R/readSpectraData.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures.
Defines functions
readSpectraData
readSpectraData <- function(UploadDates, nearestColSubset = TRUE, dataTableReturned = TRUE) {
' # Initial code provided by Jordan Healy <jordan.healy@noaa.gov> '
sourceFunctionURL <- function (URL) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
require(httr)
File.ASCII <- tempfile()
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(URL)
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/readXlsx.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/get.subs.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/match.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/ColumnMove.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/catf.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/renum.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/rgit/master/R/gitAFile.R")
# Nearest Neighbor; x is the shorter column. The y elements nearest the x elements are returned.
nearbor <- function(x, y) {
Out <- NULL
for (i in 1:length(x))
Out <- c(Out, y[order(abs(y - x[i]))[1]])
Out
}
require(hexView)
require(simplerspec)
require(readxl)
homeDir <- paste0(getwd(), '/')
on.exit(setwd(homeDir))
# Load standard wave freq.
gitAFile("John-R-Wallace-NOAA/FishNIRS/master/WaveFreqs1331.RData", type = 'RData')
gitAFile("John-R-Wallace-NOAA/FishNIRS/master/WaveFreqs921.RData", type = 'RData')
# load("WaveFreqs1331.RData", envir = .GlobalEnv)
# load("WaveFreqs921.RData", envir = .GlobalEnv)
WaveFreqs1331.subset.for.921 <- as.numeric(WaveFreqs1331) %in% nearbor(as.numeric(WaveFreqs921), as.numeric(WaveFreqs1331))
# WaveFreqs921.B.subset.for.921 <- as.numeric(WaveFreqs921.B) %in% nearbor(as.numeric(WaveFreqs921), as.numeric(WaveFreqs921.B)) # Both 921 - just use the WaveFreqs921 column labels below
# Add 'X' prefix for data frame columns
WaveFreqs1331 <- paste0('X', WaveFreqs1331)
WaveFreqs921 <- paste0('X', WaveFreqs921)
hakeStabSpcStudy <- NULL
for(h in 1:length(UploadDates)) {
if( h %% 3 == 1) {
dev.new(width = 400, height = 300)
par(mfrow = c(3, 2))
}
for( i in UploadDates[h]) {
for(j in c("Dry", "Etoh")) {
setwd(paste0(homeDir, "../OPUS Spectra/", i, "/", j)) # Set working directory to folder containing spectral files
ldf <- list() # creates an empty list
# (listspc <- dir(pattern = '*HAKE*'))[1:20] # Alternate pattern that works
(listspc <- dir(pattern = '*\\.0'))[1:20] # Creates the list of all the '.0' file names in the directory
# Loops through and uploads each file, depends on simplerspec package
for (k in 1:length(listspc)) {
ldf[[k]] <- simplerspec::read_opus_bin_univ(listspc[k], extract = "spc", print_progress = TRUE, atm_comp_minus4offset = FALSE)
ldf[[k]][['spc']] <- data.frame(ldf[[k]][['spc']])
}
# print(str(ldf[[1]])) # check first element
cat("\n\n"); print(names(ldf[[1]]))
# cat("\n\n"); print(str(ldf[[1]])) # Debug only
Spc_df <- NULL
for (k in 1:length(ldf)) {
# cat("\n", k, "\n")
if(ncol(ldf[[k]][['spc']]) >= 1331) {
if(ncol(ldf[[k]][['spc']][, WaveFreqs1331]) == 1331) {
if(nearestColSubset) {
WaveFreqs1331.subset <- cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf,
ldf[[k]][['spc']][, WaveFreqs1331])[, c(TRUE, TRUE, WaveFreqs1331.subset.for.921)]
names(WaveFreqs1331.subset)[-(1:2)] <- WaveFreqs921 # Need the WaveFreqs921 labels for matching with true 921's
Spc_df <- rbind(Spc_df, WaveFreqs1331.subset)
WaveFreqsUsed <- WaveFreqs921
N <- 921
} else {
Spc_df <- rbind(Spc_df, cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf, ldf[[k]][['spc']][, WaveFreqs1331]))
WaveFreqsUsed <- WaveFreqs1331
N <- 1331
}
} else
catf('\nThe number of wave freqs is greater than 1,331, but the standard wave freqs are not a subset of that greater number\n')
} else {
# Code could handle Wavefreqs for which WaveFreqs921 or WaveFreqs921.B are a subset (as seen above for 1,331). This was not seen here for 921.
if(ncol(ldf[[k]][['spc']]) >= 921) {
if(ncol(ldf[[k]][['spc']]) == 921)
names(ldf[[k]][['spc']]) <- WaveFreqs921 # WaveFreqs921.B freqs are all close to WaveFreqs921 so just use WaveFreqs921
try( if(ncol(ldf[[k]][['spc']][, WaveFreqs921]) == 921) {
Spc_df <- rbind(Spc_df, cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf, ldf[[k]][['spc']][, WaveFreqs921]))
WaveFreqsUsed <- WaveFreqs921
N <- 921
} )
} else
catf('\nNumber', k, 'Sample', ldf[[k]]$metadata$sample_id, 'has length', length(ldf[[k]][['spc']]), 'which is less than 1,331, but not greater or equal to 921.\n')
}
}
if(is.null(Spc_df)) {
# Read in raggged data
Spc_df.ragged <- NULL
for (l in 1:length(ldf))
Spc_df.ragged <- rbind(Spc_df.ragged, renum(data.frame(Sample_ID = ldf[[l]]$metadata$sample_id,
Spectra = as.numeric(substring(names(ldf[[l]][['spc']]), 2)), Absorbance = unlist(ldf[[l]][['spc']]))))
# Look at the ragged data
plot(0, type = 'n', xlim = range(Spc_df.ragged$Spectra) + c(-10, 10), ylim = range(Spc_df.ragged$Absorbance) + c(-0.1, 0.1),
main = paste0(i, '; ', j), xlab = "Wavelength Energy (1/cm)", ylab = "Absorbance")
for( m in unique(Spc_df.ragged$Sample_ID) ) {
sampData <- Spc_df.ragged[Spc_df.ragged$Sample_ID %in% m, ]
lines(sampData$Spectra, sampData$Absorbance, col = ifelse(j == 'Dry', 'green', 'red'))
}
} else {
Spc_df$Sample_ID <- substr(Spc_df$Sample_ID, 1, 45) # Remove '.OA1' or 'OA2' suffix
# Need to have the standard wave freqs loaded above to check Spc_df on the fly
# WaveFreqs1331 <- names(Spc_df)[-(1:2)] # 1,331 Wave freq.
# save(WaveFreqs1331, file = 'WaveFreqs1331.RData')
# Look at spectra
# This shows extra data after the dry oties 1,331 readings
# dev.new()
# matplot(1:ncol(Spc_df), t(Spc_df), type = 'l', lty = 1, col = 'red', ) #
# Remove extra data after the dry oties 1,331 readings
# Spc_df <- Spc_df[, 1:1331]
# dev.new(width = 400, height = 300)
# matplot(1:ncol(Spc_df), t(Spc_df), col = ifelse(j == 'Dry', 'green', 'red'), type = 'l', lty = 1, main = paste0(i, '; ', j))
matplot(as.numeric(substring(WaveFreqsUsed, 2)), t(Spc_df[, WaveFreqsUsed]), col = ifelse(j == 'Dry', 'green', 'red'), type = 'l', lty = 1,
main = paste0(i, '; ', j), xlab = "Wavelength Energy (1/cm)", ylab = "Absorbance")
# Look at the data
cat("\n\n")
print(Spc_df[1:3, 1:10])
# Read in metadata
# hakeMetaData <- xlsxToR(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020.xlsx")
# hakeMetaData <- readxl::read_excel(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020.xlsx")
# Regardless of 'OA1' or 'OA2' - change all to 'OA1'
# Spc_df$Sample_ID <- paste0(substr(Spc_df$Sample_ID , 1, 46), 'OA1')
# if(i == "2019_11_01") {
# hakeMetaData <- read.csv(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020_OA1.csv")[1:87, ] # 87 good rows
# } else
# hakeMetaData <- read.csv(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020_OA2.csv")[1:86, ] # 86 good dry oties & 89 good Etoh
hakeMetaData <- data.frame(readxl::read_excel(paste0(homeDir, "../OPUS Spectra/", i, "/", j, "/", dir(pattern = '*QueryData*'))))
# hakeMetaData <- readXlsx(paste0(homeDir, "../OPUS Spectra/", i, "/", j, "/", dir(pattern = '*QueryData*')))
hakeMetaData <- hakeMetaData[!is.na(hakeMetaData$cruise_number), ]
if(!'readability' %in% names(hakeMetaData)) {
hakeMetaData$readability <- NA
hakeMetaData <- ColumnMove(hakeMetaData, 14)
}
hakeMetaData$date_collected <- as.POSIXct(as.character(hakeMetaData$date_collected), format = "%Y-%m-%d")
hakeMetaData$Sample_ID <- substr(hakeMetaData$file_name, 1, 45) # Remove '.OA1' or 'OA2' suffix
hakeMetaData$file_name <- NULL
# Match metadata to spectral data
Spc_Meta_df <- data.frame(Spc_df)
Spc_Meta_df$Storage <- j
Spc_Meta_df <- match.f(Spc_Meta_df, hakeMetaData, 'Sample_ID', 'Sample_ID', names(hakeMetaData)[-24])
catf('\nNumber of matches of metadata to Spectra table that occured:', sum(!is.na(Spc_Meta_df$collection_year)), "\n\n")
if(any(is.na(Spc_Meta_df$collection_year)))
stop("Good matching to metadata did not occur!")
Spc_Meta_df <- Spc_Meta_df[, c(1:2, N + 3:26, 3:(N + 2))] # Move metadata to the front
# print(names(Spc_Meta_df)[1:28]); cat("\n")
# print( WaveFreqsUsed[1:2] ); cat("\n")
metaDataNames <- c(names(Spc_df)[1:2], 'Storage', names(hakeMetaData)[-24]) # 26 metadata columns
# print( metaDataNames ); cat("\n")
print(Spc_Meta_df[1:4, c(metaDataNames, WaveFreqsUsed[1:2]) ])
hakeStabSpcStudy <- rbind(hakeStabSpcStudy, Spc_Meta_df)
}
}
}
}
if(dataTableReturned) {
hakeStabSpcStudy <- data.table(hakeStabSpcStudy)
names(hakeStabSpcStudy)[27:ncol(hakeStabSpcStudy)] <- substring(names(hakeStabSpcStudy)[27:ncol(hakeStabSpcStudy)], 2)
return(hakeStabSpcStudy)
} else
hakeStabSpcStudy
}
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.
R Package Documentation
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Defines functions readSpectraData
readSpectraData <- function(UploadDates, nearestColSubset = TRUE, dataTableReturned = TRUE) {
' # Initial code provided by Jordan Healy <jordan.healy@noaa.gov> '
sourceFunctionURL <- function (URL) {
" # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
require(httr)
File.ASCII <- tempfile()
on.exit(file.remove(File.ASCII))
getTMP <- httr::GET(URL)
write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
source(File.ASCII)
}
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/readXlsx.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/get.subs.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/match.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/ColumnMove.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/catf.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/renum.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/rgit/master/R/gitAFile.R")
# Nearest Neighbor; x is the shorter column. The y elements nearest the x elements are returned.
nearbor <- function(x, y) {
Out <- NULL
for (i in 1:length(x))
Out <- c(Out, y[order(abs(y - x[i]))[1]])
Out
}
require(hexView)
require(simplerspec)
require(readxl)
homeDir <- paste0(getwd(), '/')
on.exit(setwd(homeDir))
# Load standard wave freq.
gitAFile("John-R-Wallace-NOAA/FishNIRS/master/WaveFreqs1331.RData", type = 'RData')
gitAFile("John-R-Wallace-NOAA/FishNIRS/master/WaveFreqs921.RData", type = 'RData')
# load("WaveFreqs1331.RData", envir = .GlobalEnv)
# load("WaveFreqs921.RData", envir = .GlobalEnv)
WaveFreqs1331.subset.for.921 <- as.numeric(WaveFreqs1331) %in% nearbor(as.numeric(WaveFreqs921), as.numeric(WaveFreqs1331))
# WaveFreqs921.B.subset.for.921 <- as.numeric(WaveFreqs921.B) %in% nearbor(as.numeric(WaveFreqs921), as.numeric(WaveFreqs921.B)) # Both 921 - just use the WaveFreqs921 column labels below
# Add 'X' prefix for data frame columns
WaveFreqs1331 <- paste0('X', WaveFreqs1331)
WaveFreqs921 <- paste0('X', WaveFreqs921)
hakeStabSpcStudy <- NULL
for(h in 1:length(UploadDates)) {
if( h %% 3 == 1) {
dev.new(width = 400, height = 300)
par(mfrow = c(3, 2))
}
for( i in UploadDates[h]) {
for(j in c("Dry", "Etoh")) {
setwd(paste0(homeDir, "../OPUS Spectra/", i, "/", j)) # Set working directory to folder containing spectral files
ldf <- list() # creates an empty list
# (listspc <- dir(pattern = '*HAKE*'))[1:20] # Alternate pattern that works
(listspc <- dir(pattern = '*\\.0'))[1:20] # Creates the list of all the '.0' file names in the directory
# Loops through and uploads each file, depends on simplerspec package
for (k in 1:length(listspc)) {
ldf[[k]] <- simplerspec::read_opus_bin_univ(listspc[k], extract = "spc", print_progress = TRUE, atm_comp_minus4offset = FALSE)
ldf[[k]][['spc']] <- data.frame(ldf[[k]][['spc']])
}
# print(str(ldf[[1]])) # check first element
cat("\n\n"); print(names(ldf[[1]]))
# cat("\n\n"); print(str(ldf[[1]])) # Debug only
Spc_df <- NULL
for (k in 1:length(ldf)) {
# cat("\n", k, "\n")
if(ncol(ldf[[k]][['spc']]) >= 1331) {
if(ncol(ldf[[k]][['spc']][, WaveFreqs1331]) == 1331) {
if(nearestColSubset) {
WaveFreqs1331.subset <- cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf,
ldf[[k]][['spc']][, WaveFreqs1331])[, c(TRUE, TRUE, WaveFreqs1331.subset.for.921)]
names(WaveFreqs1331.subset)[-(1:2)] <- WaveFreqs921 # Need the WaveFreqs921 labels for matching with true 921's
Spc_df <- rbind(Spc_df, WaveFreqs1331.subset)
WaveFreqsUsed <- WaveFreqs921
N <- 921
} else {
Spc_df <- rbind(Spc_df, cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf, ldf[[k]][['spc']][, WaveFreqs1331]))
WaveFreqsUsed <- WaveFreqs1331
N <- 1331
}
} else
catf('\nThe number of wave freqs is greater than 1,331, but the standard wave freqs are not a subset of that greater number\n')
} else {
# Code could handle Wavefreqs for which WaveFreqs921 or WaveFreqs921.B are a subset (as seen above for 1,331). This was not seen here for 921.
if(ncol(ldf[[k]][['spc']]) >= 921) {
if(ncol(ldf[[k]][['spc']]) == 921)
names(ldf[[k]][['spc']]) <- WaveFreqs921 # WaveFreqs921.B freqs are all close to WaveFreqs921 so just use WaveFreqs921
try( if(ncol(ldf[[k]][['spc']][, WaveFreqs921]) == 921) {
Spc_df <- rbind(Spc_df, cbind(Sample_ID = ldf[[k]]$metadata$sample_id, Date_Time_rf = ldf[[k]]$metadata$date_time_rf, ldf[[k]][['spc']][, WaveFreqs921]))
WaveFreqsUsed <- WaveFreqs921
N <- 921
} )
} else
catf('\nNumber', k, 'Sample', ldf[[k]]$metadata$sample_id, 'has length', length(ldf[[k]][['spc']]), 'which is less than 1,331, but not greater or equal to 921.\n')
}
}
if(is.null(Spc_df)) {
# Read in raggged data
Spc_df.ragged <- NULL
for (l in 1:length(ldf))
Spc_df.ragged <- rbind(Spc_df.ragged, renum(data.frame(Sample_ID = ldf[[l]]$metadata$sample_id,
Spectra = as.numeric(substring(names(ldf[[l]][['spc']]), 2)), Absorbance = unlist(ldf[[l]][['spc']]))))
# Look at the ragged data
plot(0, type = 'n', xlim = range(Spc_df.ragged$Spectra) + c(-10, 10), ylim = range(Spc_df.ragged$Absorbance) + c(-0.1, 0.1),
main = paste0(i, '; ', j), xlab = "Wavelength Energy (1/cm)", ylab = "Absorbance")
for( m in unique(Spc_df.ragged$Sample_ID) ) {
sampData <- Spc_df.ragged[Spc_df.ragged$Sample_ID %in% m, ]
lines(sampData$Spectra, sampData$Absorbance, col = ifelse(j == 'Dry', 'green', 'red'))
}
} else {
Spc_df$Sample_ID <- substr(Spc_df$Sample_ID, 1, 45) # Remove '.OA1' or 'OA2' suffix
# Need to have the standard wave freqs loaded above to check Spc_df on the fly
# WaveFreqs1331 <- names(Spc_df)[-(1:2)] # 1,331 Wave freq.
# save(WaveFreqs1331, file = 'WaveFreqs1331.RData')
# Look at spectra
# This shows extra data after the dry oties 1,331 readings
# dev.new()
# matplot(1:ncol(Spc_df), t(Spc_df), type = 'l', lty = 1, col = 'red', ) #
# Remove extra data after the dry oties 1,331 readings
# Spc_df <- Spc_df[, 1:1331]
# dev.new(width = 400, height = 300)
# matplot(1:ncol(Spc_df), t(Spc_df), col = ifelse(j == 'Dry', 'green', 'red'), type = 'l', lty = 1, main = paste0(i, '; ', j))
matplot(as.numeric(substring(WaveFreqsUsed, 2)), t(Spc_df[, WaveFreqsUsed]), col = ifelse(j == 'Dry', 'green', 'red'), type = 'l', lty = 1,
main = paste0(i, '; ', j), xlab = "Wavelength Energy (1/cm)", ylab = "Absorbance")
# Look at the data
cat("\n\n")
print(Spc_df[1:3, 1:10])
# Read in metadata
# hakeMetaData <- xlsxToR(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020.xlsx")
# hakeMetaData <- readxl::read_excel(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020.xlsx")
# Regardless of 'OA1' or 'OA2' - change all to 'OA1'
# Spc_df$Sample_ID <- paste0(substr(Spc_df$Sample_ID , 1, 46), 'OA1')
# if(i == "2019_11_01") {
# hakeMetaData <- read.csv(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020_OA1.csv")[1:87, ] # 87 good rows
# } else
# hakeMetaData <- read.csv(homeDir, "../OPUS Spectra/NIR export exportQueryData 3_2_2020_OA2.csv")[1:86, ] # 86 good dry oties & 89 good Etoh
hakeMetaData <- data.frame(readxl::read_excel(paste0(homeDir, "../OPUS Spectra/", i, "/", j, "/", dir(pattern = '*QueryData*'))))
# hakeMetaData <- readXlsx(paste0(homeDir, "../OPUS Spectra/", i, "/", j, "/", dir(pattern = '*QueryData*')))
hakeMetaData <- hakeMetaData[!is.na(hakeMetaData$cruise_number), ]
if(!'readability' %in% names(hakeMetaData)) {
hakeMetaData$readability <- NA
hakeMetaData <- ColumnMove(hakeMetaData, 14)
}
hakeMetaData$date_collected <- as.POSIXct(as.character(hakeMetaData$date_collected), format = "%Y-%m-%d")
hakeMetaData$Sample_ID <- substr(hakeMetaData$file_name, 1, 45) # Remove '.OA1' or 'OA2' suffix
hakeMetaData$file_name <- NULL
# Match metadata to spectral data
Spc_Meta_df <- data.frame(Spc_df)
Spc_Meta_df$Storage <- j
Spc_Meta_df <- match.f(Spc_Meta_df, hakeMetaData, 'Sample_ID', 'Sample_ID', names(hakeMetaData)[-24])
catf('\nNumber of matches of metadata to Spectra table that occured:', sum(!is.na(Spc_Meta_df$collection_year)), "\n\n")
if(any(is.na(Spc_Meta_df$collection_year)))
stop("Good matching to metadata did not occur!")
Spc_Meta_df <- Spc_Meta_df[, c(1:2, N + 3:26, 3:(N + 2))] # Move metadata to the front
# print(names(Spc_Meta_df)[1:28]); cat("\n")
# print( WaveFreqsUsed[1:2] ); cat("\n")
metaDataNames <- c(names(Spc_df)[1:2], 'Storage', names(hakeMetaData)[-24]) # 26 metadata columns
# print( metaDataNames ); cat("\n")
print(Spc_Meta_df[1:4, c(metaDataNames, WaveFreqsUsed[1:2]) ])
hakeStabSpcStudy <- rbind(hakeStabSpcStudy, Spc_Meta_df)
}
}
}
}
if(dataTableReturned) {
hakeStabSpcStudy <- data.table(hakeStabSpcStudy)
names(hakeStabSpcStudy)[27:ncol(hakeStabSpcStudy)] <- substring(names(hakeStabSpcStudy)[27:ncol(hakeStabSpcStudy)], 2)
return(hakeStabSpcStudy)
} else
hakeStabSpcStudy
}
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