##
## main demo for AQP package
##

# load packages
library(aqp)
library(ape)
library(cluster)
library(lattice)
library(data.table)

# Example 1: sp1: 9 soil profiles from Pinnacles National Monument, CA.
data(sp1)

# 
# 1. basic profile aggregation and plotting
# 
depths(sp1) <- id ~ top + bottom

# aggregate all profiles into 1,5,10,20 cm thick slabs, computing mean values by slab
s1 <- slab(sp1, ~ prop, slab.fun=mean, na.rm=TRUE)
# 5cm
s5 <- slab(sp1, ~ prop, slab.fun=mean, na.rm=TRUE, slab.structure=5)
# 10cm segments:
s10 <- slab(sp1, ~ prop, slab.fun=mean, na.rm=TRUE, slab.structure=10)
# 20cm
s20 <- slab(sp1, ~ prop, slab.fun=mean, na.rm=TRUE, slab.structure=20)

# check results
head(s1)

# variation in segment-weighted mean property: very little
round(sapply(list(s1, s5, s10, s20),
             function(i) {
               with(i, sum((bottom - top) * value) / sum(bottom - top))
             }), 1)

# combined viz
g2 <- make.groups(
  "1cm interval" = s1,
  "5cm interval" = s5,
  "10cm interval" = s10,
  "20cm interval" = s20
)

# note special syntax for plotting step function
xyplot(
  cbind(top, bottom) ~ value,
  groups = which,
  data = g2,
  id = g2$which,
  panel = panel.depth_function,
  ylim = c(250, -10),
  scales = list(y = list(tick.number = 10)),
  xlab = 'Property',
  ylab = 'Depth (cm)',
  main = 'Soil Profile Aggregation by Regular Depth-slice',
  auto.key = list(
    columns = 2,
    points = FALSE,
    lines = TRUE
  )
)

# Example 2: sp3: 10 soil profiles from the Sierra Nevada Foothills Region of California.
data(sp3)

# 
# 2. investigate the concept of a 'median profile'
# note that this involves aggregation between two dissimilar groups of soils
# 

# generate a RGB version of soil colors
# and convert to HSV for aggregation
sp3$h <- NA
sp3$s <- NA
sp3$v <- NA
sp3.rgb <- with(sp3, munsell2rgb(hue, value, chroma, return_triplets = TRUE))
sp3[, c('h', 's', 'v')] <- t(with(sp3.rgb, rgb2hsv(r, g, b, maxColorValue = 1)))

# promote to SoilProfileCollection
depths(sp3) <- id ~ top + bottom

# aggregate across entire collection
a <- slab(sp3,
          fm = ~ clay + cec + ph + h + s + v,
          slab.structure = 10)

# check
str(a)

# convert back to wide format
a.wide.q25 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q25'))
a.wide.q50 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q50'))
a.wide.q75 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q75'))

# add a new id for the 25th, 50th, and 75th percentile pedons
a.wide.q25$id <- 'Q25'
a.wide.q50$id <- 'Q50'
a.wide.q75$id <- 'Q75'

# combine original data with "mean profile"
vars <- c('id', 'top', 'bottom', 'clay', 'cec', 'ph', 'h', 's', 'v')

# make data.frame version of sp3
sp3.grouped <- as.data.frame(rbind(as.data.table(horizons(sp3))[, .SD, .SDcol = vars], 
                     a.wide.q25[, .SD, .SDcol = vars],
                     a.wide.q50[, .SD, .SDcol = vars], 
                     a.wide.q75[, .SD, .SDcol = vars]))

# re-constitute the soil color from HSV triplets
# convert HSV back to standard R colors
sp3.grouped$soil_color <- with(sp3.grouped, hsv(h, s, v))

# give each horizon a name
sp3.grouped$name <- paste(round(sp3.grouped$clay), '/' ,
                          round(sp3.grouped$cec), '/',
                          round(sp3.grouped$ph, 1))

plot(sp3.grouped)

## perform comparison, and convert to phylo class object
## D is rescaled to [0,]
d <- profile_compare(sp3.grouped,
                     vars = c('clay', 'cec', 'ph'),
                     max_d = 100,
                     k = 0.01,
                     replace_na = TRUE,
                     add_soil_flag = TRUE, 
                     rescale.result = TRUE)

h <- agnes(d, method = 'ward')
p <- ladderize(as.phylo(as.hclust(h)))

# look at distance plot-- just the median profile
plot_distance_graph(d, 12)

# similarity relative to median profile (profile #12)
round(1 - (as.matrix(d)[12, ] / max(as.matrix(d)[12, ])), 2)

## make dendrogram + soil profiles
# first promote to SoilProfileCollection
depths(sp3.grouped) <- id ~ top + bottom

# setup plot: note that D has a scale of [0,1]
par(mar = c(1, 1, 1, 1))
p.plot <- plot(p,
               cex = 0.8,
               label.offset = 3,
               direction = 'up',
               y.lim = c(2, 0),
               x.lim = c(1.25, length(sp3.grouped) + 1),
               show.tip.label = FALSE)

# get the last plot geometry
lastPP <- get("last_plot.phylo", envir = .PlotPhyloEnv)

# the original labels, and new (indexed) order of pedons in dendrogram
d.labels <- attr(d, 'Labels')

new_order <- sapply(1:lastPP$Ntip,
                    function(i)
                      which(as.integer(lastPP$xx[1:lastPP$Ntip]) == i))

# plot the profiles, in the ordering defined by the dendrogram
# with a couple fudge factors to make them fit
plotSPC(
  sp3.grouped,
  color = "soil_color",
  plot.order = new_order,
  scaling.factor = 0.01,
  width = 0.1,
  cex.names = 0.5,
  y.offset = max(lastPP$yy) + 0.1,
  add = TRUE
)
