##### Written by Jeannine Cavender-Bares. 2/21/2019
##### Calculate Euclidean spectral distances among pixels within plots (or subplots)
##### Distances can start with raw spectra or use vector normalized spectra

setwd("")
library(pls)
library(picante)

spec.plots <- read.csv("raw/all.plots.csv")
splots     <- data.frame(spec.plots)

###############################################
#distance calculation for loop with raw spectra 
smat  <- spec.plots #assign a data matrix
dmat  <- vector() #create empty vector
p     <- matrix() #create empty matrix
r     <- vector()
for (i in 1:24) {  #set for all Plots in matrix
    p     <- subset(smat, Plot==i) # subset all rows (pixels) within the designated plot
    d     <- p[, c(8:178)] # strip off the plot name and pixel number
    ds    <- as.matrix(dist(d))
    dsum  <- (sum(ds) / ((nrow(ds) * ncol(ds)) - nrow(ds)))
    dmat  <- rbind(dmat, dsum)
    dmat  <- as.data.frame(dmat)
    rownames(dmat)[i] <- i
    r <- rbind(r,i)
    print(dmat[i,]) 
}
dmat$Plot <- r #add the plot numbers back on
#save raw spectral distances per plot
#write.csv(dmat, file="processed/spec.dist.plots.csv")

#################################################
#vector normalize the spectra in all of the plots
#### don't rerun - takes a long time
fmat <- spec.plots 
mat                         <-        vector()
for (i in 1:nrow(fmat)){
   t    <- fmat[i,c(8:178)]
   s    <- sqrt(rowSums((t)^2))
   m    <- t/s
   mat  <- rbind(mat,m)
   #print(i)
 }
mat$Plot    <-  fmat$Plot  #put the plot numbers back on
mat$pixel   <-  fmat$pixel #put the pixel numbers back on

# save the vn spectra
write.csv(mat, file="processed/spec.vn.plots.csv")

###########################################
# calculate the distances for the vn spectra
#distance calculation for loop
smat  <- mat #assign data matrix
vmat  <- vector() #create empty vector
p     <- matrix() #create empty matrix
r     <- vector()
for (i in 1:24) {  #set for all Plots in matrix
  #i=1 
  p     <- subset(smat, Plot==i) # subset all rows (pixels) within the designated plot
  d     <- p[, c(1:171)] # strip off the plot name and pixel number
  ds    <- as.matrix(dist(d))
  dsum  <- (sum(ds) / ((nrow(ds) * ncol(ds)) - nrow(ds)))
  vmat  <- rbind(vmat, dsum)
  vmat  <- as.data.frame(vmat)
  rownames(vmat)[i] <- i
  r <- rbind(r,i)
  print(vmat[i,]) 
}
vmat$Plot <- r #add the plot numbers back on
write.csv(vmat, file="processed/spec.vn.dist.plots.csv")


###############################################
#SUBPLOT SCALE
###############################################
# number subplots consecutively and calculate average pairwise distances using above loops
sp.dat<- read.csv("raw/subplot.airborne.spn.csv")
sp.dat$TransectT <- paste("T",sp.dat$Transect,sep="")
sp.dat$Plot.t.sp <-paste(sp.dat$Plot, sp.dat$TransectT,sp.dat$subplot,sep=".")
#################################################
#vector normalize the spectra in all of the plots
#### don't rerun - takes a long time
fmat <- sp.dat 
mat                         <-        vector()
for (i in 1:nrow(fmat)){
   t    <- fmat[i,c(8:178)]
   s    <- sqrt(rowSums((t)^2))
   m    <- t/s
   mat  <- rbind(mat,m)
   #print(i)
 }
mat$Plot    <-  fmat$Plot  #put the plot numbers back on
mat$pixel   <-  fmat$pixel #put the pixel numbers back on
mat$Plot.t.sp <- fmat$Plot.t.sp
mat$subplot.no <- fmat$subplot.no
# save the vn spectra
write.csv(mat, file="processed/spec.vn.subplots.csv")

###############################################
# Use VN spectra
###############################################
#distance calculation for loop with VN spectra
smat  <- mat #assign a data matrix
dmat  <- vector() #create empty vector
p     <- matrix() #create empty matrix
r     <- vector()
for (i in 1:192) {  #set for all subplots in matrix
  p     <- subset(smat, subplot.no==i) # subset all rows (pixels) within the designated plot
  d     <- p[, c(1:171)] # strip off the plot name and pixel number
  ds    <- as.matrix(dist(d))
  dsum  <- (sum(ds) / ((nrow(ds) * ncol(ds)) - nrow(ds)))
  dmat  <- rbind(dmat, dsum)
  dmat  <- as.data.frame(dmat)
  rownames(dmat)[i] <- i
  r <- rbind(r,i)
  print(dmat[i,]) 
}
dmat$subplot <- r #add the plot numbers back on
#use these as a look up table to put the subplot ID back on by hand

#save raw spectral distances per subplot
write.csv(dmat, file="processed/spec.vn.dist.subplots.csv")

###############################################
# Raw spectral dist
###############################################
#distance calculation for loop with raw spectra
smat  <- sp.dat #assign a data matrix
dmat  <- vector() #create empty vector
p     <- matrix() #create empty matrix
r     <- vector()
for (i in 1:192) {  #set for all subplots in matrix
  p     <- subset(smat, subplot.no==i) # subset all rows (pixels) within the designated plot
  d     <- p[, c(6:183)] # strip off the plot name and pixel number
  ds    <- as.matrix(dist(d))
  dsum  <- (sum(ds) / ((nrow(ds) * ncol(ds)) - nrow(ds)))
  dmat  <- rbind(dmat, dsum)
  dmat  <- as.data.frame(dmat)
  rownames(dmat)[i] <- i
  r <- rbind(r,i)
  print(dmat[i,]) 
}
dmat$subplot <- r #add the plot numbers back on
#use these as a look up table to put the subplot ID back on by hand

#save raw spectral distances per subplot
write.csv(dmat, file="processed/spec.dist.subplots.csv")