John-R-Wallace-NOAA/FishNIRS
NIRS on fish structures.

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R_Scripts/iPLS, NN Model Batch Self Call Loop.R

R_Scripts/iPLS, NN Model Batch Self Call Loop.R
In John-R-Wallace-NOAA/FishNIRS: NIRS on fish structures. 

####################################################################
# --- Initial setup  (The curly brakets with a '###' comment and without indented lines directly below them, are there to enable the hiding of code sections using Notepad++.) ---
#            (Notepad++ is here: https://notepad-plus-plus.org/ and NppToR that passes R code from Notepad++ to R is here: https://sourceforge.net/projects/npptor/)

# Spectral analysis initially followed Jordan Healey's code - thanks!

# You may need a 'GITHUB_PAT' from GitHub set somewhere in R (If you need help, search the Web how to get one from GitHub.)
# Sys.setenv(GITHUB_PAT = "**************")   # If you set GITHUB_PAT here, uncomment this line. Note, do not share your GITHUB_PAT, nor load it onto GitHib.
Sys.getenv("GITHUB_PAT") 
####################################################################


# --- Load functions and packages ---
{ ###

sourceFunctionURL <- function (URL,  type = c("function", "script")[1]) {
       " # For more functionality, see gitAFile() in the rgit package ( https://github.com/John-R-Wallace-NOAA/rgit ) which includes gitPush() and git() "
       if (!any(installed.packages()[, 1] %in% "httr"))  install.packages("httr") 
       File.ASCII <- tempfile()
       if(type == "function")
         on.exit(file.remove(File.ASCII))
       getTMP <- httr::GET(gsub(' ', '%20', URL))
       
       if(type == "function") {
         write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), File.ASCII)
         source(File.ASCII)
       } 
       if(type == "script") {
         fileName <- strsplit(URL, "/")[[1]]
         fileName <- rev(fileName)[1]
         write(paste(readLines(textConnection(httr::content(getTMP))), collapse = "\n"), fileName)
       }  
}

# Toolbox functions 
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Ls.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/openwd.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/lib.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/sort.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/predicted_observed_plot.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/residuals_plot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/as.num.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/Table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/renum.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/agg.table.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/r.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/gof.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Date.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/timeStamp.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/dec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/loess.line.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/plot.loess.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/browsePlot.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/gPlot.R")   
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/recode.simple.R")  
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/headTail.R")  
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/factor.f.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/Cor_R_squared_RMSE_MAE_SAD_APE.R")

# FishNIRS funtion
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly.Spec.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/plotly_spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/predicted_observed_plot.R")
# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Mode.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/agreementFigure.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/FCNN_Model.R");  FCNN_model_ver_1 <- FCNN_model
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_ver_5.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")
sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/Predict_NN_Age_Wrapper.R")


# sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/FishNIRS/master/R/CNN_model_2D.R")  # Not working yet

lib(lattice)
lib(dplyr)
# remotes::install_github("r-lib/ragg@bc501c9951c5934afa55da6d36cdf03c2705d99f")
# lib(tidyverse)
lib(recipes)
lib(rsample)
lib(GGally)
lib(skimr)
lib(e1071)
lib(mdatools)
lib(plotly)
lib(FSA)
lib(remotes)

# lib(reticulate) # Need to test if the latest version of reticulate, works or not, with the working versions of tensorflow and keras below 
# lib(tensorflow) # Latest versions of tensorflow and keras don't work as of 1/6/2025. At least not with my current Condo environment below - but I don't beleive that's the issue.
# lib(keras)

if(!any(installed.packages()[, 1] %in% "reticulate")) remotes::install_version('reticulate', '1.32.0', repos = "http://cran.us.r-project.org", upgrade = "never")  
if(!any(installed.packages()[, 1] %in% "tensorflow")) remotes::install_version('tensorflow', '2.13.0', repos = "http://cran.us.r-project.org", upgrade = "never")
if(!any(installed.packages()[, 1] %in% "keras")) remotes::install_version('keras', '2.13.0', repos = "http://cran.us.r-project.org", upgrade = "never")

lib(reticulate) # Now just using lib() as library(), not using the install feature
lib(tensorflow)
lib(keras)

lib(prospectr)
lib(openxlsx)
# install.packages('RcppArmadillo')  # Need to use 4.0 version
lib(RcppArmadillo)


# --- Setup for TensorFlow and Keras ---  # See https://www.youtube.com/watch?app=desktop&v=UbpuXWKQJXs

#  --- Conda TensorFlow environment ---
Conda_TF_Eniv <- ifelse(.Platform$OS.type == 'windows', "C:/m3/envs/tf", "/more_home/h_jwallace/Python/tf_cpu_only/bin")  # Change these paths as needed
Sys.setenv(RETICULATE_PYTHON = Conda_TF_Eniv) 
Sys.getenv("RETICULATE_PYTHON") # If environment variable RETICULATE_PYTHON was not set, use_condaenv("base", required=T) or use_condaenv(Conda_TF_Eniv, required=T) should work



if(.Platform$OS.type != 'windows') {
   # https://github.com/rstudio/tensorflow/issues/412
   config <- tf$compat$v1$ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
   session = tf$compat$v1$Session(config=config)
   tf$compat$v1$keras$backend$set_session(session)
}


# Set pseudo random number seeds for model replication
Seed_Fold <- c(777, 747, 727, 787, 797)[3] 
set.seed(Seed_Fold)

Seed_Model <- c(777, 747, 727, 787, 797)[3]  


# Pick the NN model to use (CNN_model_2D currently not working.)
model_Name <- c('FCNN_model_ver_1', 'CNN_model_ver_5', 'CNN_model_2D')[1]
 
# Disable_GPU <- model_Name == 'FCNN_model_ver_1' # Only using the CPU is faster for the FCNN model but slower for CNN_model_ver_5, at least on Sablefish with data from 2017 and 2019.
Disable_GPU <- FALSE
cat("\nDisable_GPU =", Disable_GPU, "\n\n")

# !!! Disabling the GPU has to come first for it to work. !!! (Grrrr, finally found this on the Web applied to Python and it is true here also!)
# Use < nvidia-smi -l 5 > within e.g. < conda activate C:/m3/envs/tf > inside of Powershell or Command Prompt to see a more correct percent of GPU utilization then with Task Manager.
tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU) 

# Test to see if TensorFlow is working in R and has the correct path
cat("\n\nCheck that Tensorflow has the correct path:", Conda_TF_Eniv, "\n\n"); print(tf_config()); cat("\n")
cat("\n\nCheck NVIDIA SMI:\n\n"); print(system("nvidia-smi")); cat("\n")

a <- tf$Variable(5.56)
b <- tf$Variable(2.7)
print(a + b)

k_clear_session()

}  ###

# --- Initial settings setup ---
{ ### 

if(interactive()) 
      setwd(ifelse(.Platform$OS.type == 'windows', "C:/SIDT/Train_NN_Model", "/more_home/h_jwallace/SIDT/Train_NN_Models"))   # Change path to the Spectra Set's .GlobalEnv as needed
if(!interactive()) options(width = 120) 
 
# Defaults for reading in the spectra sets are in the Read_OPUS_Spectra() function.    
Spectra_Set <- c("PWHT_Acoustic2019", "PWHT_Acoustic_2023", "PWHT_Acoustic_2019_2023", "PWHT_Acoustic_2019_2023_Half_2024", "Sable_2017_2019", "Sable_Combo_2022", "Sable_Combo_2021", "Sable_Combo_2019", "Sable_Combo_2018", # 9
                 "Sable_Combo_2017", "Sable_Combo_2023", "Sable_Combo_Multi_2000", "Sable_Combo_Multi_17_21", "Sable_Combo_Multi_17_22", "Sable_Combo_Multi_17_18_19_22", # 15
                 "Sable_Combo_Multi_17_22_21_200N", "Sable_Combo_2017_21_22_200N", "REYE_Comm_2012_2023", "REYE_Comm_AShop_2008_2023", "CLPR_Combo_2010_2014", "CLPR_Combo_2010__2024")[21] 
                 
Spectra_Path <- "Model_Scans"    # Put new spectra scans in a separate folder and enter the name of the folder below
dir.create('Figures', showWarnings = FALSE)
TMA_Ages = c(TRUE, FALSE)[1]
verbose <- c(TRUE, FALSE)[1]
plot <- c(TRUE, FALSE)[1]

Read_In_OPUS_Spectra <- c(TRUE, FALSE)[2]

Extra_Meta_Path <- list(NULL, "C:/SIDT/Get Otie Info from Data Warehouse/selectSpAgesFramFeb2024.RData", "C:/SIDT/PWHT_Acoustic2019/PWHT_Acoustic2019_Extra_Metadata.RData")[[1]] 

Spectra_Only <- c(TRUE, FALSE)[2] 
if(!Spectra_Only)
    Metadata_Names <- list(c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max', 'Latitude_prop_max'), # 1 The main five metadata variables
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max'), # 2 No lat
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max'),  # 3 No lat nor depth
                       c('structure_weight_dg', 'Weight_prop_max', 'Depth_prop_max', 'Latitude_prop_max'), # 4 All standard metadata but fish length
                       c('structure_weight_dg', 'Length_prop_max', 'Depth_prop_max', 'Latitude_prop_max'), # 5 All standard metadata but fish weight
                       c('structure_weight_dg', 'Weight_prop_max', 'Depth_prop_max'), # 6 No fish length nor lat
                       c('structure_weight_dg', 'Length_prop_max'), # 7
                       c('structure_weight_dg'), # 8 
					   c('structure_weight_dg', 'Sex_F', 'Sex_M', 'Sex_U'), # 9
					   c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Sex_F'), # 10 
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Sex_F', 'Sex_M', 'Sex_U'), # 11 
					   c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Year_2019', 'Year_2023', 'Year_2024'), # 12 
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Sex_F', 'Sex_M', 'Sex_U', 'Year_2019', 'Year_2023', 'Year_2024'), # 13 
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max', 'Latitude_prop_max', 'Sex_F', 'Sex_M'), # 14
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max', 'Latitude_prop_max', 'Sex_F', 'Sex_M', 'Sex_U'), # 15
                       c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max', 'Month_May', 'Month_Jun', 'Month_Jul', 'Month_Aug', 'Month_Sep', 'Month_Oct') # 16
                      )[[9]]
else
    Metadata_Names <- NULL
 
 # !is.null(Extra_Meta_Path) = FALSE and Spectra_Only = FALSE gives otie weight as the only metadata used
Metadata_Only <- c(TRUE, FALSE)[2]


if(Spectra_Set == "Sable_Combo_Multi_2000")
    Model_Spectra_Meta_Path <- "C:/SIDT/Train_NN_Model/Sable_Combo_Rdm_500_2017_18_19_22_Model_Spectra_Meta.Rdata"

else if(Spectra_Set == "Sable_Combo_Multi_17_21")
     Model_Spectra_Meta_Path <- "C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_18_19_21_Model_Spectra_Meta.RData"
     
else if(Spectra_Set == "Sable_Combo_Multi_17_22")
     Model_Spectra_Meta_Path <- "C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_18_19_21_22_Model_Spectra_Meta.RData"     
     
else if(Spectra_Set == "Sable_Combo_Multi_17_18_19_22")
     Model_Spectra_Meta_Path <- "C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_18_19_22_Model_Spectra_Meta.RData"     
       
else if(Spectra_Set == "Sable_Combo_Multi_17_22_21_200N")
     Model_Spectra_Meta_Path <- "C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_18_19_22_21_200N_Model_Spectra_Meta.RData"          
     
else if(Spectra_Set == "Sable_Combo_2017_21_22_200N")
     Model_Spectra_Meta_Path <- "C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_21_22_200N_Model_Spectra_Meta.RData"               
     
# if(Spectra_Set %in% "PWHT_Acoustic2019") 
#     Model_Spectra_Meta_Path <- "C:/SIDT/PWHT_Acoustic2019/PWHT_Acoustic2019_Model_Spectra_Meta_ALL_GOOD_DATA.RData"
   
else if(Spectra_Set %in% c("Sable_2017_2019", "Sable_Combo_2022", "Sable_Combo_2018", "Sable_Combo_2017", "Sable_Combo_2023"))   # "Sable_Combo_2019" & "Sable_Combo_2021", Now will predict themselves - with their own model
    Model_Spectra_Meta_Path <- "C:\\SIDT\\Sable_Combo_2022\\Sable_Combo_2022_Model_Spectra_Meta_ALL_GOOD_DATA.RData"  # Reduced down to 1,553 from 1556 due to missing metadata
  
else if(Spectra_Set %in% "REYE_Comm_2012_2023")   
    #  Model_Spectra_Meta_Path <- "C:\\SIDT\\REYE_Comm_2012_2023\\REYE_Comm_2012_2023_Model_Spectra_Meta_REYE_ALL_GOOD_DATA.RData"  # Year 2011 NOT included
    #   Model_Spectra_Meta_Path <- "C:\\SIDT\\REYE_Comm_2012_2023\\REYE_Comm_2011_2023_Model_Spectra_Meta_Extra_ALL_GOOD_DATA.RData"  # Year 2011 included
       Model_Spectra_Meta_Path <- "C:\\SIDT\\REYE_Comm_2012_2023\\REYE_Comm_2011_2023_Model_Spectra_Meta_ALL_GOOD_DATA.RData"  # Year 2011& 2022 included, no Extra. No TMA in 2022 & 2023
    
else if(Spectra_Set %in% "REYE_Comm_AShop_2008_2023")   
        Model_Spectra_Meta_Path <- "C:\\SIDT\\REYE_Comm_AShop_2008_2023\\REYE_Comm_AShop_2008_2023_Model_Spectra_Meta_ALL_GOOD_DATA.RData" 
        
else if(Spectra_Set %in% "PWHT_Acoustic_2019_2023")   
        Model_Spectra_Meta_Path <- "C:\\SIDT\\PWHT_Acoustic_2019_2023\\PWHT_Acoustic_2019_2023_Model_Spectra_Str_Wt_ALL_GOOD_DATA.RData"         
        
else if(Spectra_Set %in% "PWHT_Acoustic_2023") 
        Model_Spectra_Meta_Path <- "C:\\SIDT\\PWHT_Acoustic_2023\\PWHT_Acoustic_2023_Model_Spectra_Str_Wt_ALL_GOOD_DATA.RData"             

else if(Spectra_Set %in% "PWHT_Acoustic_2019_2023_Half_2024")   # 
        # Model_Spectra_Meta_Path <- "C:\\SIDT\\PWHT_Acoustic_2019_2023_Half_2024\\PWHT_A_2019_2023_2024_Half_Model_Spectra_Str_Wt_ALL_GOOD_DATA.RData"     
        Model_Spectra_Meta_Path <- "C:\\SIDT\\PWHT_Acoustic_2019_2023_Half_2024\\PWHT_Acoustic_2019_2023_2024_Half_Model_Spectra_ALL_GOOD_DATA.RData"             

else if(Spectra_Set %in% "CLPR_Combo_2010_2014")   		
		Model_Spectra_Meta_Path <- "C:\\SIDT\\CLPR_Combo_2010_2014\\CLPR_SWFSC_2010_2014_Model_Spectra_Meta_ALL_GOOD_DATA.RData"
        
else if(Spectra_Set %in% "CLPR_Combo_2010__2024")   		
		Model_Spectra_Meta_Path <- "C:\\SIDT\\CLPR_Combo_2010__2024\\CLPR_SWFSC_2010__2024_Model_Spectra_Meta_ALL_GOOD_DATA.RData"        
        
else  {
   # Below works for standard Spectra_Set setups, like "PWHT_Acoustic2019", "Sable_Combo_2019", and "Sable_Combo_2021"
   # print(Model_Spectra_Meta_Path <- paste0('C:/SIDT/', Spectra_Set, '/', Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData'))
   Year <- get.subs(Spectra_Set, sep = "_")[3] 
   print(Model_Spectra_Meta_Path <- paste0('C:/SIDT/Sable_Combo_', Year, '/', Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData'))
   print(Meta_Path <- paste0('C:/SIDT/Sable_Combo_', Year, '/', Spectra_Set, '_NIRS_Scanning_Session_Report_For_NWFSC.xlsx'))  # Meta_Path only used if Read_In_OPUS_Spectra is TRUE
}
  
 
# Default number of new spectra to be plotted in spectra figures. (The plot within Read_OPUS_Spectra() is given a different default below). 
# All spectra in the Spectra_Path folder will be assigned an age regardless of the number plotted in the figure.
Max_N_Spectra <- list(50, 200, 300, 'All')[[3]] 
Rdm_Reps_Main <- c(3, 20, 40, 60)[2]
Folds_Num <- c(5, 10)[2] # i loop    # How many folds work best for metadata only models was checked. Trying 2 for single sex models
Iter_Num <- 8  # Iter while() loop
Num_Oties_Model <- c(NA, 500, 750, 1000)[1] # NA => use all available
activation_function <- c('relu', 'elu', 'selu')[1]


print(getwd())
print(Spectra_Set)
cat("\nSpectra_Only =", Spectra_Only)
cat("\nMetadata_Only =", Metadata_Only) 
cat("\nRdm_Reps_Main =", Rdm_Reps_Main)
cat("\nFolds_Num =", Folds_Num)

if(!Spectra_Only)
   cat("\nMetadata_Names =", paste0(Metadata_Names, collpse = c(rep(",", length(Metadata_Names) -1), "")), "\n\n")


# Load the data if needed - not needed for Metadata only model run 
if(file.exists(paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData')) & !Metadata_Only) {

    base::load(paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData'))
	base::load(paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData'))
	    
    # base::load(paste0(Spectra_Set, '_SaveOutOties_Seed_727.RData')); print(length(SaveOutOties))
    # Model_Spectra_Meta <- Model_Spectra_Meta[-SaveOutOties, ]; print(dim(Model_Spectra_Meta))
    
    # print(Model_Spectra_Meta[1:3, c(1, (grep('project', names(Model_Spectra_Meta))):ncol(Model_Spectra_Meta))])
    
    TMA_Vector <- Model_Spectra_Meta$TMA
    fileNames <- Model_Spectra_Meta$filenames
    
    if(verbose) {
	    cat("\n\nModel_Spectra_Meta:\n")
	    headTail(Model_Spectra_Meta, 2, 2, 3, 70)
		
		cat("\n\nModel_Spectra.sg.iPLS:\n")
	    headTail(Model_Spectra.sg.iPLS, 2, 2, 3, 5)
		
        cat("\n\nLength of TMA_Vector =", length(TMA_Vector), "\n\n")
	    cat("\n\nLength of fileNames =", length(fileNames), "\n\n")
    }
    
    if(!is.null(Extra_Meta_Path) | (is.null(Extra_Meta_Path) & !Read_In_OPUS_Spectra)) {
        Model_Spectra.sg.iPLS_Meta <- cbind(Model_Spectra.sg.iPLS, Model_Spectra_Meta[, Metadata_Names, drop = FALSE]) 
		if(verbose) {
		   cat("\n\nModel_Spectra.sg.iPLS_Meta:\n")
		   headTail(Model_Spectra.sg.iPLS_Meta, 2, 2, 3, 45)
		}   
	}	   
}


#  !!!!!! NOTE: Around line 296 there is code to edit/add to specific datasets when < !is.null(Extra_Meta_Path) or (is.null(Extra_Meta_Path) & !Read_In_OPUS_Spectra) > !!!!!!

Sys.sleep(3)
}

# --- If Model_Spectra.sg.iPLS is missing for the current spectra set, create it now, unless a metadata only model is being run ---
{ ###
if(!file.exists(paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData')) & !Metadata_Only) { 

    if(Read_In_OPUS_Spectra)
        # Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set, Spectra_Path = Spectra_Path, Max_N_Spectra = Max_N_Spectra, Meta_Path = paste0(Spectra_Set, "_NIRS_Scanning_Session_Report_For_NWFSC.xlsx"),
        #                                    Extra_Meta_Path = Extra_Meta_Path, verbose = verbose, plot = plot, htmlPlotFolder = paste0('Figures/', Spectra_Set, '_Spectra_Sample_of_300'))
        
        Model_Spectra_Meta <- Read_OPUS_Spectra(Spectra_Set, Spectra_Path = Spectra_Path, Max_N_Spectra = Max_N_Spectra, Meta_Path = Meta_Path, TMA_Ages = TMA_Ages,
                                            Extra_Meta_Path = Extra_Meta_Path, verbose = verbose, plot = plot, htmlPlotFolder = paste0('Figures/', Spectra_Set, '_Spectra_Sample_of_300'))
     
    # ----- To download a function for debuging, follow the example below -----     
    # sourceFunctionURL("https://raw.githubusercontent.com/John-R-Wallace-NOAA/JRWToolBox/master/R/GitHub_File_Download.R")  
    # GitHub_File_Download("John-R-Wallace-NOAA/FishNIRS/master/R/Read_OPUS_Spectra.R")     
    # For testing Read_OPUS_Spectra(): Meta_Path <- Spectra_Path <- NULL; Extra_Meta_Path <- NULL; spectraInterp = 'stats_splinefun_lowess'; excelSheet <- 3; opusReader = 'philippbaumann_opusreader2'; (htmlPlotFolder <- paste0('Figures/', Spectra_Set, '_Spectra_Sample_of_300'))
   
    else {
        load(Model_Spectra_Meta_Path)
		# Debug: apply(Model_Spectra_Meta[, (grep('project', names(Model_Spectra_Meta))):ncol(Model_Spectra_Meta)], 2, function(x) sum(is.na(x)))
		if(TMA_Ages) 
		   Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ]
    }
    headTail(Model_Spectra_Meta, 2, 2, 3, 46)
         
    # Single sex model    
    # Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$Sex %in% 'F', ]
    # headTail(Model_Spectra_Meta, 2, 2, 3, 46)
    # print(Table(Model_Spectra_Meta$Sex))
    
       
    # -- Early save in case there are problems below --
    # save(Model_Spectra_Meta, file = paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData')) 
    # load(file = paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData'))
    
    
    # Print out metadata with missing values - some oties may have been removed by Read_OPUS_Spectra() function if there were majors issues (e.g. too much chipping or tissue).
    metadata <- Model_Spectra_Meta[, -(2:((1:ncol(Model_Spectra_Meta))[names(Model_Spectra_Meta) %in% 'project'] - 1))]  # Includes TMA
    headTail(metadata, 2)
    
    
    #  *** Code to edit/add for specific datasets when < !is.null(Extra_Meta_Path) or (is.null(Extra_Meta_Path) & !Read_In_OPUS_Spectra) > ***
    if(!is.null(Extra_Meta_Path) | (is.null(Extra_Meta_Path) & !Read_In_OPUS_Spectra)) {
    
       if(Spectra_Set %in% "PWHT_Acoustic2019")   {
       
           # Look at missing data
           print(renum(metadata[(is.na(metadata$TMA) | is.na(metadata$structure_weight_dg) | is.na(metadata$Length_prop_max) | is.na(metadata$Weight_prop_max)), 
                                            c('filenames', 'TMA', 'structure_weight_g', 'Length_cm', 'Weight_kg', 'Sex')]))
           headTail(Model_Spectra_Meta, 2, 2, 3, 45)
           
           # Remove missing values from Model_Spectra_Meta for the currently used metadata and TMA
           Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max) | is.na(Model_Spectra_Meta$Weight_prop_max)), ]
           headTail(Model_Spectra_Meta, 2, 2, 3, 45)
       
       
       } else if(Spectra_Set %in% "REYE_Comm_2012_2023")   {
       
           # Look at missing data
           # headTail(metadata[is.na(metadata$TMA) | is.na(metadata$structure_weight_dg), ], 2)
           headTail(metadata[is.na(metadata$TMA) | is.na(metadata$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max), ], 2)
           headTail(Model_Spectra_Meta, 2, 2, 3, 65)
           
           # Remove missing values from Model_Spectra_Meta for the currently used metadata and TMA
            if(!Spectra_Only)
               Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg)), ]
          
           headTail(Model_Spectra_Meta, 2, 2, 3, 45)
           
           
        } else if(Spectra_Set %in% "REYE_Comm_AShop_2008_2023")   {
       
           # Look at missing data
           # headTail(metadata[is.na(metadata$TMA) | is.na(metadata$structure_weight_dg), ], 2)
           headTail(metadata[is.na(metadata$TMA) | is.na(metadata$structure_weight_dg), ], 2)
           headTail(Model_Spectra_Meta, 2, 2, 3, 65)
           
           # Remove missing values from Model_Spectra_Meta for the currently used metadata and TMA
            if(!Spectra_Only)
               Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg)), ]
          
           headTail(Model_Spectra_Meta, 2, 2, 3, 45)   
           
        } else if(Spectra_Set %in% c("PWHT_Acoustic_2023", "PWHT_Acoustic_2019_2023", "PWHT_Acoustic_2019_2023_Half_2024", "CLPR_Combo_2010_2014", "CLPR_Combo_2010__2024"))   {
           # Data checked for missing data in: C:\SIDT\PWHT_Acoustic_2023\Read in Scans.R where besides 2023 data being read in, 2019 and 2023 years were combined
           headTail(Model_Spectra_Meta, 2, 2, 3, 45)      
       
       } else {
    
            # Look at missing data
            # print(renum(metadata[(is.na(metadata$TMA) | is.na(metadata$structure_weight_dg) | is.na(metadata$Length_prop_max) | is.na(metadata$Weight_prop_max) | is.na(metadata$Depth_prop_max) | is.na(metadata$Latitude_prop_max)), 
            #                                 c('filenames', 'TMA', 'structure_weight_g', 'Length_cm', 'Weight_kg', 'Sex', 'Depth_m', 'Month', 'Days_into_Year', 'Latitude_dd')]))
            print(renum(metadata[(is.na(metadata$TMA) | is.na(metadata$structure_weight_dg) | is.na(metadata$Length_prop_max) | is.na(metadata$Weight_prop_max) | is.na(metadata$Depth_prop_max)), 
                                            c('filenames', 'TMA', 'structure_weight_g', 'Length_cm', 'Weight_kg', 'Depth_m')]))                                            
            headTail(Model_Spectra_Meta, 2, 2, 3, 45)
           
            #  Remove missing values from Model_Spectra_Meta for the currently used metadata and TMA
            # Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max) 
            #                       | is.na(Model_Spectra_Meta$Weight_prop_max) | is.na(Model_Spectra_Meta$Depth_prop_max) | is.na(Model_Spectra_Meta$Latitude_prop_max)), ]
            # Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max) 
            #                       | is.na(Model_Spectra_Meta$Weight_prop_max) | is.na(Model_Spectra_Meta$Depth_prop_max)), ]    
            # Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max) 
            #                       | is.na(Model_Spectra_Meta$Weight_prop_max) | is.na(Model_Spectra_Meta$Depth_prop_max) | is.na(Model_Spectra_Meta$Latitude_prop_max) 
            #                       | is.na(Model_Spectra_Meta$Sex_F) | is.na(Model_Spectra_Meta$Sex_M)), ]   
            Model_Spectra_Meta <- Model_Spectra_Meta[!(is.na(Model_Spectra_Meta$TMA) | is.na(Model_Spectra_Meta$structure_weight_dg) | is.na(Model_Spectra_Meta$Length_prop_max) 
                                  | is.na(Model_Spectra_Meta$Weight_prop_max) | is.na(Model_Spectra_Meta$Depth_prop_max) | is.na(Model_Spectra_Meta$Latitude_prop_max) 
                                  | is.na(Model_Spectra_Meta$Sex_F) | is.na(Model_Spectra_Meta$Sex_M) | is.na(Model_Spectra_Meta$Sex_U)), ]                                                                         
            headTail(Model_Spectra_Meta, 2, 2, 3, 45)
        }
    }
    
    # Create the spectra only file if Spectra_Only = TRUE
    if(Spectra_Only) {
        Model_Spectra_Meta <- Model_Spectra_Meta[!is.na(Model_Spectra_Meta$TMA), ]
        headTail(Model_Spectra_Meta, 2, 2, 3, 45)
    }    
    
    # Model_Spectra <- Model_Spectra_Meta[, 2:((1:ncol(Model_Spectra_Meta))[names(Model_Spectra_Meta) %in% 'specimen_id'] - 1)]
    Model_Spectra <- Model_Spectra_Meta[, 2:((1:ncol(Model_Spectra_Meta))[names(Model_Spectra_Meta) %in% 'project'] - 1)]
    cat("\nModel Spectra only for Savitzky-Golay smoothing and iPLS selection:\n\n")
    headTail(Model_Spectra)
   
    # Double check for missing data
    print(dim(Model_Spectra))
    print(dim(na.omit(Model_Spectra)))
    print(Metadata_Names)
	
	if(nrow(Model_Spectra) != nrow(na.omit(Model_Spectra)))
       stop("\n\n!! Not all missing data was removed !!\n\n")
	
    
    TMA_Vector <- Model_Spectra_Meta$TMA; print(length(TMA_Vector)); print(length(TMA_Vector[!is.na(TMA_Vector)]))
   
    save(Model_Spectra_Meta, file = paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData')) 
    # load(file = paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData'))
   
   
    # Savitzky-Golay smoothing    
    { ##
    
    ###################################################################################################################
    ### Perform Savitzky-Golay 1st derivative with 17 point window smoothing 2nd order polynomial fit and visualize ###
    ### Intro: http://127.0.0.1:30354/library/prospectr/doc/prospectr.html
    ###################################################################################################################
    ### NOTE ### If there are multiple years of data, all subsequent transformations should be applied to the whole data set, then re-subset  
       
    Model_Spectra.sg <- data.frame(prospectr::savitzkyGolay(Model_Spectra, m = 1, p = 2, w = 15))
    
    
    ####################################################
    ###  iPLS algorithm in mdatools  ### 
    ####################################################
    
    # Maximum number of components to calculate.
    nComp <- c(10, 15)[2]
    
    cat("\nStarting iPLS on Savitzky-Golay smoothed data to find informative wavebands for the NN model\n\n")
    Model_Spectra.iPLS.F <- mdatools::ipls(Model_Spectra.sg, TMA_Vector, glob.ncomp = nComp, center = TRUE, scale = TRUE, cv = 100,
                      int.ncomp = nComp, int.num = nComp, ncomp.selcrit = "min", method = "forward", silent = FALSE)
    
    summary(Model_Spectra.iPLS.F) 
    
    # plot the newly selected spectra regions 
    browsePlot('plot(Model_Spectra.iPLS.F)', file = "Figures/iPLS Results, RMSECV vs Variables.png")
    
    Model_Spectra.iPLS.F$int.selected
    sort(Model_Spectra.iPLS.F$var.selected)
    
    # dev.new()  - With a main title
    # plot(Model_Spectra.iPLS.F, main = NULL)          
    
    # plot predictions before and after selection
    browsePlot('
      par(mfrow = c(2, 1))
      mdatools::plotPredictions(Model_Spectra.iPLS.F$gm) # gm = global PLS model with all variables included
      mdatools::plotPredictions(Model_Spectra.iPLS.F$om) # om = optimized PLS model with selected variables
    ', file = "Figures/iPLS Predictions.png")
    
    browsePlot('mdatools::plotRMSE(Model_Spectra.iPLS.F)', file = "Figures/iPLS RMSE Development.png")
    
    # RMSE before and after selection
    
    # Find the ylim outside limits and apply to both figures 
     par(mfrow = c(2, 1))
     mdatools::plotRMSE(Model_Spectra.iPLS.F$gm)
     yMin <- par()$usr[3]
     yMax <- par()$usr[4]
     
     mdatools::plotRMSE(Model_Spectra.iPLS.F$om)
     yMin <- min(par()$usr[3], yMin)
     yMax <- max(par()$usr[4], yMax)
    
     dev.off()
    
    # Use the same ylim for both plots
    browsePlot('
      par(mfrow = c(2, 1))
      mdatools::plotRMSE(Model_Spectra.iPLS.F$gm, ylim = c(yMin, yMax))
      mdatools::plotRMSE(Model_Spectra.iPLS.F$om, ylim = c(yMin, yMax))
    ', file = "Figures/iPLS RMSE vs Components, gm_top and om_bottom.png")
	
	cat("\n\n")
    
    # Select iPLS vars and add metadata wanted
    # (p <- length(Model_Spectra.iPLS.F$var.selected)) # 380 freq selected out of a total of 1140
    
    # Model_Spectra.sg.iPLS <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], length_prop_max = Model_Spectra_Meta$length_cm/max(Model_Spectra_Meta$length_cm), 
    #                                        structure_weight_dg = 10 * Model_Spectra_Meta$structure_weight_g) # dg = decigram
    
    # Order of columns in Model_Spectra.sg.iPLS needs to be kept consistent for the NN model
    # Commented out the random selection code below so that the number oties is not reduced
    
    # ---- Scans and metadata run ----
    
    if(!is.null(Extra_Meta_Path) | (is.null(Extra_Meta_Path) & !Read_In_OPUS_Spectra)) 
       	Model_Spectra.sg.iPLS_Meta <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], Model_Spectra_Meta[, Metadata_Names, drop = FALSE]) 
    
	
    #  if(!is.null(Extra_Meta_Path)) {
    #      if(Spectra_Set %in% "PWHT_Acoustic2019")    # No depth for Hake
    #          Model_Spectra.sg.iPLS <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], Model_Spectra_Meta[, c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max')]) # dg = decigram
    #          
    #      else if(Spectra_Set %in% "REYE_Comm_2012_2023")       
    #          # Model_Spectra.sg.iPLS <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], Model_Spectra_Meta[, 'structure_weight_dg']) # dg = decigram
    #         
    #          
    #      else
    #          Model_Spectra.sg.iPLS <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], Model_Spectra_Meta[, c('structure_weight_dg', 'Length_prop_max', 'Weight_prop_max', 'Depth_prop_max')]) # dg = decigram
    #  }
     
    # if(is.null(Extra_Meta_Path) & !Spectra_Only) # 2nd approach to structure_weight_dg as the only metadata - since it is not from Extra_Meta_Path
    #    Model_Spectra.sg.iPLS <- data.frame(Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)], Model_Spectra_Meta[, 'structure_weight_dg', drop = FALSE]) # dg = decigram
      
      
    if(Spectra_Only) {
        TF <- rep(FALSE, nrow(Model_Spectra.sg.iPLS_Meta))
        for(i in Metadata_Names)
            TF <- TF | is.na(Model_Spectra.sg.iPLS_Meta[, i])
        
        Model_Spectra.sg.iPLS_Meta <- renum(Model_Spectra.sg.iPLS_Meta[!TF, ])
		headTail(Model_Spectra.sg.iPLS_Meta)
    }
        
    Model_Spectra.sg.iPLS <- Model_Spectra.sg[, sort(Model_Spectra.iPLS.F$var.selected)] 
    
    if(verbose) {
       cat("\n\nModel_Spectra.sg.iPLS:\n\n")
       headTail(Model_Spectra.sg.iPLS)
    }
    
    save(Model_Spectra.sg.iPLS, file = paste0(Spectra_Set, '_Model_Spectra.sg.iPLS.RData'))  #  !!!! Model_Spectra.sg.iPLS_Meta recreated from Model_Spectra.sg.iPLS for loop at bottom of 'Initial settings setup' above !!!!
    
} ##
 
}
 
} ###

# --- NN Model ---
{ ###

# ----- Remove both metadata columns for testing  -----
# Model_Spectra.sg.iPLS_Meta$length_prop_max <- Model_Spectra.sg.iPLS_Meta$structure_weight_dg <- NULL

# ----- Leave only the fish length for testing  -----
# Model_Spectra.sg.iPLS_Meta$structure_weight_dg <- NULL

# ----- Leave only the otie weight for testing  -----
# Model_Spectra.sg.iPLS_Meta$length_prop_max <- NULL


#   # --------- Special code to test 'Month_Scaled', 'Depth_m', 'Sex', 'Weight_kg', 'Days_into_Year', and reduced model size in the NN Model with the same scans in Model_Spectra.sg.iPLS_Meta ------------------------------
#   base::load("C:/SIDT/Get Otie Info from Data Warehouse/selectSpAgesFramFeb2025.RData")  # From NWFSC Data Warehouse
#   
#   
#   #     ===>                                                                                                                                                                                              Fish length and Otie Wgt: # SAD: 2050; RMSE: 2.7280
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", c('Month_Scaled', 'Depth_m', 'Sex', 'Weight_kg')) # Very poor results
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", c('Month_Scaled', 'Weight_kg', 'Depth_m'))  # SAD: 2029; RMSE: 2.7678
#   Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", c('Weight_kg', 'Depth_m')) # SAD: 2002; RMSE: 2.6742
#   #    Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, filenames = Model_Spectra_Meta$filenames, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", c('Weight_kg', 'Depth_m', 'Length_cm', 'Age')) # Stratified Random
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", 'Weight_kg') # SAD: 2088; RMSE: 2.7389
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", 'Depth_m')  # SAD: 2042; RMSE: 2.7404
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", 'Month_Scaled')  # SAD: ????; RMSE: ????
#   # Model_Spectra.sg.iPLS_Meta <- match.f(data.frame(Model_Spectra.sg.iPLS_Meta, specimen_id = as.character(Model_Spectra_Meta$specimen_id)), selectSpAgesFramFeb2024, "specimen_id", "AgeStr_id", c('Weight_kg', 'Depth_m', 'Days_into_Year')) # SAD: 2090; RMSE: 2.8047

#   Model_Spectra.sg.iPLS_Meta$specimen_id <- NULL  # specimen_id only needed for the matching above


# Use the best metadata found with the above testing for the production code is saved in the prior section


#  ==== Metadata only model run - no need to load Model_Spectra.sg.iPLS_Meta above, as it is created below ====
     # Not using the random selection code below so that the number oties is not reduced
if(Metadata_Only) {
     
    if(Spectra_Set == "Sable_Combo_Multi_17_21")
        load("C:/SIDT/Sablefish Combo Multi Year/Sable_Combo_2017_18_19_21_Model_Spectra_Meta.RData")
       
    if(Spectra_Set %in% c("Sable_2017_2019", "Sable_Combo_2022", "Sable_Combo_2021", "Sable_Combo_2019", "Sable_Combo_2018", "Sable_Combo_2017", "Sable_Combo_2023"))   
        base::load("C:/SIDT/Predict_NN_Ages/Sable_Combo_2022_Model_Spectra_Meta_ALL_GOOD_DATA_1556N.RData")
     
    if(Spectra_Set %in% "PWHT_Acoustic2019")   # No Depth
        load("C:/SIDT/PWHT_Acoustic2019/PWHT_Acoustic2019_Model_Spectra_Meta_ALL_GOOD_DATA.RData")  
       
    TF <- is.na(Model_Spectra_Meta$TMA)
    for(i in Metadata_Names)
        TF <- TF | is.na(Model_Spectra_Meta[, i])
    
    Model_Spectra_Meta <- renum(Model_Spectra_Meta[!TF, ])
    headTail(Model_Spectra_Meta, 2, 2, 3, 70)
    
    cat("\n\nModel_Spectra.sg.iPLS_Meta (Columns below are those in the NN model.)\n\n")    
    Model_Spectra.sg.iPLS_Meta <- Model_Spectra_Meta[, Metadata_Names] # dg = decigram   # The Model_Spectra.sg.iPLS_Meta name is used here - but there is only metadata
    headTail(Model_Spectra.sg.iPLS_Meta)
    
    # - Single sex model -
    # Model_Spectra_Meta <- Model_Spectra_Meta[Model_Spectra_Meta$Sex %in% 'M', ]
    # dim(Model_Spectra_Meta)
    
    save(Model_Spectra_Meta, file = paste0(Spectra_Set, '_Model_Spectra_Meta_ALL_GOOD_DATA.RData')) 
            
    TMA_Vector <- Model_Spectra_Meta$TMA; print(length(TMA_Vector))
    fileNames <- Model_Spectra_Meta$filenames; print(length(fileNames))
}

# Check one more time for missing data
print(dim(Model_Spectra.sg.iPLS_Meta))
print(dim(na.omit(Model_Spectra.sg.iPLS_Meta)))

if(nrow(Model_Spectra.sg.iPLS_Meta) != nrow(na.omit(Model_Spectra.sg.iPLS_Meta)))
   stop("\n\n!! Not all missing data was removed !!\n\n")
  

################ OLD ###############
# These 3 oties in the metadata were missing from the Data WareHouse for Sablefish 2022: AgeStr_id %in% 102133144:102133146  ????????????????

#  Model_Spectra.sg.iPLS_Meta$Month_Scaled[is.na(Model_Spectra.sg.iPLS_Meta$Month_Scaled)] <- 6:8/12
#  Model_Spectra.sg.iPLS_Meta$Depth_m[is.na(Model_Spectra.sg.iPLS_Meta$Depth_m)] <- mean(Model_Spectra.sg.iPLS_Meta$Depth_m, na.rm = TRUE)
#  Model_Spectra.sg.iPLS_Meta$Weight_kg[is.na(Model_Spectra.sg.iPLS_Meta$Weight_kg)] <- mean(Model_Spectra.sg.iPLS_Meta$Weight_kg, na.rm = TRUE) 
#  Model_Spectra.sg.iPLS_Meta$Sex[is.na(Model_Spectra.sg.iPLS_Meta$Sex)] <- c('M','F', 'M')

#  print(dim(na.omit(Model_Spectra.sg.iPLS_Meta)))
#  print(headTail(Model_Spectra.sg.iPLS_Meta, 3, 2, 3, 5))
################################

#  # --------- Special code cont. -----------
#  if(!is.null(Model_Spectra.sg.iPLS_Meta$Sex)) {
#     Model_Spectra.sg.iPLS_Meta$Sex_prop_max <- as.numeric(recode.simple(Model_Spectra.sg.iPLS_Meta$Sex, data.frame(c('F','M', 'U'), 0:2)))/2  # ** All variables have to be numeric ** 
#     Model_Spectra.sg.iPLS_Meta$Sex <- NULL
#  }   
#  
#  if(!is.null(Model_Spectra.sg.iPLS_Meta$Depth_m)) {
#     Model_Spectra.sg.iPLS_Meta$Depth_prop_max <- (Model_Spectra.sg.iPLS_Meta$Depth_m - min(Model_Spectra.sg.iPLS_Meta$Depth_m))/(max(Model_Spectra.sg.iPLS_Meta$Depth_m) - min(Model_Spectra.sg.iPLS_Meta$Depth_m))
#     Model_Spectra.sg.iPLS_Meta$Depth_m <- NULL
#  }   
#  
#  if(!is.null(Model_Spectra.sg.iPLS_Meta$Weight_kg)) {
#     Model_Spectra.sg.iPLS_Meta$Weight_prop_max <- (Model_Spectra.sg.iPLS_Meta$Weight_kg - min(Model_Spectra.sg.iPLS_Meta$Weight_kg))/(max(Model_Spectra.sg.iPLS_Meta$Weight_kg) - min(Model_Spectra.sg.iPLS_Meta$Weight_kg))
#     Model_Spectra.sg.iPLS_Meta$Weight_kg <- NULL
#  }   
#  
#  if(!is.null(Model_Spectra.sg.iPLS_Meta$Days_into_Year)) {
#     Model_Spectra.sg.iPLS_Meta$Days_into_Year_prop_max <- (Model_Spectra.sg.iPLS_Meta$Days_into_Year - min(Model_Spectra.sg.iPLS_Meta$Days_into_Year))/(max(Model_Spectra.sg.iPLS_Meta$Days_into_Year) - min(Model_Spectra.sg.iPLS_Meta$Days_into_Year))
#      Model_Spectra.sg.iPLS_Meta$Days_into_Year <- NULL
#  }   

#  headTail(Model_Spectra.sg.iPLS_Meta, 3, 2, 3, 5)

# = = = = = = = = = = = = = = = = = Intial setup = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
   
# Special seed setting for model testing - not put in intial settings above for now for consistency with past runs
Seed_Fold <- 787 # Seed_Fold = 787 for Run 3.  Seed 747 used for Fish_Len_Otie_Wgt_Run_2 .  Using a different seed starting here, to test main run of Sable_2022 with fish length and otie weight (and other metadata runs)
                 #      Seed_Fold = 727 used in the code above and for previous runs (Fish_Len_Otie_Wgt Run 1) of Sable_2022 before 28 Dec 2023
  
# ------- Reduce model size to see the change in prediction ability -------

# # --- Random selection of a reduced number of oties ---
    if(!Metadata_Only & !is.na(Num_Oties_Model)) {
        set.seed(Seed_Fold) 
        Rdm_Oties <- sample(1:nrow(Model_Spectra.sg.iPLS_Meta), Num_Oties_Model)  
        
        Model_Spectra.sg.iPLS_Meta <- Model_Spectra.sg.iPLS_Meta[Rdm_Oties, ]
        print(dim(Model_Spectra.sg.iPLS_Meta))
        headTail(Model_Spectra.sg.iPLS_Meta, 3, 2, 3, 5)
    
        Model_Spectra_Meta <- Model_Spectra_Meta[Rdm_Oties, ]
        print(Model_Spectra_Meta[1:3, c(1, (grep('project', names(Model_Spectra_Meta))):ncol(Model_Spectra_Meta))])
        
        TMA_Vector <- Model_Spectra_Meta$TMA; print(length(TMA_Vector))
        fileNames <- Model_Spectra_Meta$filenames; print(length(fileNames))
    }
 

# --- Stratified random selection of a reduced number of oties ---
    #   print(Bin_Num <- Table(factor.f(Model_Spectra.sg.iPLS_Meta$Length_cm, breaks = c(0, 25, 45, 65, Inf))))
    #   print(Mid_Split <- (500 - Bin_Num[1] - Bin_Num[4])/2)
    #   
    #   Model_Spectra.sg.iPLS_Meta <- rbind(Model_Spectra.sg.iPLS_Meta[Model_Spectra.sg.iPLS_Meta$Length_cm <= 25 | Model_Spectra.sg.iPLS_Meta$Length_cm > 65, ], 
    #                                  Model_Spectra.sg.iPLS_Meta[Model_Spectra.sg.iPLS_Meta$Length_cm > 25 & Model_Spectra.sg.iPLS_Meta$Length_cm <= 45, ][sample(1:Bin_Num[2], floor(Mid_Split)), ], 
    #                                  Model_Spectra.sg.iPLS_Meta[Model_Spectra.sg.iPLS_Meta$Length_cm > 45 & Model_Spectra.sg.iPLS_Meta$Length_cm <= 65, ][sample(1:Bin_Num[2], ceiling(Mid_Split)), ])
    #   print(dim(Model_Spectra.sg.iPLS_Meta))
    #   
    #   TMA_Vector <- Model_Spectra.sg.iPLS_Meta$Age
    #   fileNames <- Model_Spectra.sg.iPLS_Meta$filenames
    #   
    #   Model_Spectra.sg.iPLS_Meta$Length_cm <- NULL
    #   Model_Spectra.sg.iPLS_Meta$Age <- NULL
    #   Model_Spectra.sg.iPLS_Meta$filenames <- NULL
    #   
    #   print(headTail(Model_Spectra.sg.iPLS_Meta, 3, 2, 3, 5))

# One Random Model step for each call to the R sub-process (Calling Rgui from Rgui)
if(!file.exists('Rdm_reps_Iter_Flag.RData')) {

if(!Spectra_Only)   
   for(i in Metadata_Names)
       browsePlot('plot(Model_Spectra_Meta$TMA, Model_Spectra_Meta[,i], xlab = "TMA", ylab = i, main = paste0("Raw Metadata ", i, " vs TMA"))', file = paste0("Figures/Raw Metadata ", i, " vs TMA.png"))

   # Create the local initial file '.Rprofile' which Rgui.exe will run when started
   # The listed default packages do not immediately load.  They are loaded by the time a prompt is given,
   #       but that doesn't happen here, so these packages are loaded explictly.
   shell("echo library(stats); library(graphics); library(grDevices); library(utils); library(datasets) > .Rprofile")
   shell("echo source('iPLS, NN Model Batch Self Call Loop.R') >> .Rprofile")   # Self calling this file
   shell("echo quit('no',,FALSE) >> .Rprofile")

   # for(Rdm_reps_Iter in seq(1, 19, by = 2)) {  # Two loops at a time
   # If restarting the loops, replace '1' below with the next random model wanted, so if x model is already finished, replace 1 with x + 1. !!! Don't forget to reset back to 1 when done. !!!
   for(Rdm_reps_Iter in 1:Rdm_Reps_Main) {    
      cat("\n\nRdm_reps_Iter =", Rdm_reps_Iter, "\n\n")
      save(Rdm_reps_Iter, file = 'C:/SIDT/Train_NN_Model/Rdm_reps_Iter_Flag.RData')    
      
      shell("echo cd C:/SIDT/Train_NN_Model > run.bat")
      shell("echo C:/R/R/bin/x64/Rgui.exe --no-save --no-restore --no-site-file --no-environ >> run.bat")
      shell("echo exit >> run.bat")
      shell("start /wait run.bat")
      shell("del run.bat")
   }
   
   
   
   
   on.exit(unlink(c('.Rprofile', 'Rdm_reps_Iter_Flag.RData')))
   load(paste0("C:/SIDT/Train_NN_Model/Rdm_model_", Rdm_Reps_Main, ".RData"))
   Wrap_Up_Flag <- ""
   
} else {

   # --- Setup graphic windows ---
   graphics.off()  
   dev.new(width = 14, height = 6) #2
   dev.new() # 3
   dev.new(width = 11, height = 8) # 4
   dev.new(width = 11, height = 8) # 5
   dev.new(width = 10, height = 10) # 6
   dev.new(width = 10, height = 10) # 7
   
   # = = = = = = Pick number of random reps (Rdm_reps), number of folds (Folds_Num), and iteration number (Iter_Num), then run the NN code and expect long run times. = = = = = = = = =
       
   load('Rdm_reps_Iter_Flag.RData')    
   cat("\n\nRdm_reps_Iter =", Rdm_reps_Iter, "\n\n")
   
   # Rdm_reps <- Rdm_reps_Iter + 1  # j loop - two loops at a time
   Rdm_reps <- Rdm_reps_Iter  # j loop - one loop at a time
   
   # (Rdm_reps <- ifelse(model_Name == 'FCNN_model_ver_1', 20, 10))
   Seed_Main <- Seed_Fold + 20 # Seed_Fold 747 used for Fish_Len_Otie_Wgt_Run_2. Seed_Main <- 707 used for previous runs of Sable_2022 before 28 Dec 2023  # Reducing the number of seeds will be considered later
   set.seed(Seed_Main) 
   Seed_reps <- sample(1e7, Rdm_reps)  # Long vector of Seed_reps is created using Seed_Main so that Seed_Data is always correct regardless of restarts
   
   # Start fresh or continue by loading a file with model iterations already finished (see the commented line with an example model file). 
   if(Rdm_reps_Iter == 1) {
      Rdm_models <- list() 
      Rdm_folds_index <- list()
   } else {
        # Loads Iter, i, j, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, Seed_Fold, Seed_Model, Seed_Main, Rdm_models, Rdm_folds_index, SG_Variables_Selected, roundingDelta
        base::load(paste0("Rdm_model_", Rdm_reps_Iter - 1, ".RData")) 
   }
   
   file.create('Run_NN_Model_Flag', showWarnings = TRUE) # Stopping the model with this flag is broken by the nested loops, but left for now in a hope that it can prehaps be fixed.
   
   
   # Note that errors from plot.loess() are trapped by try() and are normal early in the iteration loop since there is not enough data to smooth.
   for(j in (length(Rdm_folds_index) + 1):Rdm_reps) {
    
      cat(paste0("\n\nStart of Random Rep = ", j , "\n\n"))
     
      Seed_Data <- Seed_reps[j]
      
      # Split the data into folds, spitting the remainder of an un-even division into the first folds, one otie per fold until finished.
      #   The split is based on the current seed which is dictated by Seed_Main (see above).
      set.seed(Seed_Data)
      index_org <- 1:nrow(Model_Spectra.sg.iPLS_Meta)
      (fold_size_min <- floor(length(index_org)/Folds_Num))
      (num_extra <- Folds_Num * dec(length(index_org)/Folds_Num))
      index <- index_org
      folds_index <- list()
      for(i in 1:(Folds_Num - 1)) {
         print(c(fold_size_min, i, num_extra, i <= num_extra, fold_size_min + ifelse(i <= num_extra, 1, 0), i - num_extra))
         folds_index[[i]] <- sample(index, fold_size_min + ifelse(i < (num_extra + 0.1), 1, 0))  # Finite math - grr!
         index <- index[!index %in% folds_index[[i]]]
      }
      folds_index[[Folds_Num]] <- index  # Remainder from the above for() loop goes into the last fold index
      
      print(lapply(folds_index, length)) # Check the binning result
      print(c(sum(unlist(lapply(folds_index, length))), length(index_org)))
      Sys.sleep(5)
      
      Fold_models <- list()
      for (i in 1:Folds_Num) {
      
          Model_Spectra.sg.iPLS_Meta.F <- Model_Spectra.sg.iPLS_Meta[-folds_index[[i]], ]
          print(dim(Model_Spectra.sg.iPLS_Meta.F)); Sys.sleep(3)
          TMA_Vector.F <- TMA_Vector[-folds_index[[i]]]
          
          # Split the data into training set (2/3) and test set (1/3)
          set.seed(Seed_Data)
          index <- 1:nrow(Model_Spectra.sg.iPLS_Meta.F)
          testindex <- sample(index, trunc(length(index)/3))
          x.test <- 1000 * Model_Spectra.sg.iPLS_Meta.F[testindex, ]
          x.train <- 1000 * Model_Spectra.sg.iPLS_Meta.F[-testindex, ]
          y.test <- TMA_Vector.F[testindex]
          y.train <- TMA_Vector.F[-testindex]
          
          cat(paste0("\n\nDimension of x.train = ", paste(dim(x.train), collapse = ' '), '\n\n')) #  906 380; 905 380 with crystallized otie removed
            
          # Same learning rate for all models
          learningRate <- c(0.00088, 0.0009)[2]
          
          layer_dropout_rate <- NULL
          # layer_dropout_rate <- 0.2
          
          if(model_Name == 'FCNN_model_ver_1')  model <- FCNN_model_ver_1(layer_dropout_rate = layer_dropout_rate, activation_function = activation_function)
          if(model_Name == 'CNN_model_ver_5')  model <- CNN_model_ver_5()
          if(model_Name == 'CNN_model_2D')  model <- CNN_model_2D()
                
          # -- Don't reset Iter, Cor, CA_diag, SAD, or .Random.seed when re-starting the same run ---
          tensorflow::set_random_seed(Seed_Model, disable_gpu = Disable_GPU); Seed_Model  # Trying to this here and above (see the help for: tensorflow::set_random_seed)
          set.seed(Seed_Data); Seed_Data # Re-setting the 'data' seed here to know where the model starts, also the Keras backend needs to cleared and the model reloaded - see above.
          
          Iter <- 0
          Cor <- RMSE <- CA_diag <- SAD <- saveModels <- NULL
          saveModels_List <- list()
          
          while(file.exists('Run_NN_Model_Flag')) {    # The multiple full fold version breaks the "stop by removing the file flag", but it remains for now
          
             # R memory garbage collection
             gc()
             
             # Clear TensorFlow's session
             keras::k_clear_session()
          
             (Iter <- Iter + 1)
             
             cat(paste0("\n\nRandom Replicates = ", j, ": Fold number = ", i, ": Iter = ", Iter,"\n"))
             
             if(Iter > 1) {
                 Iter_Loop_Time_Min <- as.numeric(difftime(Sys.time(), Loop_Start_Time, units = "mins"))
                 cat("\nThe last 500 epochs took", format(Iter_Loop_Time_Min, digits = 4), "minutes.\n\n")  # 500 epochs is hardwired - see below
                 Time_Left_Min <- ((Rdm_reps - j) * Folds_Num * Iter_Num + (Folds_Num - i) * Iter_Num + (Iter_Num - Iter + 1)) * Iter_Loop_Time_Min
                 if(Time_Left_Min < 60)  cat("Approximately", format(Time_Left_Min, digits = 4), "minutes remaining for this replicate. ")
                 if(Time_Left_Min >= 60 & Time_Left_Min < 60 * 24)  cat("Approximately", format(Time_Left_Min/60, digits = 4), "hours remaining for this replicate. ")
                 if(Time_Left_Min >= 60 * 24)  cat("Approximately", format(Time_Left_Min/60/24, digits = 4), "days remaining for this replicate. ")
                 
                 Time_Left_Min_All <- ((Rdm_Reps_Main - j) * Folds_Num * Iter_Num + (Folds_Num - i) * Iter_Num + (Iter_Num - Iter + 1)) * Iter_Loop_Time_Min
                 if(Time_Left_Min_All < 60)  cat("Around", format(Time_Left_Min_All, digits = 4), "minutes left to finish.\n\n")
                 if(Time_Left_Min_All >= 60 & Time_Left_Min_All < 60 * 24)  cat("Around", format(Time_Left_Min_All/60, digits = 4), "hours left to finish.\n\n")
                 if(Time_Left_Min_All >= 60 * 24)  cat("Around", format(Time_Left_Min_All/60/24, digits = 4), "days left to finish.\n\n")
             } 
             
             Loop_Start_Time <- Sys.time()
             
        
             # config <- tf$compat.v1.ConfigProto(intra_op_parallelism_threads = 2L, inter_op_parallelism_threads = 2L)
             # session <-  tf$Session(config = config)
             # k_set_session(session)
             
             # blas_set_num_threads(4)
             # blas_get_num_procs()
             
             # FCNN model
             if(model_Name == 'FCNN_model_ver_1') {
                x.train.array <- as.matrix(x.train)
                # callback_tensorboard() writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics.
                history <- fit(model, x.train.array, y.train, epochs = 1, batch_size = 32, validation_split = 0.2, verbose = 2, 
                                callbacks = if(file.exists('NN_Verbose_Flag.txt')) list(callback_tensorboard(histogram_freq = 1, profile_batch = 2)) else NULL, view_metrics = FALSE)  
                history <- fit(model, x.train.array, y.train, epochs = 198, batch_size = 32, validation_split = 0.2, verbose = ifelse(file.exists('NN_Verbose_Flag.txt'), 2, 0), view_metrics = ifelse(file.exists('NN_Verbose_Flag.txt'), TRUE, FALSE))
                history <- fit(model, x.train.array, y.train, epochs =   1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = FALSE)
                history <- fit(model, x.train.array, y.train, epochs =  99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = FALSE)
                history <- fit(model, x.train.array, y.train, epochs =   1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = FALSE)
                history <- fit(model, x.train.array, y.train, epochs = 200, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = FALSE)
                x.test.array <- as.matrix(x.test)
             }
             
             viewMetrics <- c(TRUE, FALSE)[2]
             
             # CNN_model ver 1,3,4,5
             if(model_Name == 'CNN_model_ver_5') {
                x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
                history <- fit(model, x.train.array, y.train, epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2, 
                     view_metrics = viewMetrics)
                  #  view_metrics = viewMetrics, callbacks = list(callback_tensorboard(histogram_freq = 1, profile_batch = 2))) # profile_batch = c(1, 5)
                 x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
              }
             
             # CNN_model ver 2 
             # x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))   
             # history <- fit(model, x.train.array, y.train, epochs =   1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
             # history <- fit(model, x.train.array, y.train, epochs =  99, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
             # history <- fit(model, x.train.array, y.train, epochs =   1, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
             # history <- fit(model, x.train.array, y.train, epochs = 199, batch_size = 32, validation_split = 0.2, verbose = 0, view_metrics = viewMetrics)
             # x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
           
             if(model_Name == 'CNN_model_2D') {
                 x.train.array <- array(as.matrix(x.train), c(nrow(x.train), ncol(x.train), 1))
                 history <- fit(model, x.train.array, y.train * diag(length(y.train)), epochs = 50, batch_size = 32, validation_split = 0.2, verbose = 2, view_metrics = viewMetrics)
                 x.test.array <- array(as.matrix(x.test), c(nrow(x.test), ncol(x.test), 1))
             }  
            
             evaluate(model, x.test.array,  y.test, verbose = 0)
             cat("\n")
             
             print(summary(history))
             
             dev.set(3)
             print(plot(history))
             if(file.exists('NN_Verbose_Flag.txt'))
                browsePlot('print(plot(history))', file = paste0("Figures/NN_History_Iter_", Iter, ".png")) # Save NN History figures
                 
             # Predict using the test set; plot, create statistics, and create an agreement table
             y.test.pred <- predict(model, x.test.array)
            
             if(model_Name == 'FCNN_model_ver_1' & is.null(layer_dropout_rate))  Delta <- -0.05 # Delta is a previous estimate or guess for now, which varies by species.
             if(model_Name == 'FCNN_model_ver_1' & !is.null(layer_dropout_rate))  Delta <- -0.3
             if(model_Name == 'CNN_model_ver_5')  Delta <- -0.2
             if(model_Name == 'CNN_model_2D')  Delta <- 0
               
             y.test.pred.rd <- round(y.test.pred + Delta)  # Rounding with a added delta  (which is a negative number)
             
             dev.set(4)
             # plot(y.test, y.test.pred)
             # abline(0, 1, col = 'green', lty = 2)
             print(predicted_observed_plot(y.test, y.test.pred, xlab = 'y.test', ylab = 'y.test.pred'))
             
             # SAD vector the Sum of absolute differences plot
             # SAD <- c(SAD, sqrt(sum((y.test - y.test.pred.rd)^2)/(length(y.test) - 1)))  # RMSE
             SAD <- c(SAD, sum(abs(y.test - y.test.pred.rd)))
               
             # Correlation vector for the iterations plot
             Cor <- c(Cor, cor(y.test, y.test.pred))
          
             # RMSE vector for the iterations plot
             RMSE <- c(RMSE, sqrt(mean((y.test - y.test.pred)^2, na.rm = TRUE)))
                
             
             # e1071::classAgreement diagonal
             CA_diag <- c(CA_diag, e1071::classAgreement(table(y.test.pred.rd, y.test), match.names = FALSE)$diag)
             cat("\nclassAgreement Diagonal =", rev(CA_diag)[1], "\n")
             cat("\n\n")
             # print(e1071::classAgreement(Table(y.test.pred.rd, y.test), match.names = TRUE)$diag)  #  match.names = TRUE option
             
             # Correlation Between Sum of Absolute Differences and the classAgreement diagonal 
             if(length(SAD) >= 10)
             #    cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD[5:length(SA)], CA_diag[5:length(CA_diag)]), 6), "\n")
                  cat("\nCorrelation between sum of Absolute Differences and the classAgreement Diagonal =", signif(cor(SAD, tail(CA_diag, length(SAD))), 6), "\n")
            
             dev.set(5)  # agreementFigure() also prints out the Correlation, R_squared, RMSE, MAE, SAD (Sum of Absolute Differences) 
             agreementFigure(y.test, y.test.pred, Delta = Delta, full = FALSE, main = paste0("Random Reps = ", j, "; Folds = ", i, "; Iter = ", Iter))
            
             dev.set(2)
             par(mfrow = c(3, 1))
             
             plot(1:length(RMSE), RMSE, col = 'green', type = 'b', ylab = "RMSE (green)", xlab = "Iteration Number")
             abline(h = 4, lty = 2, col ='grey39', lwd = 1.25)
             
             # if(Iter < 5) 
             #    try(plot(1:length(CA_diag), CA_diag, col = 'red', type = 'b', ylab = "Diagonal of Class Agreement (red)", xlab = "Iteration Number"))
             # else
             #    try(plot.loess(1:length(CA_diag), CA_diag, col = 'red', line.col = 'deeppink', type = 'b', ylab = "Diagonal of Class Agreement (red)", xlab = "Iteration Number"))
             try(plot(1:length(CA_diag), CA_diag, col = 'red', type = 'b', ylab = "Diagonal of Class Agreement (red)", xlab = "Iteration Number"))
             abline(h = 0.2, lty = 2, col ='grey39', lwd = 1.25)
            
             # Avoiding high SAD values at the beginning, and rarely, during a run.
             SAD_plot <- SAD
             # SAD_plot[SAD_plot > 1400] <- NA  # Need different value for 5k+ oties in model... # Extreme model runs can, on a very rare occasion, put the value of SAD above 1,400 beyond the initial runs
             
             # if(Iter < 5) 
             #    try(plot(1:length(SAD_plot), SAD_plot, col = 'blue', type = 'b', ylab = "Sum of Absolute Differences (blue)", xlab = "Iteration Number"))
             # else
             #    try(plot.loess(1:length(SAD_plot), SAD_plot, col = 'blue', line.col = 'dodgerblue', type = 'b', ylab = "Sum of Absolute Differences (blue)", xlab = "Iteration Number"))
             try(plot(1:length(SAD_plot), SAD_plot, col = 'blue', type = 'b', ylab = "Sum of Absolute Differences (blue)", xlab = "Iteration Number"))
             abline(h = 950, lty = 2, col ='grey39', lwd = 1.25)
             
             # Save all the iteration models until the best one is found
             print(saveName <- paste0(Spectra_Set, '_', paste(get.subs(model_Name, "_")[-2], collapse = "_"), '_SM_', Seed_Model, '_RI_', j, '_LR_', 
                format(learningRate, sci = FALSE), '_LD_', ifelse(is.null(layer_dropout_rate), 0, layer_dropout_rate), '_It_', length(SAD), 
                '_SAD_', rev(SAD)[1], '_', timeStamp()))
             assign(saveName, keras::serialize_model(model, include_optimizer = TRUE))
             # save(Iter, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, .Random.seed, list = saveName, file = paste0(saveName, '.RData')) # For debugging
             
             saveModels <- c(saveModels, saveName)
             saveModels_List[[saveName]] <- keras::serialize_model(model, include_optimizer = TRUE)
            
             if(Iter == Iter_Num)
                 break
          } # Iter while() loop
          
          if(!file.exists('Run_NN_Model_Flag'))
             break
          
          Iter_Best_Model <- sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 2))[1, 4]  # Best model is when SAD is lowest, with ties broken by RMSE
          print(sort.f(data.frame(SAD, RMSE, CA_diag, Iter = 1:Iter_Num), c(1, 3)))
          cat(paste0('\n\nBest_Model Number = ', Iter_Best_Model, '\n\n'))
          
          Fold_models[[i]] <- saveModels_List[[Iter_Best_Model]]
          cat(paste0('\nBest Model Name = ', saveModels[Iter_Best_Model], "\n\n"))
          rm(list = saveModels)
          
          x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS_Meta[folds_index[[i]], ])
          y.fold.test <- TMA_Vector[folds_index[[i]]]
          y.fold.test.pred <- predict(keras::unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test)
          
          dev.set(6)
          agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = TRUE, main = paste0("Random Rep = ", j, "; Fold Num = ", i)) 
          
          dev.set(7)
          agreementFigure(y.fold.test, y.fold.test.pred, Delta = Delta, full = FALSE, main = paste0("Random Rep = ", j, "; Fold Num = ", i)) 
      } # j Fold loop
      
      if(!file.exists('Run_NN_Model_Flag'))
             break
   
      Rdm_models[[j]] <- Fold_models # List of lists being assigned to an element of a list - the best model for each fold (10 or other used) within the jth random rep
      Rdm_folds_index[[j]] <- folds_index # List of vectors being assigned to an element of a list - the index for each fold (10 or other used) within the jth random rep
     
     
      x.fold.test.ALL <- NULL
      y.fold.test.ALL <- NULL
      y.fold.test.pred.ALL <- NULL
      for (k in 1:length(Fold_models)) {
         x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS_Meta[folds_index[[k]], ])
         x.fold.test.ALL <- rbind(x.fold.test.ALL, x.fold.test)
         y.fold.test.ALL <- c(y.fold.test.ALL, TMA_Vector[folds_index[[k]]])
         print(length(predict(keras::unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test)))
         y.fold.test.pred.ALL <- c(y.fold.test.pred.ALL, predict(keras::unserialize_model(Fold_models[[k]], custom_objects = NULL, compile = TRUE), x.fold.test))
      }
   
      SG_Variables_Selected <- names(Model_Spectra.sg.iPLS_Meta)
      roundingDelta <- Delta  # This Delta is only the previous estimate or guess for now (see above). The best rounding Delta is again tested for in the predict script.
      
      save(Iter, i, j, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, Seed_Fold, Seed_Model, Seed_Main, Rdm_models, 
               Rdm_folds_index, SG_Variables_Selected, roundingDelta, file = paste0("Rdm_model_", length(Rdm_folds_index), ".RData"))
      
      
   }  # k Random Replicate loop
 
}
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 

} ###

# --- Find Median over all Rdm_reps Models and create figures ---
{ ###


if(exists('Wrap_Up_Flag')) {
   
   # Only 2 loads needed to redo this section with new data - the Model_Spectra.sg.iPLS_Meta has to, of course, match the Rdm_model and Rdm_folds_index and the Conda eniv needs to be loaded properly using the code above.
   # base::load("C:/SIDT/Sablefish 2022 Combo/Sable_Combo_2022_NN_Fish_Len_Otie_Wgt_GPU_Machine/Sable_Combo_2022_FCNN_model_ver_1_5_Rdm_model_21_Dec_2023_08_14_19.RData")
   # base::load("C:/SIDT/Sablefish 2022 Combo/Sable_Combo_2022_NN_Fish_Len_Otie_Wgt/Sable_Combo_2022_Model_Spectra.sg.iPLS_Meta.RData") 
   #    A generic Model_Spectra.sg.iPLS_Meta may need metadata added to the scans - compare to the end of SG_Variables_Selected (found within e.g. ...FCNN_model_ver_1_20_Pred_Median_TMA.RData).
   #    If the model has a reduced number of otoliths, then the correct Run number's pseudo random number seed has to set.
   #    cor(Sable_Combo_2022_NN_Pred_Median_TMA$TMA, TMA_Vector) needs to be 1 (one).
   
   
   # ----------------------- Put the fitted results for each random reps (Rdm_reps) full fold model into a data frame:  y.fold.test.pred_RDM ------------------------
   (Rdm_reps <- length(Rdm_folds_index))
   
   y.fold.test.pred_RDM <- NULL
   for (j in 1:Rdm_reps) {
   
      folds_index <- Rdm_folds_index[[j]]
      Fold_models <- Rdm_models[[j]]
      
      y.fold.test.pred.ALL <- NULL
      for (i in 1:length(Fold_models)) {
         if(verbose)
            cat(paste0("\nRdm_reps ", j, ": Fold_model ", i, "\n"))
         x.fold.test <- as.matrix(1000 * Model_Spectra.sg.iPLS_Meta[folds_index[[i]], ])
         y.fold.test.pred <- as.vector(predict(keras::unserialize_model(Fold_models[[i]], custom_objects = NULL, compile = TRUE), x.fold.test))
         if(verbose)
            print(c(length(folds_index[[i]]), length(y.fold.test.pred)))
         y.fold.test.pred.ALL <- rbind(y.fold.test.pred.ALL, cbind(Index = folds_index[[i]], y.test.fold.pred = y.fold.test.pred))
      }
      
      cat(paste0("\nFold_model ", j, "\n"))
      print(dim(y.fold.test.pred.ALL ))
      
      y.test.pred <- sort.f(data.frame(y.fold.test.pred.ALL))[, 2]  # Sort on the Index to match back to the order of the full TMA_Vector and Model_Spectra.sg.iPLS_Meta
      
      y.fold.test.pred_RDM <- rbind(y.fold.test.pred_RDM, y.test.pred)
      
      cat(paste0("\nRdm_reps ", j, "\n"))
      print(dim(y.fold.test.pred_RDM))
     
      if(verbose)
           browsePlot('agreementFigure(TMA_Vector, y.test.pred, Folds = Folds_Num, Delta = -0.05, full = TRUE, main = paste0("Random Rep = ", j))') # Delta is a previous estimate or guess for now
     
      # Full figure only needed for a long-lived species like Sablefish
      # dev.new(width = 11, height = 8)
      # agreementFigure(TMA_Vector, y.test.pred, Folds = Folds_Num,  Delta = -0.05, full = FALSE, main = paste0("Random Rep = ", j))
   }
   
   
   # ----------------------- Median over all Rdm_reps Models ------------------------
   
   # Look to see if all random reps fit well and didn't have an issue with good fitting (only seen in the metadata only models). Also compare the agreement figures.
   renum(t(apply(y.fold.test.pred_RDM, 1, function(x) unlist(Cor_R_squared_RMSE_MAE_SAD_APE(x, TMA_Vector)))))
   
   y.fold.test.pred_RDM_median <- apply(y.fold.test.pred_RDM, 2, median)
   # y.fold.test.pred_RDM_median <- apply(y.fold.test.pred_RDM[1, , drop = FALSE], 2, median)  # Select only those random runs which show good fits 
   
   if(!exists('Delta'))
      Delta <- -0.05  # Previous estimate or guess
   data.frame(Delta = Delta, Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median + Delta)))
   
     
   # What is the best Delta (by SAD, with ties broken by RMSE) on the median over all, Rdm_reps, full k-folds 
   Delta_Table <- NULL
   for (Delta. in seq(0, -0.45, by  = -0.05)) {
     # cat("\n\n")
     # print(c(Delta = Delta., Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median + Delta.))))
     Delta_Table <- rbind(Delta_Table, c(Delta = Delta., unlist(Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median + Delta.)))))
   }
     
   print(Delta_Table <- data.frame(Delta_Table)) 
     
   # Best Delta from table above
   (Delta <- Delta_Table$Delta[order(Delta_Table$SAD, Delta_Table$RMSE)[1]])
   
   SG_Variables_Selected <- names(Model_Spectra.sg.iPLS_Meta)
   roundingDelta <- Delta
   
   # Do a final save after the best rounding Delta is official found for this spectra set - the best rounding Delta is again tested for in the predict script.
   save(Iter, i, j, Cor, CA_diag, SAD, learningRate, layer_dropout_rate, Seed_Fold, Seed_Model, Seed_Main, Rdm_models, 
            Rdm_folds_index, SG_Variables_Selected, roundingDelta, file = paste0(Spectra_Set, '_', model_Name, '_', gsub(" ", "0", format(length(Rdm_folds_index), width = 2)), '_Rdm_model_', timeStamp(), '.RData'))
            
   
   # Agreement Figures (standard and zoomed) using the best delta from above
   # browsePlot('agreementFigure(TMA_Vector, y.fold.test.pred_RDM_median, Folds = Folds_Num, Delta = Delta, full = TRUE, main = paste0("Median over ", Rdm_reps, " Full k-Fold Models"), cex = 1.25)', file = 'Figures/Sable_2022_Combo_20_Rdm_Final.pdf', pdf = TRUE) # PDF version
   browsePlot('agreementFigure(TMA_Vector, y.fold.test.pred_RDM_median, Folds = Folds_Num, Delta = Delta, full = TRUE, main = paste0("Median over ", Rdm_reps, " Full k-Fold Models"))', file = paste0('Figures/', Spectra_Set, '_', length(Rdm_folds_index), '_Rdm_Final.png'))
   browsePlot('agreementFigure(TMA_Vector, y.fold.test.pred_RDM_median,  Folds = Folds_Num, Delta = Delta, full = FALSE, main = paste0("Median over ", Rdm_reps, " Full k-Fold Models"))', file = paste0('Figures/', Spectra_Set, '_', length(Rdm_folds_index), '_Rdm_Final_Zoomed.png'))
                                                                                                                                                  
   
   # Apply that best Delta (from above) to all Rdm_reps models individually
   Stats_RDM_median_by_model <- NULL
   for(numRdmModels in 1:Rdm_reps) {
   
      y.fold.test.pred_RDM_median_one_rep <- apply(y.fold.test.pred_RDM[numRdmModels, ,drop = FALSE], 2, median)
      Stats_RDM_median_by_model <- rbind(Stats_RDM_median_by_model, data.frame(t(unlist(Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median_one_rep + Delta))))))
   }
   
   print(Stats_RDM_median_by_model)
    
   
   # An additional full k-fold added to the total number of models at each step in turn
   # dev.new(width = 11, height = 8)
   
   { # matplot of Various stats vs nunber of complete Folds
   browsePlot("
     par(mfrow = c(3,2))  
     # Delta <- -0.05  # Reset Delta if recreating this figure as an old Delta may linger.
     Stats_RDM_median_by_model_added <- NULL
     for(numRdmModels in 1:Rdm_reps) {
     
        y.fold.test.pred_RDM_median <- apply(y.fold.test.pred_RDM[1:numRdmModels, ,drop = FALSE], 2, median)
        Stats_RDM_median_by_model_added  <- rbind(Stats_RDM_median_by_model_added, data.frame(t(unlist(Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median + Delta))))))
     }
     
     print(Stats_RDM_median_by_model_added)
     
     min.stats <- apply(Stats_RDM_median_by_model_added[, c(3,5)], 2, min)
     minAdj <- sweep(data.matrix(Stats_RDM_median_by_model_added[, c(3,5)]), 2, min.stats)
     max.of.Adj <- apply(minAdj, 2, max)
     print(Stats_0_1_interval <- cbind(Stats_RDM_median_by_model_added[, 1:2], t(t(minAdj)/max.of.Adj)))
     
     
     matplot(1:Rdm_reps, Stats_0_1_interval, type = 'o', col = c(1:3, 6), xlab = 'Number of Complete Folds', ylab = 'Various Stats', main = 'Original Order')
      
     # Add 5 more Randomized order figures
     set.seed(Seed_Main) 
     (Seed_reps <- round(runif(6, 0, 1e8)))
     
     for (i in 1:5) { 
        set.seed(Seed_reps[i])
        (Rdm_Vec <- sample(1:Rdm_reps)) 
        Stats_RDM_median_by_model_added <- NULL
        for(numRdmModels in 1:Rdm_reps) {
        
           y.fold.test.pred_RDM_median <- apply(y.fold.test.pred_RDM[Rdm_Vec[1:numRdmModels], , drop = FALSE], 2, median)
           Stats_RDM_median_by_model_added  <- rbind(Stats_RDM_median_by_model_added, data.frame(t(unlist(Cor_R_squared_RMSE_MAE_SAD_APE(TMA_Vector, round(y.fold.test.pred_RDM_median + Delta))))))
        }
          
        min.stats <- apply(Stats_RDM_median_by_model_added[, c(3,5)], 2, min)
        minAdj <- sweep(data.matrix(Stats_RDM_median_by_model_added[, c(3,5)]), 2, min.stats)
        max.of.Adj <- apply(minAdj, 2, max)
        (Stats_0_1_interval <- cbind(Stats_RDM_median_by_model_added[, 1:2], t(t(minAdj)/max.of.Adj)))
             
        matplot(1:Rdm_reps, Stats_0_1_interval, type = 'o', col = c(1:3, 6), xlab = 'Number of Complete Folds', ylab = 'Various Stats', main = 'Randomized Order')
     }
     ", width = 11, height = 8, file = paste0('Figures/Full_k-fold_models_added_sequentially.png'))
   }
   
   # --- NN prediction for each otie in the NN model ---
   Pred_median <- r(data.frame(NN_Pred_Median = apply(y.fold.test.pred_RDM, 2, median), 
                               Lower_Quantile_0.025 = apply(y.fold.test.pred_RDM, 2, quantile, probs = 0.025, na.rm = TRUE),
                               Upper_Quantile_0.975 = apply(y.fold.test.pred_RDM, 2, quantile, probs = 0.975, na.rm = TRUE)), 4) 
   cat(paste0("\n\n--- Note: The quantiles are a reflection of the NN models precision based on ", length(Rdm_models), " full 10-fold randomized models, not the accuracy to a TMA Age ---\n\n"))   
    
   assign(paste0(Spectra_Set, '_NN_Pred_Median_TMA'), data.frame(filenames = fileNames, Pred_median, TMA = TMA_Vector), pos = 1)
   save(list = paste0(Spectra_Set, '_NN_Pred_Median_TMA'), file = paste0(Spectra_Set, '_', model_Name, '_', length(Rdm_folds_index), '_Pred_Median_TMA_', timeStamp(), '.RData'))
   
   
   # Copy the spectra set prediction to a generic name and add a rounded prediction by adding the best delta found above
   Model_Ages <- get(paste0(Spectra_Set, '_NN_Pred_Median_TMA'))
   Model_Ages$Pred_Age_Rounded <- round(Model_Ages$NN_Pred_Median + Delta)
   Model_Ages[1:5,]
   
   # -- Plot by sorted difference --
   # g <- ggplot(Model_Ages_Sub, aes(jitter(TMA, 1.25), TMA - NN_Pred_Median)) +  
   # geom_point() 
   # browsePlot('print(g)', file = paste0('Figures/TMA_minus_NN_Pred_vs_TMA.png'))
   
   # Jitter TMA; vertical line for each unique TMA - without standard grid - all the data used for the training (not a subset like below).
   xlim <- c(min(Model_Ages$TMA) - 1.25, max(Model_Ages$TMA) + 1.25)   
   Model_Ages$TMA_Minus_Pred_Age_Rounded <- Model_Ages$TMA - Model_Ages$Pred_Age_Rounded
   browsePlot('set.seed(707); gPlot(Model_Ages, "TMA", "TMA_Minus_Pred_Age_Rounded", ylab = "TMA - Pred_Age_Rounded", xFunc = jitter, ylim = c(-xlim[2], xlim[2]), xlim = xlim,
                  grid = FALSE, vertLineEachPoint = TRUE)', file = paste0('Figures/TMA_minus_round_NN_Pred_vs_TMA_Jitter.png')) 
   
                  
   # ====== Using a SAMPLE of 100 ages so the figures are not too crowded =====
   set.seed(Seed_Fold)
   Model_Ages_Sub <- Model_Ages[sample(1:nrow(Model_Ages), 100),  ]  
   Model_Ages_Sub$Index <- 1:nrow(Model_Ages_Sub)
   
   # - Plot by order implied by the spectra file names - ggplotly() changes how scale_color_manual() works ?????????????????
   cols <- c('green', 'red')
   g <- ggplot(Model_Ages_Sub, aes(Index, NN_Pred_Median)) +  
   geom_point() +
   geom_errorbar(aes(ymin = Lower_Quantile_0.025, ymax = Upper_Quantile_0.975)) + 
   geom_point(aes(Index, Pred_Age_Rounded, col = cols[1])) + 
   geom_point(aes(Index + 0.1, TMA, col = cols[2])) + 
   scale_color_manual(labels = c('Rounded Age', 'TMA'), values = cols, name = ' ')
   browsePlot('print(g)', file = paste0('Figures/Predicted_Ages_Order_by_File_Names_Subset.png'))
   
      
   # -- Plot by sorted NN predicted ages --
   Model_Ages_Sub_Sorted <- sort.f(Model_Ages_Sub, 'NN_Pred_Median') # Sort Model_Ages_Sub by NN_Pred_Median, except for "Index" (see the next line below)
   Model_Ages_Sub_Sorted$Index <- sort(Model_Ages_Sub_Sorted$Index)  # Reset Index for graphing
   if(verbose) head(Model_Ages_Sub_Sorted, 10)
   
   cols <- c('green', 'red')
   g <- ggplot(Model_Ages_Sub_Sorted, aes(Index, NN_Pred_Median)) +  
   # xlim(0, 65) + ylim(0, 20) +
   geom_point() +
   geom_errorbar(aes(ymin = Lower_Quantile_0.025, ymax = Upper_Quantile_0.975)) + 
   geom_point(aes(Index, Pred_Age_Rounded, col = cols[1])) + 
   geom_point(aes(Index + 0.1, TMA, col = cols[2])) + 
   scale_color_manual(labels = c('Rounded Age', 'TMA'), values = cols, name = ' ')
   browsePlot('print(g)', file = paste0('Figures/Predicted_Ages_Sorted_Subset.png'))
   
   
   # -- Plot by sorted TMA --
   Model_Ages_Sub_Sorted <- sort.f(Model_Ages_Sub, 'TMA') # Sort Model_Ages_Sub by TMA, except for "Index" (see the next line below)
   Model_Ages_Sub_Sorted$Index <- sort(Model_Ages_Sub_Sorted$Index)  # Reset Index for graphing
   if(verbose) head(Model_Ages_Sub_Sorted, 10)
   
   cols <- c('green', 'red')
   g <- ggplot(Model_Ages_Sub_Sorted, aes(Index, NN_Pred_Median)) +  
   # xlim(0, 65) + ylim(0, 20) +
   geom_point() +
   geom_errorbar(aes(ymin = Lower_Quantile_0.025, ymax = Upper_Quantile_0.975)) + 
   geom_point(aes(Index, TMA, col = cols[2])) + 
   geom_point(aes(Index + 0.1, Pred_Age_Rounded, col = cols[1])) + 
   scale_color_manual(labels = c('Rounded Age', 'TMA'), values = cols, name = ' ')
   browsePlot('print(g)', file = paste0('Figures/TMA_Sorted_Subset.png'))
   
   
   # How many TMA ages are 3 or under
   print(Table(TMA_Vector <= 3)/length(TMA_Vector))
   
   # How many TMA ages are 15 or under
   print(Table(TMA_Vector <= 15)/length(TMA_Vector))
   
   # How many TMA ages are 20 or under
   print(Table(TMA_Vector <= 20)/length(TMA_Vector))
}   
   
}  ###


# If data is not from multiple sessions, then Use_Session_Report_Meta can be TRUE  
# Having 'Multi' in the the Spectra_Set sets Use_Session_Report_Meta to FALSE
if(exists('Wrap_Up_Flag'))
   Predict_NN_Age_Wrapper(Spectra_Set = Spectra_Set, Train_Result_Path = "C:/SIDT/Train_NN_Model", Multi_Year = TRUE, Use_Session_Report_Meta = FALSE, 
     Bias_Adj_Factor_Ages = c(12, 13:15), Bias_Reduction_Factor = 1.5, Lowess_smooth_para = 2/3, TMA_Ages = TRUE, TMA_Ages_Only = FALSE, # TMA_Ages_Only = FALSE => Predictions without TMA
     # Metadata_Extra = c("Ship", "Cruise", "Haul", "Date_UTC", "Length_cm", "Sex", "Weight_kg", "Net_Partition"), 
     Metadata_Extra = "Length_cm", 
     Graph_Metadata_Extra = c("structure_weight_dg", "Length_cm"), 
     Metadata_Extra_File = Model_Spectra_Meta_Path, 
     Model_Spectra_Meta_Path = Model_Spectra_Meta_Path, main = "Metadata: Str Wgt & Sex")
John-R-Wallace-NOAA/FishNIRS documentation built on April 12, 2025, 12:59 a.m.

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