_snip)

## Rubberband baseline
bsl_rubber <- baseline_rubberband(spc)
plot(spc, bsl_rubber)

## Remove baseline
spc_clean1 <- signal_correct(spc)
spc_clean2 <- spc - bsl_snip
all(spc_clean1 == spc_clean2)

plot(spc_clean1)
## Import a CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Remove the first 35 channels
spc <- signal_slice(spc, -c(1:35))

## Linear baseline
bsl_linear <- baseline_linear(spc, from = 250, to = 750)
plot(spc, bsl_linear)

## SNIP baseline
bsl_snip <- baseline_snip(spc, LLS = FALSE, decreasing = FALSE, n = 100)
plot(spc, bsl_snip)

## Rubberband baseline
bsl_rubber <- baseline_rubberband(spc)
plot(spc, bsl_rubber)

## Remove baseline
spc_clean1 <- signal_correct(spc)
spc_clean2 <- spc - bsl_snip
all(spc_clean1 == spc_clean2)

plot(spc_clean1)

CRP2A Calibration Curve (LaBr)

Description

CRP2A Calibration Curve (LaBr)

Usage

data(BDX_LaBr_1)
data(BDX_LaBr_1)

Format

An object of class CalibrationCurve.

Laboratory	IRAMAT-CRP2A (UMR 5060)
Instrument	Canberra Inspector 1000
Detector	LaBr
Authors	CRP2A Luminescence Team

Examples

## Load the curve
data(BDX_LaBr_1, package = "gamma")
plot(BDX_LaBr_1)
## Load the curve
data(BDX_LaBr_1, package = "gamma")
plot(BDX_LaBr_1)

An S4 class to Represent a Dose Rate Calibration Curve

Description

An S4 class to Represent a Dose Rate Calibration Curve

Slots

Ni: A DoseRateModel object.
NiEi: A DoseRateModel object.
details: A list of length-one vector giving the curve metadata.
slope: A numeric vector.
intercept: A numeric vector.
covariance: A numeric vector.
MSWD: A numeric vector.
df: A numeric vector.
p_value: A numeric vector.
data: A data.frame.
range: A numeric vector.
background: A numeric vector.

Subset

In the code snippets below, x is a CalibrationCurve object.

x[[i]]: Extracts information from a slot selected by subscript i. i is a character vector of length one.

Author(s)

N. Frerebeau

Clermont Reference Data

Description

Clermont Reference Data

Usage

data("clermont")
data("clermont")

Format

A data.frame with tabulated data and conversion factor reference.

Source

Guérin, G., Mercier, N. & Adamiec, G. (2011). Dose-Rate Conversion Factors: Update. Ancient TL, 29(1), p. 5-8.

Miallier, D., Guérin, G., Mercier, N., Pilleyre, T. & Sanzelle, S. (2009). The Clermont Radiometric Reference Rocks: A Convenient Tool for Dosimetric Purposes. Ancient TL, 27(2), p. 37-44.

Clermont Reference Data 2024

Description

An updated version for the clermont dataset with additional columns for the applied dose rate conversion factors and their reference. The dataset also contains gamma dose rate values and uncertainties for different published conversion factors. Please note that the values for the conversion factors applied in the original clermont dataset differ slightly due to rounding errors.

Usage

data("clermont_2024")
data("clermont_2024")

Format

A data.frame with tabulated data and conversion factor reference.

Source

Adamiec, G. & Aitken, M.J. (1998). Dose-rate conversion factors: update. Ancient TL, 16, p. 37–50.

Cresswell, A.J., Carter, J. & Sanderson, D.C.W. (2018). Dose rate conversion parameters: Assessment of nuclear data. Radiation Measurements, 120, p. 195–201.

Guérin, G., Mercier, N. & Adamiec, G. (2011). Dose-Rate Conversion Factors: Update. Ancient TL, 29(1), p. 5-8.

Liritzis, I., Stamoulis, K., Papachristodoulou, C. & Ioannides, K. (2013). A re-evaluation of radiation dose-rate conversion factors. Mediterranean Archaeology and Archaeometry, 12, p. 1–15.

Miallier, D., Guérin, G., Mercier, N., Pilleyre, T. & Sanzelle, S. (2009). The Clermont Radiometric Reference Rocks: A Convenient Tool for Dosimetric Purposes. Ancient TL, 27(2), p. 37-44.

Coerce

Description

Coerce

Usage

## S3 method for class 'GammaSpectrum'
as.matrix(x, ...)
## S3 method for class 'GammaSpectrum'
as.matrix(x, ...)

Arguments

`x`	An object to be coerced.
`...`	Currently not used.

Value

A coerced object.

Author(s)

N. Frerebeau

Examples

## Import a Canberra CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Coerce
mtx <- as.matrix(spc)
df <- as.data.frame(spc)
head(df)
## Import a Canberra CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Coerce
mtx <- as.matrix(spc)
df <- as.data.frame(spc)
head(df)

Dose Rate Estimation

Description

dose_fit() builds a calibration curve for gamma dose rate estimation.
dose_predict() predicts in situ gamma dose rate.

Usage

dose_fit(object, background, doses, ...)

dose_predict(object, spectrum, ...)

## S4 method for signature 'GammaSpectra,GammaSpectrumOrNumeric,matrix'
dose_fit(
  object,
  background,
  doses,
  range_Ni,
  range_NiEi,
  details = list(authors = "", date = Sys.time())
)

## S4 method for signature 'GammaSpectra,GammaSpectrumOrNumeric,data.frame'
dose_fit(
  object,
  background,
  doses,
  range_Ni,
  range_NiEi,
  details = list(authors = "", date = Sys.time())
)

## S4 method for signature 'CalibrationCurve,missing'
dose_predict(
  object,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)

## S4 method for signature 'CalibrationCurve,GammaSpectrum'
dose_predict(
  object,
  spectrum,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)

## S4 method for signature 'CalibrationCurve,GammaSpectra'
dose_predict(
  object,
  spectrum,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)
dose_fit(object, background, doses, ...)

dose_predict(object, spectrum, ...)

## S4 method for signature 'GammaSpectra,GammaSpectrumOrNumeric,matrix'
dose_fit(
  object,
  background,
  doses,
  range_Ni,
  range_NiEi,
  details = list(authors = "", date = Sys.time())
)

## S4 method for signature 'GammaSpectra,GammaSpectrumOrNumeric,data.frame'
dose_fit(
  object,
  background,
  doses,
  range_Ni,
  range_NiEi,
  details = list(authors = "", date = Sys.time())
)

## S4 method for signature 'CalibrationCurve,missing'
dose_predict(
  object,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)

## S4 method for signature 'CalibrationCurve,GammaSpectrum'
dose_predict(
  object,
  spectrum,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)

## S4 method for signature 'CalibrationCurve,GammaSpectra'
dose_predict(
  object,
  spectrum,
  sigma = 1,
  epsilon = 0.015,
  water_content = NULL,
  use_MC = FALSE
)

Arguments

`object`	A GammaSpectra or CalibrationCurve object.
`background`	A GammaSpectrum object or a length-two `numeric` vector giving the background noise integration value and error, respectively. If no background subtraction is wanted, you can set `background = c(0,0,)`
`doses`	A `matrix` or `data.frame` object with gamma dose values and uncertainties. The row names must match the names of the spectrum.
`...`	Currently not used.
`spectrum`	An optional GammaSpectrum or GammaSpectra object in which to look for variables with which to predict. If omitted, the fitted values are used.
`range_Ni`, `range_NiEi`	A length-two `numeric` vector giving the energy range to integrate within (in keV).
`details`	A `list` of length-one vector specifying additional informations about the instrument for which the curve is built.
`sigma`	A `numeric` value giving the confidence level of which the error from the slope is considered in the final uncertainty calculation
`epsilon`	A `numeric` value giving an extra relative error term, introduced by the calibration of the energy scale of the spectrum, e.g., `0.015` for an additional 1.5% error
`water_content`	`numeric` or `matrix` gravimetric field water content to correct the gamma-dose rate to using the correction factor by Aitken (1985) to obtain the dry gamma-dose rate. Example: `c(0.05,0.0001)` for water content of 5% +/- 0.01 %. The default is `NULL` (nothing is corrected). The correction only works on the final dose rate. For more information see details.
`use_MC`	A `logical` parameter, enabling/disabling Monte Carlo simulations for estimating the dose rate uncertainty

Details

To estimate the gamma dose rate, one of the calibration curves distributed with this package can be used. These built-in curves are in use in several luminescence dating laboratories and can be used to replicate published results. As these curves are instrument specific, the user may have to build its own curve.

The construction of a calibration curve requires a set of reference spectra for which the gamma dose rate is known and a background noise measurement. First, each reference spectrum is integrated over a given interval, then normalized to active time and corrected for background noise. The dose rate is finally modelled by the integrated signal value used as a linear predictor (York et al., 2004).

Value

dose_fit() returns a CalibrationCurve object.
dose_predict() returns a data.frame with the following columns:

name

(character) the name of the spectra.

signal_Ni

(numeric) the integrated signal value (according to the value of threshold; see signal_integrate()) for energy = FALSE

signal_err_Ni

(numeric) the integrated signal error value (according to the value of threshold; see signal_integrate()) for energy = FALSE.

dose_Ni

(numeric) the predicted gamma dose rate for energy = FALSE.

dose_err_Ni

(numeric) the predicted gamma dose rate error for energy = FALSE.

signal_Ni

(numeric) the integrated signal value (according to the value of threshold; see signal_integrate()).

signal_err_NiEi

(numeric) the integrated signal error value (according to the value of threshold; see signal_integrate()) for energy = TRUE.

dose_NiEi

(numeric) the predicted gamma dose rate for energy = TRUE.

dose_err_NiEi

(numeric) the predicted gamma dose rate error for energy = TRUE.

dose_final

(numeric) the predicted final gamma dose rate as the mean of dose_Ni and dose_NiEi

dose_err_final

(numeric) the predicted final gamma dose rate error as $SE(\dot{D}_{\gamma}) = \sqrt{(\frac{SE(\dot{D}_{\gamma\mathrm{Ni}})}{\dot{D}_{\gamma\mathrm{Ni}}})^2 + (\frac{SE(\dot{D}_{\gamma\mathrm{NiEi}})}{\dot{D}_{\gamma\mathrm{NiEi}}})^2}$

Uncertainty calculation of the gamma-dose rate

The analytical uncertainties of the final gamma-dose rate ( $SE(\dot{D}_{\gamma})$ ) are calculated as follows:

$\sigma_{\dot{D_\gamma}} = \sqrt((\frac{m_{\delta}s}{m})^2 + (\frac{s_{\delta}}{s})^2 + \epsilon^2)$

with $m$ and $m_{\delta}$ being the slope of the fit an its uncertainty, $\sigma$ the error scaler for the slope uncertainty, $s$ and $s_{\delta}$ the integrated signal and its uncertainty, and $\epsilon$ an additional relative uncertainty term that can be set by the user using the argument epsilon.

If the parameter use_MC is set to TRUE, the a Monte Carlo sampling approach is chosen to approximate the uncertainties on the dose rate:

$\sigma_{\dot{D_\gamma}} := \sqrt((\frac{SD(\mathcal{N}(\mu_{slope}, \sigma_{slope}) \times \mathcal{N}(\mu_{signal}, \sigma_{signal}) + \mathcal{N}(\mu_{intercept}, \sigma_{intercept})) * \rho}{\dot{D_\gamma}})^2 + \epsilon^2) * \dot{D_\gamma}$

$\ rho$ is the parameter sigma provided with the function call, $SD$ equals the the call to sd(), i.e. the calculation of the standard deviation. To achieve a good Gaussian normal approximation with sample 1+e06 times (the values is fixed).

Water content correction

If gamma-dose rates are measured in the field, they are measured at "as-is" conditions. In dating studies, however, using the dry dose rate is often more desirable to model the long-term effect of different assumptions for the water content. If the parameter water_content, either as numeric vector or as matrix with the number of rows equal to the number of processed spectra, if different values are desired, the final gamma-dose rate is corrected for the water content provided. Final uncertainties are obtained using the square root of the summed squared relative uncertainties of the dose rate and the water content.

A word of caution: When estimating the water content in the laboratory, the water analytical uncertainty is usually minimal, and it does not make sense to correct with a relative water content of, e.g., c(0.02,0.02) (2% +/- 2%) because this massively inflates the final dose rate error.

Note

See vignette(doserate) for a reproducible example.

Author(s)

N. Frerebeau

References

Aitken, M.J. (1985). Thermoluminescence dating. London: Academic Press.

Mercier, N. & Falguères, C. (2007). Field Gamma Dose-Rate Measurement with a NaI(Tl) Detector: Re-Evaluation of the "Threshold" Technique. Ancient TL, 25(1), p. 1-4.

York, D., Evensen, N. M., Martínez, M. L. & De Basabe Delgado, J. (2004). Unified Equations for the Slope, Intercept, and Standard Errors of the Best Straight Line. American Journal of Physics, 72(3), p. 367-75. doi:10.1119/1.1632486.

Examples

## Import CNF files
## Spectra
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)

## Background
bkg_dir <- system.file("extdata/BDX_LaBr_1/background", package = "gamma")
bkg <- read(bkg_dir)

## Get dose rate values
data("clermont")
(doses <- clermont[, c("gamma_dose", "gamma_error")])

## Build the calibration curve
calib_curve <- dose_fit(spc, bkg, doses,
                        range_Ni = c(300, 2800),
                        range_NiEi = c(165, 2800))

## Plot the curve
plot(calib_curve, threshold = "Ni")

## Estimate gamma dose rates
dose_predict(calib_curve, spc)
## Import CNF files
## Spectra
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)

## Background
bkg_dir <- system.file("extdata/BDX_LaBr_1/background", package = "gamma")
bkg <- read(bkg_dir)

## Get dose rate values
data("clermont")
(doses <- clermont[, c("gamma_dose", "gamma_error")])

## Build the calibration curve
calib_curve <- dose_fit(spc, bkg, doses,
                        range_Ni = c(300, 2800),
                        range_NiEi = c(165, 2800))

## Plot the curve
plot(calib_curve, threshold = "Ni")

## Estimate gamma dose rates
dose_predict(calib_curve, spc)

Energy Scale Calibration

Description

Calibrates the energy scale of a gamma spectrum.

Usage

energy_calibrate(object, lines, ...)

has_energy(object)

has_calibration(object)

## S4 method for signature 'GammaSpectrum,lm'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,CalibrationCurve'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,list'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,PeakPosition'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,list'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,PeakPosition'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,lm'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,GammaSpectrum'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,CalibrationCurve'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum'
has_energy(object)

## S4 method for signature 'GammaSpectra'
has_energy(object)

## S4 method for signature 'GammaSpectrum'
has_calibration(object)

## S4 method for signature 'GammaSpectra'
has_calibration(object)

## S4 method for signature 'CalibrationCurve'
has_calibration(object)
energy_calibrate(object, lines, ...)

has_energy(object)

has_calibration(object)

## S4 method for signature 'GammaSpectrum,lm'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,CalibrationCurve'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,list'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum,PeakPosition'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,list'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,PeakPosition'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,lm'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,GammaSpectrum'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectra,CalibrationCurve'
energy_calibrate(object, lines, ...)

## S4 method for signature 'GammaSpectrum'
has_energy(object)

## S4 method for signature 'GammaSpectra'
has_energy(object)

## S4 method for signature 'GammaSpectrum'
has_calibration(object)

## S4 method for signature 'GammaSpectra'
has_calibration(object)

## S4 method for signature 'CalibrationCurve'
has_calibration(object)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`lines`	A PeakPosition object or a `list` of length two. If a `list` is provided, each element must be a named numeric vector giving the observed peak position ("`channel`") and the corresponding expected "`energy`" value (in keV). Alternatively, the function accepts the stats::lm object from a another calibration or a GammaSpectrum object from which such calibration is copied. With this, energy calibrations can be transferred from one object to another.
`...`	Currently not used.

Details

The energy calibration of a spectrum is the most tricky part. To do this, the user must specify the position of at least three observed peaks and the corresponding energy value (in keV). A second order polynomial model is fitted on these energy vs channel values, then used to predict the new energy scale of the spectrum.

The package allows to provide the channel-energy pairs to be use. However, the spectrum can be noisy so it is difficult to properly determine the peak channel. In this case, a better approach may be to pre-process the spectrum (variance-stabilization, smoothing and baseline correction) and perform a peak detection. Once the identified peaks are satisfactory, you can set the corresponding energy values (in keV) and use these lines to calibrate the energy scale of the spectrum.

Regardless of the approach you choose, it is strongly recommended to check the result before proceeding.

Value

energy_calibrate() returns either a GammaSpectrum or a GammaSpectra object depending on the input#
has_energy() and has_calibration() return a logical vector.

Author(s)

N. Frerebeau

Examples

## Import a CNF file
spc_file <- system.file("extdata/LaBr.TKA", package = "gamma")
(spc <- read(spc_file))

## Set peak positions (channel) and expected energy values
calib_lines <- list(
  channel = c(86, 495, 879),
  energy = c(238, 1461, 2615)
)

## Adjust the energy scale
(spc1 <- energy_calibrate(spc, lines = calib_lines))

## Inspect results
plot(spc1, xaxis = "energy", yaxis = "count") +
  ggplot2::geom_vline(xintercept = c(238, 1461, 2615), linetype = 3)
## Import a CNF file
spc_file <- system.file("extdata/LaBr.TKA", package = "gamma")
(spc <- read(spc_file))

## Set peak positions (channel) and expected energy values
calib_lines <- list(
  channel = c(86, 495, 879),
  energy = c(238, 1461, 2615)
)

## Adjust the energy scale
(spc1 <- energy_calibrate(spc, lines = calib_lines))

## Inspect results
plot(spc1, xaxis = "energy", yaxis = "count") +
  ggplot2::geom_vline(xintercept = c(238, 1461, 2615), linetype = 3)

An S4 Class to Represent a Collection of Gamma Sectra

Description

Represents a collection of spectra of gamma ray spectrometry measurements.

Details

This class extends the base list and can only contains GammaSpectrum objects.

Access

In the code snippets below, x is a GammaSpectra object.

length(x): Get the number of elements in x.
lengths(x): Get the number of channels in each element of x.
get_names(x), set_names(x) <- value: Retrieves or sets the names of x according to value.
get_hash(x): Get the MD5 hash of the raw data files.
get_channels(x): Get the number of channels in each element of x.
get_counts(x): Get the counts of each element of x.
get_energy(x): Get the energy range of each element of x.
get_rates(x): Get the count rates of each element of x.

Subset

In the code snippets below, x is a GammaSpectra object.

x[i]: Extracts the elements selected by subscript i. i can be missing or NULL, numeric or character vector or a factor. Returns a new GammaSpectra object.
x[i, j]: Like the above but allows to select a slot thru j (see examples). j is a character vector of length one. Returns a list.
x[[i]]: Extracts the elements selected by subscript i. i can be a numeric or character vector of length one. Returns the corresponding GammaSpectrum object.

Author(s)

N. Frerebeau

Examples

## Import all CNF files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
(spc <- read(spc_dir))

## Access
get_hash(spc)
get_names(spc)
get_livetime(spc)
get_realtime(spc)

lengths(spc)
range_energy(spc)

## Subset
spc[] # All spectra
spc[NULL] # All spectra
spc[1] # The first spectrum
spc[-6] # Delete the sixth spectrum
spc[1:3] # The first three spectra
spc[c(1, 3)] # The first and third spectra
spc["BRIQUE"] # The spectrum named 'BRIQUE'
spc[c("BRIQUE", "C347")] # The spectra named 'BRIQUE' and 'C347'
spc[1:3, "energy"] # The slot 'energy' of the first three spectra
spc[[1]]
spc[["BRIQUE"]]
## Import all CNF files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
(spc <- read(spc_dir))

## Access
get_hash(spc)
get_names(spc)
get_livetime(spc)
get_realtime(spc)

lengths(spc)
range_energy(spc)

## Subset
spc[] # All spectra
spc[NULL] # All spectra
spc[1] # The first spectrum
spc[-6] # Delete the sixth spectrum
spc[1:3] # The first three spectra
spc[c(1, 3)] # The first and third spectra
spc["BRIQUE"] # The spectrum named 'BRIQUE'
spc[c("BRIQUE", "C347")] # The spectra named 'BRIQUE' and 'C347'
spc[1:3, "energy"] # The slot 'energy' of the first three spectra
spc[[1]]
spc[["BRIQUE"]]

An S4 Class to Represent a Gamma Spectrum

Description

Represents a single spectrum of a gamma ray spectrometry measurement.

Slots

hash: A character string giving the 32-byte MD5 hash of the imported file.
name: A character string the measurement reference.
date: A POSIXct element giving the measurement date and time.
instrument: A character string giving the instrument name.
file_format: A character string giving the format of the imported file.
live_time: A numeric value.
real_time: A numeric value.
channel: A integer vector giving the channel number. Numeric values are coerced to integer as by as.integer() (and hence truncated towards zero).
energy: A numeric vector giving the gamma ray's energy (in keV).
count: A numeric vector giving the counts number for each channel. Numeric values are coerced to integer as by as.integer() (and hence truncated towards zero).
rate: A numeric vector the count rate ( $1/s$ ) for each channel.
calibration: A linear model used for energy scale calibration (see energy_calibrate()).

Access

In the code snippets below, x is a GammaSpectrum object.

length(x): Get number of channel in x.
get_hash(x): Get the MD5 hash of the raw data file.
get_names(x), set_names(x) <- value: Retrieves or sets the name of x according to value.
get_channels(x): Get the number of channels in x.
get_counts(x): Get the counts of x.
get_energy(x): Get the energy range of x.
get_rates(x): Get the count rates of x.

Coerce

In the code snippets below, x is a GammaSpectrum object.

as.matrix(x): Coerces x to a matrix.
as.data.frame(x): Coerces x to a data.frame.

Subset

In the code snippets below, x is a GammaSpectrum object.

x[[i]]: Extracts information from a slot selected by subscript i. i is a character vector of length one and will be matched to the name of the slots.

Note

This class retains copy construction.

Author(s)

N. Frerebeau

Examples

## Import a Canberra CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
(spc <- read(spc_file))

## Access
get_hash(spc)
get_names(spc)
get_livetime(spc)
get_realtime(spc)

length(spc)
range_energy(spc)

## Subset
spc[["date"]]
spc[["instrument"]]
spc[["file_format"]]
## Import a Canberra CNF file
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
(spc <- read(spc_file))

## Access
get_hash(spc)
get_names(spc)
get_livetime(spc)
get_realtime(spc)

length(spc)
range_energy(spc)

## Subset
spc[["date"]]
spc[["instrument"]]
spc[["file_format"]]

Get or Set Parts of an Object

Description

Getters and setters to extract or replace parts of an object.

Usage

get_hash(x)

get_names(x)

set_names(x) <- value

get_energy_calibration(x)

set_energy_calibration(x) <- value

get_livetime(x)

get_realtime(x)

get_channels(x)

get_counts(x)

get_rates(x)

get_energy(x, ...)

set_energy(x) <- value

get_method(x)

set_method(x) <- value

get_residuals(x)

range_channels(x, ...)

range_energy(x, ...)

## S4 method for signature 'GammaSpectrum'
length(x)

## S4 method for signature 'GammaSpectrum'
get_hash(x)

## S4 method for signature 'GammaSpectrum'
get_names(x)

## S4 method for signature 'GammaSpectrum'
get_livetime(x)

## S4 method for signature 'GammaSpectrum'
get_realtime(x)

## S4 method for signature 'GammaSpectrum'
get_channels(x)

## S4 method for signature 'GammaSpectrum'
get_counts(x)

## S4 method for signature 'GammaSpectrum'
get_rates(x)

## S4 method for signature 'GammaSpectrum'
get_energy(x)

## S4 method for signature 'GammaSpectrum'
range_energy(x, na.rm = FALSE)

## S4 method for signature 'GammaSpectrum'
range_channels(x, na.rm = FALSE)

## S4 method for signature 'Baseline'
get_method(x)

## S4 method for signature 'GammaSpectra'
get_hash(x)

## S4 method for signature 'GammaSpectra'
get_names(x)

## S4 method for signature 'GammaSpectra'
get_livetime(x)

## S4 method for signature 'GammaSpectra'
get_realtime(x)

## S4 method for signature 'GammaSpectra'
get_channels(x)

## S4 method for signature 'GammaSpectra'
get_counts(x)

## S4 method for signature 'GammaSpectra'
get_rates(x)

## S4 method for signature 'GammaSpectra'
get_energy(x)

## S4 method for signature 'GammaSpectra'
range_energy(x, na.rm = FALSE)

## S4 method for signature 'GammaSpectra'
range_channels(x, na.rm = FALSE)

## S4 method for signature 'DoseRateModel'
get_residuals(x)

## S4 method for signature 'PeakPosition'
get_hash(x)

## S4 method for signature 'PeakPosition'
get_channels(x)

## S4 method for signature 'PeakPosition'
get_energy(x, expected = FALSE)

## S4 replacement method for signature 'GammaSpectrum'
set_names(x) <- value

## S4 replacement method for signature 'GammaSpectrum'
set_energy_calibration(x) <- value

## S4 method for signature 'GammaSpectrum'
get_energy_calibration(x)

## S4 replacement method for signature 'Baseline'
set_method(x) <- value

## S4 replacement method for signature 'GammaSpectra'
set_names(x) <- value

## S4 replacement method for signature 'GammaSpectra'
set_energy_calibration(x) <- value

## S4 method for signature 'GammaSpectra'
get_energy_calibration(x)

## S4 replacement method for signature 'PeakPosition,numeric'
set_energy(x) <- value
get_hash(x)

get_names(x)

set_names(x) <- value

get_energy_calibration(x)

set_energy_calibration(x) <- value

get_livetime(x)

get_realtime(x)

get_channels(x)

get_counts(x)

get_rates(x)

get_energy(x, ...)

set_energy(x) <- value

get_method(x)

set_method(x) <- value

get_residuals(x)

range_channels(x, ...)

range_energy(x, ...)

## S4 method for signature 'GammaSpectrum'
length(x)

## S4 method for signature 'GammaSpectrum'
get_hash(x)

## S4 method for signature 'GammaSpectrum'
get_names(x)

## S4 method for signature 'GammaSpectrum'
get_livetime(x)

## S4 method for signature 'GammaSpectrum'
get_realtime(x)

## S4 method for signature 'GammaSpectrum'
get_channels(x)

## S4 method for signature 'GammaSpectrum'
get_counts(x)

## S4 method for signature 'GammaSpectrum'
get_rates(x)

## S4 method for signature 'GammaSpectrum'
get_energy(x)

## S4 method for signature 'GammaSpectrum'
range_energy(x, na.rm = FALSE)

## S4 method for signature 'GammaSpectrum'
range_channels(x, na.rm = FALSE)

## S4 method for signature 'Baseline'
get_method(x)

## S4 method for signature 'GammaSpectra'
get_hash(x)

## S4 method for signature 'GammaSpectra'
get_names(x)

## S4 method for signature 'GammaSpectra'
get_livetime(x)

## S4 method for signature 'GammaSpectra'
get_realtime(x)

## S4 method for signature 'GammaSpectra'
get_channels(x)

## S4 method for signature 'GammaSpectra'
get_counts(x)

## S4 method for signature 'GammaSpectra'
get_rates(x)

## S4 method for signature 'GammaSpectra'
get_energy(x)

## S4 method for signature 'GammaSpectra'
range_energy(x, na.rm = FALSE)

## S4 method for signature 'GammaSpectra'
range_channels(x, na.rm = FALSE)

## S4 method for signature 'DoseRateModel'
get_residuals(x)

## S4 method for signature 'PeakPosition'
get_hash(x)

## S4 method for signature 'PeakPosition'
get_channels(x)

## S4 method for signature 'PeakPosition'
get_energy(x, expected = FALSE)

## S4 replacement method for signature 'GammaSpectrum'
set_names(x) <- value

## S4 replacement method for signature 'GammaSpectrum'
set_energy_calibration(x) <- value

## S4 method for signature 'GammaSpectrum'
get_energy_calibration(x)

## S4 replacement method for signature 'Baseline'
set_method(x) <- value

## S4 replacement method for signature 'GammaSpectra'
set_names(x) <- value

## S4 replacement method for signature 'GammaSpectra'
set_energy_calibration(x) <- value

## S4 method for signature 'GammaSpectra'
get_energy_calibration(x)

## S4 replacement method for signature 'PeakPosition,numeric'
set_energy(x) <- value

Arguments

`x`	An object from which to get or set element(s).
`value`	A possible value for the element(s) of `x`.
`...`	Currently not used.
`na.rm`	A `logical` scalar: should `NA` be omitted?
`expected`	A `logical` scalar: should the expected values be returned instead of observed values?

Value

An object of the same sort as x with the new values assigned.

Author(s)

N. Frerebeau

Common Operations on GammaSpectrum Objects

Description

Performs common operations on GammaSpectrum objects.

Usage

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Arith(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Arith(e1, e2)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Compare(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Compare(e1, e2)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum,logical'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum'
Math(x)

## S4 method for signature 'GammaSpectrum'
Math2(x, digits)

## S4 method for signature 'GammaSpectrum'
Summary(x, ..., na.rm = FALSE)
## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Arith(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Arith(e1, e2)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Compare(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Compare(e1, e2)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum,numeric'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum,logical'
Logic(e1, e2)

## S4 method for signature 'GammaSpectrum'
Math(x)

## S4 method for signature 'GammaSpectrum'
Math2(x, digits)

## S4 method for signature 'GammaSpectrum'
Summary(x, ..., na.rm = FALSE)

Arguments

`x`, `e1`, `e2`	An object (typically a GammaSpectrum object).
`digits`	A length-one `numeric` vector giving the number of digits to be used in `round()` or `signif()`.
`...`	Further arguments passed to or from methods.
`na.rm`	A `logical` scalar: should missing values (including `NaN`) be omitted from the calculations?

Group Generics

GammaSpectrum objects have support for S4 group generic functionality to operate within elements across objects:

Arith: "+", "-", "*", "^", "⁠\%\%⁠", "⁠\%/\%⁠", "/"
Compare: "==", ">", "<", "!=", "<=", ">="
Logic: "&", "|"
Math: "abs", "sign", "sqrt", "ceiling", "floor", "trunc", "cummax", "cummin", "cumprod", "cumsum", "log", "log10", "log2", "log1p", "acos", "acosh", "asin", "asinh", "atan", "atanh", "exp", "expm1", "cos", "cosh", "cospi", "sin", "sinh", "sinpi", "tan", "tanh", "tanpi", "gamma", "lgamma", "digamma", "trigamma"
Math2: "round", "signif"
Ops: "Arith", "Compare", "Logic"
Summary: "min", "max", "range", "prod", "sum", "any", "all"

Author(s)

N. Frerebeau

Examples

## No examples
## No examples

An S4 Class to Represent a Set of Peaks

Description

An S4 Class to Represent a Set of Peaks

Slots

hash: A character string giving the 32-byte MD5 hash of the imported spectrum file.
noise_method: A character string specifying the method used for peak detection.
noise_threshold: A length one numeric vector giving the noise threshold.
window: A length one numeric vector giving the half-window size.
channel: A integer vector giving the channel number. Numeric values are coerced to integer as by as.integer() (and hence truncated towards zero).
energy_observed: A numeric vector giving the observed gamma ray energy (in keV).
energy_expected: A numeric vector giving the expected gamma ray energy (in keV).

Access

In the code snippets below, x is a PeakPosition object.

get_hash(x): Get the MD5 hash of the raw data file.
get_channels(x): Get the channels of x.
get_energy(x), set_energy(x) <- value: Retrieves or sets the energy scale of x according to value.

Coerce

In the code snippets below, x is a PeakPosition object.

as.matrix(x): Coerces x to a matrix.
as.data.frame(x): Coerces x to a data.frame.

Subset

In the code snippets below, x is a PeakPosition object.

x[[i]]: Extracts information from a slot selected by subscript i. i is a character vector of length one and will be matched to the name of the slots.

Note

This class retains copy construction.

Author(s)

N. Frerebeau

Find Peaks

Description

Finds local maxima in sequential data.

Usage

peaks_find(object, ...)

## S4 method for signature 'GammaSpectrum'
peaks_find(object, method = c("MAD"), SNR = 2, span = NULL, ...)
peaks_find(object, ...)

## S4 method for signature 'GammaSpectrum'
peaks_find(object, method = c("MAD"), SNR = 2, span = NULL, ...)

Arguments

`object`	A GammaSpectrum object.
`...`	Extra parameters to be passed to internal methods.
`method`	A `character` string specifying the method to be used for background noise estimation (see below).
`SNR`	An `integer` giving the signal-to-noise-ratio for peak detection (see below).
`span`	An `integer` giving the half window size (in number of channels). If `NULL`, 5\ window size.

Details

A local maximum has to be the highest one in the given window and has to be higher than $SNR \times noise$ to be recognized as peak.

The following methods are available for noise estimation:

MAD: Median Absolute Deviation.

Value

A PeakPosition object.

Author(s)

N. Frerebeau

Examples

## Import a LaBr spectrum
LaBr_file <- system.file("extdata/LaBr.TKA", package = "gamma")
LaBr_spc <- read(LaBr_file)

## Find peaks by channel
(LaBr_pks <- peaks_find(LaBr_spc)) # Ugly
plot(LaBr_spc, LaBr_pks)

## Search peaks by channel
(LaBr_pks <- peaks_search(LaBr_spc, index = c(86L, 207L, 496L), span = 7))
plot(LaBr_spc, LaBr_pks, split = TRUE)

## Import a BEGe spectrum
BEGe_file <- system.file("extdata/BEGe.CNF", package = "gamma")
BEGe_spc <- read(BEGe_file)

## Search peaks by energy
(BEGe_pks <- peaks_search(BEGe_spc, index = c(47, 63, 911, 1460)))
plot(BEGe_spc, BEGe_pks, split = TRUE)
## Import a LaBr spectrum
LaBr_file <- system.file("extdata/LaBr.TKA", package = "gamma")
LaBr_spc <- read(LaBr_file)

## Find peaks by channel
(LaBr_pks <- peaks_find(LaBr_spc)) # Ugly
plot(LaBr_spc, LaBr_pks)

## Search peaks by channel
(LaBr_pks <- peaks_search(LaBr_spc, index = c(86L, 207L, 496L), span = 7))
plot(LaBr_spc, LaBr_pks, split = TRUE)

## Import a BEGe spectrum
BEGe_file <- system.file("extdata/BEGe.CNF", package = "gamma")
BEGe_spc <- read(BEGe_file)

## Search peaks by energy
(BEGe_pks <- peaks_search(BEGe_spc, index = c(47, 63, 911, 1460)))
plot(BEGe_spc, BEGe_pks, split = TRUE)

Search Peaks

Description

Search the maxima in sequential data around a given value.

Usage

peaks_search(object, index, ...)

## S4 method for signature 'GammaSpectrum,integer'
peaks_search(object, index, span = 10, tolerance = 0.025)

## S4 method for signature 'GammaSpectrum,numeric'
peaks_search(object, index, span = 10, tolerance = 0.025)
peaks_search(object, index, ...)

## S4 method for signature 'GammaSpectrum,integer'
peaks_search(object, index, span = 10, tolerance = 0.025)

## S4 method for signature 'GammaSpectrum,numeric'
peaks_search(object, index, span = 10, tolerance = 0.025)

Arguments

`object`	A GammaSpectrum object.
`index`	A vector giving the expected peak position. If `index` is a `numeric` vector, peaks are searched by energy (`index` is assumed to be expressed in keV). If `index` is an `integer` vector, peaks are searched by channel.
`...`	Currently not used.
`span`	A `numeric` value giving the half window size for searching. If `index` is a `numeric` vector, `span` is expressed in keV. If `index` is an `integer` vector, `span` is expressed in channel.
`tolerance`	A `numeric` value giving the threshold above which a warning/error is raised.

Value

A PeakPosition object.

Author(s)

N. Frerebeau

Examples

## Import a LaBr spectrum
LaBr_file <- system.file("extdata/LaBr.TKA", package = "gamma")
LaBr_spc <- read(LaBr_file)

## Find peaks by channel
(LaBr_pks <- peaks_find(LaBr_spc)) # Ugly
plot(LaBr_spc, LaBr_pks)

## Search peaks by channel
(LaBr_pks <- peaks_search(LaBr_spc, index = c(86L, 207L, 496L), span = 7))
plot(LaBr_spc, LaBr_pks, split = TRUE)

## Import a BEGe spectrum
BEGe_file <- system.file("extdata/BEGe.CNF", package = "gamma")
BEGe_spc <- read(BEGe_file)

## Search peaks by energy
(BEGe_pks <- peaks_search(BEGe_spc, index = c(47, 63, 911, 1460)))
plot(BEGe_spc, BEGe_pks, split = TRUE)
## Import a LaBr spectrum
LaBr_file <- system.file("extdata/LaBr.TKA", package = "gamma")
LaBr_spc <- read(LaBr_file)

## Find peaks by channel
(LaBr_pks <- peaks_find(LaBr_spc)) # Ugly
plot(LaBr_spc, LaBr_pks)

## Search peaks by channel
(LaBr_pks <- peaks_search(LaBr_spc, index = c(86L, 207L, 496L), span = 7))
plot(LaBr_spc, LaBr_pks, split = TRUE)

## Import a BEGe spectrum
BEGe_file <- system.file("extdata/BEGe.CNF", package = "gamma")
BEGe_spc <- read(BEGe_file)

## Search peaks by energy
(BEGe_pks <- peaks_search(BEGe_spc, index = c(47, 63, 911, 1460)))
plot(BEGe_spc, BEGe_pks, split = TRUE)

Plot

Description

Plot

Usage

## S4 method for signature 'GammaSpectrum,missing'
plot(x, xaxis = c("channel", "energy"), yaxis = c("count", "rate"), ...)

## S4 method for signature 'GammaSpectrum,Baseline'
plot(x, y, xaxis = c("channel", "energy"), yaxis = c("count", "rate"), ...)

## S4 method for signature 'GammaSpectra,missing'
plot(
  x,
  xaxis = c("channel", "energy"),
  yaxis = c("count", "rate"),
  select = NULL,
  facet = FALSE,
  nrow = c("fixed", "auto")
)

## S4 method for signature 'GammaSpectrum,PeakPosition'
plot(x, y, split = FALSE, span = 25)

## S4 method for signature 'CalibrationCurve,missing'
plot(
  x,
  error_ellipse = TRUE,
  error_bar = FALSE,
  energy = FALSE,
  level = 0.95,
  n = 50,
  ...
)
## S4 method for signature 'GammaSpectrum,missing'
plot(x, xaxis = c("channel", "energy"), yaxis = c("count", "rate"), ...)

## S4 method for signature 'GammaSpectrum,Baseline'
plot(x, y, xaxis = c("channel", "energy"), yaxis = c("count", "rate"), ...)

## S4 method for signature 'GammaSpectra,missing'
plot(
  x,
  xaxis = c("channel", "energy"),
  yaxis = c("count", "rate"),
  select = NULL,
  facet = FALSE,
  nrow = c("fixed", "auto")
)

## S4 method for signature 'GammaSpectrum,PeakPosition'
plot(x, y, split = FALSE, span = 25)

## S4 method for signature 'CalibrationCurve,missing'
plot(
  x,
  error_ellipse = TRUE,
  error_bar = FALSE,
  energy = FALSE,
  level = 0.95,
  n = 50,
  ...
)

Arguments

`x`, `y`	Objects to be plotted.
`xaxis`, `yaxis`	A `character` string specifying the data to be plotted along each axis. It must be one of "`energy`" or "`channel`" (`x` axis) and "`counts`" or "`rate`" (`y` axis). Any unambiguous substring can be given.
`...`	Currently not used.
`select`	A `numeric` or `character` vector giving the selection of the spectrum that are drawn.
`facet`	A `logical` scalar: should a matrix of panels defined by spectrum be drawn?
`nrow`	A `character` string specifying the number of rows. It must be one of "`fixed`" or "`auto`". Any unambiguous substring can be given. Only used if `facet` is `TRUE`.
`split`	A `logical` scalar: should.
`span`	An `integer` giving the half window size (in number of channels). Only used if `split` is `TRUE`.
`error_ellipse`	A `logical` scalar: should error ellipses be plotted?
`error_bar`	A `logical` scalar: should error bars be plotted?
`energy`	A `logical` scalar plotting the count threshold value or the energy threshold value
`level`	length-one `numeric` vector giving the the probability cutoff for the error ellipses.
`n`	A length-one `numeric` vector giving the resolution of the error ellipses.

Value

A ggplot2::ggplot object.

Author(s)

N. Frerebeau

Examples

# Import CNF files
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)

# Plot all spectra
plot(spc, yaxis = "rate", facet = FALSE) +
  ggplot2::theme_bw()

# Plot the spectrum named 'BRIQUE'
plot(spc, xaxis = "energy", yaxis = "count", select = "BRIQUE") +
  ggplot2::theme_bw()

# Plot the first three spectra
plot(spc, xaxis = "channel", yaxis = "rate", select = 1:3, facet = TRUE) +
  ggplot2::theme_bw()
# Import CNF files
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)

# Plot all spectra
plot(spc, yaxis = "rate", facet = FALSE) +
  ggplot2::theme_bw()

# Plot the spectrum named 'BRIQUE'
plot(spc, xaxis = "energy", yaxis = "count", select = "BRIQUE") +
  ggplot2::theme_bw()

# Plot the first three spectra
plot(spc, xaxis = "channel", yaxis = "rate", select = 1:3, facet = TRUE) +
  ggplot2::theme_bw()

Data Input

Description

Reads a gamma ray spectrum file.

Usage

read(file, ...)

## S4 method for signature 'character'
read(file, extensions = c("cnf", "tka", "spe"), ...)
read(file, ...)

## S4 method for signature 'character'
read(file, extensions = c("cnf", "tka", "spe"), ...)

Arguments

`file`	A `character` string giving the path of files to be imported.
`...`	Extra parameters to be passed to `rxylib::read_xyData()`.
`extensions`	A `character` vector specifying the possible file extensions. It must be one or more of "`cnf`", "`tka`", "`spe`".

Value

A GammaSpectra object if more than one spectrum are imported at once, else a GammaSpectrum object.

Note

Supports Canberra CNF and TKA and Kromek SPE files.

Author(s)

N. Frerebeau

Examples

## Import a Canberra CNF file
cnf_file <- system.file("extdata/LaBr.CNF", package = "gamma")
(cnf_spc <- read(cnf_file))

## Import a TKA file
tka_file <- system.file("extdata/LaBr.TKA", package = "gamma")
(tka_spc <- read(tka_file))

## Import all files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
(spc <- read(spc_dir))
## Import a Canberra CNF file
cnf_file <- system.file("extdata/LaBr.CNF", package = "gamma")
(cnf_spc <- read(cnf_file))

## Import a TKA file
tka_file <- system.file("extdata/LaBr.TKA", package = "gamma")
(tka_spc <- read(tka_file))

## Import all files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
(spc <- read(spc_dir))

Signal Integration

Description

Integration of the spectrum including uncertainty calculation.

Usage

signal_integrate(object, background, ...)

## S4 method for signature 'GammaSpectrum,missing'
signal_integrate(object, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
signal_integrate(object, background, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectrum,numeric'
signal_integrate(object, background, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectra,missing'
signal_integrate(object, range = NULL, energy = FALSE, simplify = TRUE)

## S4 method for signature 'GammaSpectra,GammaSpectrum'
signal_integrate(
  object,
  background,
  range = NULL,
  energy = FALSE,
  simplify = TRUE
)

## S4 method for signature 'GammaSpectra,numeric'
signal_integrate(
  object,
  background,
  range = NULL,
  energy = FALSE,
  simplify = TRUE
)
signal_integrate(object, background, ...)

## S4 method for signature 'GammaSpectrum,missing'
signal_integrate(object, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectrum,GammaSpectrum'
signal_integrate(object, background, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectrum,numeric'
signal_integrate(object, background, range = NULL, energy = FALSE)

## S4 method for signature 'GammaSpectra,missing'
signal_integrate(object, range = NULL, energy = FALSE, simplify = TRUE)

## S4 method for signature 'GammaSpectra,GammaSpectrum'
signal_integrate(
  object,
  background,
  range = NULL,
  energy = FALSE,
  simplify = TRUE
)

## S4 method for signature 'GammaSpectra,numeric'
signal_integrate(
  object,
  background,
  range = NULL,
  energy = FALSE,
  simplify = TRUE
)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`background`	A GammaSpectrum object.
`...`	Currently not used.
`range`	A length-two `numeric` vector giving the energy range to integrate within (in keV).
`energy`	A `logical` scalar: use the energy or count threshold for the signal integration
`simplify`	A `logical` scalar: should the result be simplified to a `matrix`? The default value, `FALSE`, returns a `list`.

Details

The function supports two integration techniques (see Guérin & Mercier 2011), the (1) count threshold integration and the (2) energy integration method:

The count integration technique (energy = FALSE) integrates all counts in given range:

$A = \frac{\Sigma_{i}^{N}S_i}{t_{live}}$

Contrary, the energy integration techniques is the integrated cross-product of counts and corresponding energy per channel:

$A = \frac{\Sigma_{i}^{N}S_i \times E_i}{t_{live}}$

$A$ is the area, $S_i$ is the signal in the $i^{th}$ channel, $N$ the number of channels, $E_i$ the energy of the corresponding channel in keV. $t_{live}$ is the live time of the measurement in s.

For calculating the uncertainties, Poisson statistics are assumed and hence the errors is calculated as:

$\sigma_A = \frac{\sqrt{A}}{t_{live}}$

Value

If simplify is FALSE (the default) returns a list of numeric vectors (the signal value and its error), else returns a matrix.

Note

The integration assumes that each spectrum has an energy scale.

Author(s)

N. Frerebeau

References

Guérin, G. & Mercier, M. (2011). Determining Gamma Dose Rates by Field Gamma Spectroscopy in Sedimentary Media: Results of Monte Carlo Simulations. Radiation Measurements, 46(2), p. 190-195. doi:10.1016/j.radmeas.2010.10.003.

Mercier, N. & Falguères, C. (2007). Field Gamma Dose-Rate Measurement with a NaI(Tl) Detector: Re-Evaluation of the "Threshold" Technique. Ancient TL, 25(1), p. 1-4.

Choose channels by Position

Description

Choose channels by position.

Usage

signal_slice(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_slice(object, ...)

## S4 method for signature 'GammaSpectra'
signal_slice(object, ...)
signal_slice(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_slice(object, ...)

## S4 method for signature 'GammaSpectra'
signal_slice(object, ...)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`...`	`integer` values giving the channels of the spectrum to be kept/dropped (see below). Numeric values are coerced to integer as by `as.integer()` (and hence truncated towards zero).

Details

Either positive values to keep, or negative values to drop, should be provided. The values provided must be either all positive or all negative.

If no value is provided, an attempt is made to define the number of channels to skip at the beginning of the spectrum. This drops all channels before the highest count maximum. This is intended to deal with the artefact produced by the rapid growth of random background noise towards low energies.

Value

A GammaSpectrum or GammaSpectra object.

Author(s)

N. Frerebeau

Examples

## Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Plot spectrum
plot(spc)

## Slice
sliced <- signal_slice(spc)
plot(sliced)

sliced <- signal_slice(spc, -c(1:35))
plot(sliced)

sliced <- signal_slice(sliced, 450:550)
plot(sliced)

## Split
g <- rep(c("A", "B", "C"), c(250, 500, 274))
splited <- signal_split(spc, g)
plot(splited, facet = TRUE)
## Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Plot spectrum
plot(spc)

## Slice
sliced <- signal_slice(spc)
plot(sliced)

sliced <- signal_slice(spc, -c(1:35))
plot(sliced)

sliced <- signal_slice(sliced, 450:550)
plot(sliced)

## Split
g <- rep(c("A", "B", "C"), c(250, 500, 274))
splited <- signal_split(spc, g)
plot(splited, facet = TRUE)

Split

Description

Split

Usage

signal_split(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_split(object, groups)
signal_split(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_split(object, groups)

Arguments

`object`	A GammaSpectrum object.
`...`	Currently not used.
`groups`	A a `factor` in the sense that `as.factor(groups)` defines the grouping (see `split`).

Value

A GammaSpectra object.

Author(s)

N. Frerebeau

Examples

## Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Plot spectrum
plot(spc)

## Slice
sliced <- signal_slice(spc)
plot(sliced)

sliced <- signal_slice(spc, -c(1:35))
plot(sliced)

sliced <- signal_slice(sliced, 450:550)
plot(sliced)

## Split
g <- rep(c("A", "B", "C"), c(250, 500, 274))
splited <- signal_split(spc, g)
plot(splited, facet = TRUE)
## Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)

## Plot spectrum
plot(spc)

## Slice
sliced <- signal_slice(spc)
plot(sliced)

sliced <- signal_slice(spc, -c(1:35))
plot(sliced)

sliced <- signal_slice(sliced, 450:550)
plot(sliced)

## Split
g <- rep(c("A", "B", "C"), c(250, 500, 274))
splited <- signal_split(spc, g)
plot(splited, facet = TRUE)

Transform Intensities

Description

Transform Intensities

Usage

signal_stabilize(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_stabilize(object, f, ...)

## S4 method for signature 'GammaSpectra'
signal_stabilize(object, f, ...)
signal_stabilize(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_stabilize(object, f, ...)

## S4 method for signature 'GammaSpectra'
signal_stabilize(object, f, ...)

Arguments

`object`	A GammaSpectrum object.
`...`	Extra arguments to be passed to `f`.
`f`	A `function` that takes a numeric vector as argument and returns a numeric vector.

Details

The stabilization step aims at improving the identification of peaks with a low signal-to-noise ratio. This particularly targets higher energy peaks.

Value

A GammaSpectrum or GammaSpectra object with transformed intensities.

Author(s)

N. Frerebeau

Smooth

Description

Smoothes intensities.

Usage

signal_smooth(object, ...)

smooth_rectangular(object, ...)

smooth_triangular(object, ...)

smooth_savitzky(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_smooth(object, method = c("rectangular", "triangular", "savitzky"), ...)

## S4 method for signature 'GammaSpectra'
signal_smooth(object, method = c("rectangular", "triangular", "savitzky"), ...)

## S4 method for signature 'GammaSpectrum'
smooth_rectangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectra'
smooth_rectangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectrum'
smooth_savitzky(object, m = 3, p = 2, ...)

## S4 method for signature 'GammaSpectra'
smooth_savitzky(object, m = 3, p = 2, ...)

## S4 method for signature 'GammaSpectrum'
smooth_triangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectra'
smooth_triangular(object, m = 3, ...)
signal_smooth(object, ...)

smooth_rectangular(object, ...)

smooth_triangular(object, ...)

smooth_savitzky(object, ...)

## S4 method for signature 'GammaSpectrum'
signal_smooth(object, method = c("rectangular", "triangular", "savitzky"), ...)

## S4 method for signature 'GammaSpectra'
signal_smooth(object, method = c("rectangular", "triangular", "savitzky"), ...)

## S4 method for signature 'GammaSpectrum'
smooth_rectangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectra'
smooth_rectangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectrum'
smooth_savitzky(object, m = 3, p = 2, ...)

## S4 method for signature 'GammaSpectra'
smooth_savitzky(object, m = 3, p = 2, ...)

## S4 method for signature 'GammaSpectrum'
smooth_triangular(object, m = 3, ...)

## S4 method for signature 'GammaSpectra'
smooth_triangular(object, m = 3, ...)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`...`	Extra parameters to be passed to further methods.
`method`	A `character` string specifying the smoothing method to be used. It must be one of "`unweighted`" (default), "`weighted`" or "`savitzky`" (see details). Any unambiguous substring can be given.
`m`	An odd `integer` giving the number of adjacent points to be used.
`p`	An `integer` giving the polynomial degree. Only used if `method` is "`savitzky`".

Details

The following smoothing methods are available:

rectangular: Unweighted sliding-average or rectangular smooth. It replaces each point in the signal with the average of $m$ adjacent points.
triangular: Weighted sliding-average or triangular smooth. It replaces each point in the signal with the weighted mean of $m$ adjacent points.
savitzky: Savitzky-Golay filter. This method is based on the least-squares fitting of polynomials to segments of $m$ adjacent points.

There will be $(m - 1) / 2$ points both at the beginning and at the end of the spectrum for which a complete $m$ -width smooth cannot be calculated. To prevent data loss, progressively smaller smooths are used at the ends of the spectrum if method is unweighted or weighted. If the Savitzky-Golay filter is used, the original $(m - 1) / 2$ points at the ends of the spectrum are preserved.

Value

A GammaSpectrum or GammaSpectra object.

Author(s)

N. Frerebeau

References

Gorry, P. A. (1990). General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method. Analytical Chemistry, 62(6), p. 570-573. doi:10.1021/ac00205a007.

Savitzky, A. & Golay, M. J. E. (1964). Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36(8), p. 1627-1639. doi:10.1021/ac60214a047.

Examples

# Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)
spc <- signal_slice(spc, -c(1:35))

# Plot raw spectrum
spc_clean <- signal_correct(spc)
plot(spc_clean)

# Rectangular smooth
spc_unweighted <- smooth_rectangular(spc, m = 3)
spc_unweighted_clean <- signal_correct(spc_unweighted)
plot(spc_unweighted_clean)

# Triangular smooth
spc_weighted <- smooth_triangular(spc, m = 5)
spc_weighted_clean <- signal_correct(spc_weighted)
plot(spc_weighted_clean)

# Savitzky–Golay
spc_savitzky <- smooth_savitzky(spc, m = 21, p = 2)
spc_savitzky_clean <- signal_correct(spc_savitzky)
plot(spc_savitzky_clean)
# Import CNF files
spc_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(spc_file)
spc <- signal_slice(spc, -c(1:35))

# Plot raw spectrum
spc_clean <- signal_correct(spc)
plot(spc_clean)

# Rectangular smooth
spc_unweighted <- smooth_rectangular(spc, m = 3)
spc_unweighted_clean <- signal_correct(spc_unweighted)
plot(spc_unweighted_clean)

# Triangular smooth
spc_weighted <- smooth_triangular(spc, m = 5)
spc_weighted_clean <- signal_correct(spc_weighted)
plot(spc_weighted_clean)

# Savitzky–Golay
spc_savitzky <- smooth_savitzky(spc, m = 21, p = 2)
spc_savitzky_clean <- signal_correct(spc_savitzky)
plot(spc_savitzky_clean)

Extract or Replace Parts of an Object

Description

Operators acting on objects to extract or replace parts.

Usage

## S4 method for signature 'GammaSpectrum'
x[[i]]

## S4 method for signature 'GammaSpectra'
x[i, j]

## S4 method for signature 'DoseRateModel'
x[[i]]

## S4 method for signature 'CalibrationCurve'
x[[i]]

## S4 method for signature 'PeakPosition'
x[[i]]
## S4 method for signature 'GammaSpectrum'
x[[i]]

## S4 method for signature 'GammaSpectra'
x[i, j]

## S4 method for signature 'DoseRateModel'
x[[i]]

## S4 method for signature 'CalibrationCurve'
x[[i]]

## S4 method for signature 'PeakPosition'
x[[i]]

Arguments

`x`	An object from which to extract element(s) or in which to replace element(s).
`i`, `j`	Indices specifying elements to extract or replace. Indices are `numeric`, `integer` or `character` vectors or empty (missing) or `NULL`. Numeric values are coerced to `integer` as by `as.integer()` (and hence truncated towards zero). Character vectors will be matched to the name of the elements. An empty index (a comma separated blank) indicates that all entries in that dimension are selected.

Value

A subsetted object.

Author(s)

N. Frerebeau

Summarize

Description

Summarize

Usage

summarise(object, ...)

## S4 method for signature 'GammaSpectrum'
summarise(object)

## S4 method for signature 'GammaSpectra'
summarise(object)

## S4 method for signature 'DoseRateModel'
summarise(object)

## S4 method for signature 'CalibrationCurve'
summarise(object)
summarise(object, ...)

## S4 method for signature 'GammaSpectrum'
summarise(object)

## S4 method for signature 'GammaSpectra'
summarise(object)

## S4 method for signature 'DoseRateModel'
summarise(object)

## S4 method for signature 'CalibrationCurve'
summarise(object)

Arguments

`object`	A GammaSpectrum or GammaSpectra object.
`...`	Currently not used.

Value

A data.frame.

Author(s)

N. Frerebeau

Examples

## Import a Canberra CNF file
cnf_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(cnf_file)
summarise(spc)

## Import all CNF files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)
summarise(spc)
## Import a Canberra CNF file
cnf_file <- system.file("extdata/LaBr.CNF", package = "gamma")
spc <- read(cnf_file)
summarise(spc)

## Import all CNF files in a given directory
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)
summarise(spc)

Package 'gamma'

Help Index

CEREGE Calibration Curve (NaI)

Description

Usage

Format

See Also

Examples

Baseline Estimation and Removal

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

An S4 Class to Represent a Spectrum Baseline

Description

Note

Author(s)

See Also

Examples

CRP2A Calibration Curve (LaBr)

Description

Usage

Format

See Also

Examples

An S4 class to Represent a Dose Rate Calibration Curve

Description

Slots

Subset

Author(s)

See Also

Clermont Reference Data

Description

Usage

Format

Source

See Also

Clermont Reference Data 2024

Description

Usage

Format

Source

See Also

Coerce

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Dose Rate Estimation

Description

Usage

Arguments

Details

Value

Uncertainty calculation of the gamma-dose rate

Water content correction

Note

Author(s)

References

See Also

Examples

Energy Scale Calibration

Description

Usage

Arguments

Details

Value

Author(s)

Examples

An S4 Class to Represent a Collection of Gamma Sectra

Description