knitr::opts_chunk$set( collapse = TRUE, message = FALSE, fig.width = 7, fig.height = 3.5, comment = "#>" )
library(gamma)
This vignette shows how to build a calibration curve for gamma dose rate prediction.
# Import CNF files for calibration spc_dir <- system.file("extdata/AIX_NaI_1/calibration", package = "gamma") (spc <- read(spc_dir)) # Import a CNF file of background measurement bkg_dir <- system.file("extdata/AIX_NaI_1/background", package = "gamma") (bkg <- read(bkg_dir))
# Spectrum pre-processing # Remove baseline for peak detection bsl <- spc |> signal_slice(-1:-40) |> signal_stabilize(f = sqrt) |> signal_smooth(method = "savitzky", m = 21) |> signal_correct()
# Peak detection pks <- peaks_find(bsl[["BRIQUE"]]) # Set energy values set_energy(pks) <- c(238, NA, NA, NA, 1461, NA, NA, 2615) # Adjust the energy scale BRIQUE <- energy_calibrate(spc[["BRIQUE"]], pks)
plot(BRIQUE, pks) + ggplot2::theme_bw()
# Spectrum pre-processing and peak detection pks <- peaks_find(bsl[["C341"]]) # Set energy values set_energy(pks) <- c(238, NA, NA, NA, 1461, NA, 2615) # Adjust the energy scale C341 <- energy_calibrate(spc[["C341"]], pks)
plot(C341, pks) + ggplot2::theme_bw()
# Spectrum pre-processing and peak detection pks <- peaks_find(bsl[["C347"]], span = 10) # Set energy values set_energy(pks) <- c(238, NA, NA, NA, NA, 1461, NA, 2615) # Adjust the energy scale C347 <- energy_calibrate(spc[["C347"]], pks)
plot(C347, pks) + ggplot2::theme_bw()
# Spectrum pre-processing and peak detection pks <- peaks_find(bsl[["GOU"]]) # Set energy values set_energy(pks) <- c(238, NA, NA, NA, 1461, NA, NA, 2615) # Adjust the energy scale GOU <- energy_calibrate(spc[["GOU"]], pks)
plot(GOU, pks) + ggplot2::theme_bw()
# Spectrum pre-processing and peak detection pks <- peaks_find(bsl[["PEP"]]) # Set energy values set_energy(pks) <- c(238, NA, NA, NA, 1461, NA, NA, 2615) # Adjust the energy scale PEP <- energy_calibrate(spc[["PEP"]], pks)
plot(PEP, pks) + ggplot2::theme_bw()
# Pb212, K40, Tl208 lines <- data.frame( channel = c(86, 496, 870), energy = c(238, 1461, 2615) ) bkg_scaled <- energy_calibrate(bkg, lines = lines)
plot(bkg_scaled, xaxis = "energy", yaxis = "rate") + ggplot2::geom_vline(xintercept = c(238, 1461, 2615), linetype = 3) + ggplot2::theme_bw()
spc_scaled <- list(BRIQUE, C341, C347, GOU, PEP) spc_scaled <- methods::as(spc_scaled, "GammaSpectra")
# Integration range (in keV) Ni_range <- c(200, 2800) # Integrate background spectrum (Ni_bkg <- signal_integrate(bkg_scaled, range = Ni_range, energy = FALSE)) # Integrate reference spectra (Ni_spc <- signal_integrate(spc_scaled, range = Ni_range, background = Ni_bkg, energy = FALSE, simplify = TRUE))
# Integration range (in keV) NiEi_range <- c(200, 2800) # Integrate background spectrum (NiEi_bkg <- signal_integrate(bkg_scaled, range = NiEi_range, energy = TRUE)) # Integrate reference spectra (NiEi_signal <- signal_integrate(spc_scaled, range = NiEi_range, background = NiEi_bkg, energy = TRUE, simplify = TRUE))
# Get reference dose rates data("clermont") doses <- clermont[, c("gamma_dose", "gamma_error")] # Metadata info <- list( laboratory = "CEREGE", instrument = "InSpector 1000", detector = "NaI", authors = "CEREGE Luminescence Team" ) # Build the calibration curve AIX_NaI <- dose_fit( spc_scaled, background = bkg_scaled, doses = doses, range_Ni = Ni_range, range_NiEi = NiEi_range, details = info ) # Summary summarise(AIX_NaI) # Plot curve plot(AIX_NaI, model = "Ni") + ggplot2::theme_bw() plot(AIX_NaI, model = "NiEi") + ggplot2::theme_bw()
# DANGER ZONE # AIX_NaI_1 <- AIX_NaI # usethis::use_data(AIX_NaI_1, internal = FALSE, overwrite = FALSE)
Ni_residuals <- get_residuals(AIX_NaI[["Ni"]]) # Residuals vs fitted values ggplot2::ggplot(Ni_residuals, ggplot2::aes(x = fitted, y = residuals)) + ggplot2::geom_hline(yintercept = 0, linetype = 3) + ggplot2::geom_segment(ggplot2::aes(x = fitted, xend = fitted, y = 0, yend = residuals)) + ggplot2::geom_point() + ggplot2::theme_bw() + ggplot2::labs(title = "Residuals vs fitted values", x = "Fitted values", y = "Residuals") # Std. residuals vs fitted values ggplot2::ggplot(Ni_residuals, ggplot2::aes(x = fitted, y = standardized)) + ggplot2::geom_hline(yintercept = 0, linetype = 3) + ggplot2::geom_hline(yintercept = c(-2, 2), linetype = 2) + ggplot2::geom_segment(ggplot2::aes(x = fitted, xend = fitted, y = 0, yend = standardized)) + ggplot2::geom_point() + ggplot2::theme_bw() + ggplot2::labs(title = "Std. residuals vs fitted values", x = "Fitted values", y = "Standardized residuals") # Normal QQ plot of standardized residuals ggplot2::ggplot(Ni_residuals, ggplot2::aes(sample = standardized)) + ggplot2::geom_abline(slope = 1, intercept = 0) + ggplot2::geom_qq_line(colour = "red") + ggplot2::geom_qq() + ggplot2::theme_bw() + ggplot2::labs(title = "Normal QQ plot", x = "Theoretical quantiles", y = "Standardize residuals") # Cook's distance # ggplot2::ggplot(Ni_residuals, ggplot2::aes(x = name, y = cook)) + # ggplot2::geom_hline(yintercept = 0, linetype = 3) + # ggplot2::geom_hline(yintercept = 1, linetype = 2) + # ggplot2::geom_segment(ggplot2::aes(x = name, xend = name, # y = 0, yend = cook)) + # ggplot2::geom_point() + # ggplot2::theme_bw() + # ggplot2::labs(title = "Cook's distance", # x = "Observation", y = "D")
NiEi_residuals <- get_residuals(AIX_NaI[["NiEi"]]) # Residuals vs fitted values ggplot2::ggplot(NiEi_residuals, ggplot2::aes(x = fitted, y = residuals)) + ggplot2::geom_hline(yintercept = 0, linetype = 3) + ggplot2::geom_segment(ggplot2::aes(x = fitted, xend = fitted, y = 0, yend = residuals)) + ggplot2::geom_point() + ggplot2::theme_bw() + ggplot2::labs(title = "Residuals vs fitted values", x = "Fitted values", y = "Residuals") # Std. residuals vs fitted values ggplot2::ggplot(NiEi_residuals, ggplot2::aes(x = fitted, y = standardized)) + ggplot2::geom_hline(yintercept = 0, linetype = 3) + ggplot2::geom_hline(yintercept = c(-2, 2), linetype = 2) + ggplot2::geom_segment(ggplot2::aes(x = fitted, xend = fitted, y = 0, yend = standardized)) + ggplot2::geom_point() + ggplot2::theme_bw() + ggplot2::labs(title = "Std. residuals vs fitted values", x = "Fitted values", y = "Standardized residuals") # Normal QQ plot of standardized residuals ggplot2::ggplot(NiEi_residuals, ggplot2::aes(sample = standardized)) + ggplot2::geom_abline(slope = 1, intercept = 0) + ggplot2::geom_qq_line(colour = "red") + ggplot2::geom_qq() + ggplot2::theme_bw() + ggplot2::labs(title = "Normal QQ plot", x = "Theoretical quantiles", y = "Standardize residuals") # Cook's distance # ggplot2::ggplot(NiEi_residuals, ggplot2::aes(x = name, y = cook)) + # ggplot2::geom_hline(yintercept = 0, linetype = 3) + # ggplot2::geom_hline(yintercept = 1, linetype = 2) + # ggplot2::geom_segment(ggplot2::aes(x = name, xend = name, # y = 0, yend = cook)) + # ggplot2::geom_point() + # ggplot2::theme_bw() + # ggplot2::labs(title = "Cook's distance", # x = "Observation", y = "D")
# Import CNF files for dose rate prediction test_dir <- system.file("extdata/AIX_NaI_1/test", package = "gamma") (test <- read(test_dir)) # Inspect spectra plot(test, yaxis = "rate") + ggplot2::theme_bw() # Dose rate prediction # (assuming that the energy scale of each spectrum was adjusted first) (rates <- dose_predict(AIX_NaI, test, sigma = 2))
sessionInfo()
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