# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")

#--------------- (1) Read data ---------------

#--------------- (1.1) Read log event data ---------------

dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
                  colClasses = c("character", "character", "POSIXct",
                                 "POSIXct", "character", "integer",
                                 "numeric", "character", "character",
                                 rep("numeric", 3), "character",
                                 "character", rep("numeric", 11),
                                 "character", "character"),
                  sep = ";", header = TRUE)
dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
                                          "openPopup"))

# Select data pre Corona
dat <- dat0[as.Date(dat0$date.start) < "2020-03-13", ]
dat <- dat[dat$path != 106098, ]

#--------------- (1.2) Read infos for PM for items ---------------

datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
                      sep = ";", row.names = 1)


#--------------- (1.3) Extract additional infos for clustering ---------------

datitem$duration <- aggregate(duration ~ item, dat, mean)$duration
datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree
datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path
datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case
datitem$ntopics <- aggregate(topic ~ item, dat, function(x) length(unique(x)))$topic
datitem$mostfreq_num <- as.numeric(gsub(".*: (.*)}", "\\1", datitem$mostfreq))

#--------------- (2) Clustering ---------------

df <- datitem[, c("precision", "generalizability", "nvariants",
                   "mostfreq_num", "duration", "distance", "scaleSize",
                   "rotationDegree", "npaths", "ncases", "ntopics")] |>
       scale()
mat <- dist(df)

hc <- hclust(mat, method = "ward.D2")

library(factoextra)
fviz_dend(hc, cex = 0.5)

datitem$grp <- cutree(hc, k = 6)

fviz_dend(hc, k = 6,
          cex = 0.5,
          k_colors = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
                       "#FF6900", "#434F4F"),
          #type = "phylogenic",
          rect = TRUE
)

p <- fviz_cluster(list(data = df, cluster = grp),
                  palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
                              "#FF6900", "#434F4F"),
                  ellipse.type = "convex",
                  repel = TRUE,
                  show.clust.cent = FALSE, ggtheme = theme_bw())
p

aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
                ncases, ntopics) ~ grp, datitem, mean)

datitem$item <- gsub("item_([0-9]{3})", "\\1", row.names(datitem))

res <- merge(dat, datitem[, c("item", "grp")], by = "item", all.x = TRUE)
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]

write.table(res,
            file = "results/haum/event_logfiles_pre-corona_with-clusters.csv",
            sep = ";",
            quote = FALSE,
            row.names = FALSE)

library(bupaverse)

res$start <- res$date.start
res$complete <- res$date.stop

for (cluster in sort(unique(res$grp))) {

  alog <- activitylog(res[res$grp == cluster, ],
                      case_id     = "path",
                      activity_id = "event",
                      resource_id = "item",
                      timestamps  = c("start", "complete"))
  
  dfg <- process_map(alog,
                     type_nodes = frequency("relative"),
                     sec_nodes  = frequency("absolute"),
                     type_edges = frequency("relative"),
                     sec_edges  = frequency("absolute"),
                     rankdir    = "LR",
                     render     = FALSE)
  export_map(dfg,
             file_name = paste0("results/processmaps/dfg_cluster", cluster, "_R.pdf"),
             file_type = "pdf",
             title     = paste("DFG Cluster", cluster))


}

#--------------- (3) Visualization with pictures ---------------

library(png)
library(jpeg)
library(grid)

colors <- c("#78004B", "#000000", "#3CB4DC", "#91C86E", "#FF6900",
            "#434F4F")

#pdf("results/haum/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
png("results/haum/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)

par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))

plot(y ~ x, p$data, type = "n", ylim = c(-3.2, 3), xlim = c(-4.7, 6.4))

for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {

  if (item == "125") {

    pic <- readJPEG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
                          item, "/", item, ".jpg"))
  } else {
    pic <- readPNG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
                          item, "/", item, ".png"))
  }

  img <- as.raster(pic[,,1:3])

  x <- p$data$x[sprintf("%03d", as.numeric(rownames(p$data))) == item]
  y <- p$data$y[sprintf("%03d", as.numeric(rownames(p$data))) == item]

  points(x, y,
         col = colors[p$data$cluster[sprintf("%03d", as.numeric(rownames(p$data))) == item]],
         cex = 9,
         pch = 15)

  rasterImage(img,
              xleft = x - .4,
              xright = x + .4,
              ybottom = y - .2,
              ytop = y + .2)

}

dev.off()