#!/usr/bin/env Rscript

library('ggplot2')
library('ggthemes')
library('tidyverse')
library('repr')
library('jcolors')
library("reticulate")

args <- commandArgs(trailingOnly = TRUE)

#setwd(source_dir)
use_python("/mnt/lustre01/work/ku0598/k202107/software/install/python/3.8.0/bin/python3", required=T)
source_python("/work/ku0598/k202107/git/mistral-job-evaluation/scripts/jupyter/r_visual_jobs#pickle_reader.py")

global = list()
global[['source_dir']] = '/work/ku0598/k202107/git/mistral-job-evaluation/data/eval_20200117'
global[['eval_dir']] = '../evaluation'
global[['fig_dir']] = sprintf('%s/figures/job_visualization', global[['eval_dir']])
global[['key']] = 22897682

config = list()
#config[['crypted_jobid']] = 4296426
config[['crypted_jobid']] = strtoi(args[1])
config[['jobid']] = bitwXor(config[['crypted_jobid']], global[['key']])
config[['cat_fn']] =  sprintf("%s/600/cats/%s.json", global[['source_dir']], config[['jobid']])
config[['raw_fn']] = sprintf('%s/600/jobdata/%s.pkl', global[['source_dir']], config[['jobid']])


graph_config = list()
# Colorized entities
# "name" : file systems
# "host" : compute nodes
# "metric" : I/O metrics
graph_config[['cols']] = c('metric', 'host', 'name')
#graph_config[['cols']] = c('host', 'name')

# Enable views
#'default', 'jscore', 'nscore', 'mscore'
graph_config[['views']] = c('jscore', 'default', 'nscore', 'mscore')
#graph_config[['views']] = c('default')

# Set at nth position a label
graph_config[['x_breakpoint_interval']] = 5

# Segments size in minutes
graph_config[['seg_size']] = 10


rename_metrics <- function(data) {
    data['metric'] <- lapply(data['metric'], gsub, pattern = "host.lustre.", replacement = "", fixed = TRUE)
    data['metric'] <- lapply(data['metric'], gsub, pattern = "stats.", replacement = "", fixed = TRUE)
    data['metric'] <- lapply(data['metric'], gsub, pattern = ".bytes", replacement = "_bytes", fixed = TRUE)
    data['metric'] <- lapply(data['metric'], gsub, pattern = ".calls", replacement = "_calls", fixed = TRUE)
    return(data)
}


visualize_categories <- function(gconf, cconf, data, view, col, x_breakpoints) {
    out_dir = sprintf('%s/%d_%d', gconf[['fig_dir']], cconf[['jobid']], cconf[['crypted_jobid']])
    dir.create(out_dir, recursive=TRUE)
   
    # Set legend title
    if (col == 'host') {
        gtitle = 'Node'
    }
    else if (col == 'metric') {
        gtitle = 'Metric'
    }
    else if (col == 'name') {
        gtitle = 'File system'
    }
    
   
    # The palette with black:
    #cbp2 = c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")
    # General plot
    p <- (
        ggplot(data, aes_string(x='bin', y='score', fill=col))
        #+ geom_bar(stat='summary', fun.y = "mean") 
        + geom_bar(stat='identity') 
        + scale_x_discrete(breaks=x_breakpoints)
        #+ scale_fill_manual(values= cbp2)
        #+ geom_line(data=dat,aes(x='rmin', y='value', color="Second line"))
        + guides(
            fill = guide_legend(title=gtitle, nrow=15)
        )
        + ylab('JScore')
        + xlab('Runtime in minutes')
        + theme_linedraw()
        #+ theme_classic()
        + theme(
            #guide_legend.title = element_text('File system'), #element_blank(),
            #legend.text=element_text(size=6),
            legend.spacing.y = unit(0, 'cm'),
            #legend.spacing.x = unit(0, 'cm'),
            legend.text = element_text(size=8, margin = margin(t = 1)),
            strip.text.x = element_text(size = 8, color = "black"),
            strip.text.y = element_text(size = 8, color = "black"),
            legend.key = element_rect(size = 1),
            legend.key.size = unit(0.5, 'lines'),
            strip.background = element_rect(color="black", fill="#FFFFFF", linetype="solid")
#             panel.grid.major=element_line(size=0.25, color=alpha('black', 0.25)),
#             panel.grid.minor=element_line(size=0.25, color=alpha('black', 0.25))
            )
    )
    if (col == 'host') {
        if (nrow(unique(data['host'])) > 13) {
            p <- (p +
                theme(
                #legend.position='none'
                )
            )
        }
    }
    else if (col == 'metric') {
        p <- (p 
            + scale_fill_jcolors("pal12")
        )
    }
    else if (col == 'name') {
    }
    
    if (view == 'jscore') {
        fn = sprintf('%s/jscore_%s.png', out_dir, col)
        ggsave(fn, width=10, height=2.5)
    }
    if (view == 'default') {
        p <- (
            p 
            + facet_grid(metric ~ .)
            + ylab('')
            + ylab('Score')
            + theme(
                legend.position='none',
                #strip.text.x = element_text(angle=0),
                strip.text.y = element_text(angle=0),
            )
        )
        fn = sprintf('%s/default_%s.png', out_dir, col)
        ggsave(fn, width=7, height=7)
    }
    else if (view == 'nscore') {
        p <- (
            p 
            + facet_grid(host ~ .)
            + ylab('NScore')
            + theme(
                #legend.position='none'
            )
        )
        if (col == 'name') {
            p <- p + theme(legend.position = 'bottom')
        }
        fn = sprintf('%s/nscore_%s.png', out_dir, col)
        ggsave(fn, width=4, height=4)
    }
    else if (view == 'mscore') {
        p <- (
            p 
            + facet_grid(host ~ metric)
            + ylab('MScore')
            + theme(
                legend.position='none',
                axis.text.x = element_text(angle=90, hjust=1)
            )
        )
        fn = sprintf('%s/mscore_%s.png', out_dir, col)
        ggsave(fn, width=8, height=4)
    }
}




# Create 10 minutes segments
cat_data <- rename_metrics(read.csv(config[['cat_fn']])) # categorized data
cat_data['rmin'] = cat_data['runtime'] / 60 # runtime in minutes
duration = max(ceiling(cat_data['rmin']))
bins = seq(0, duration, graph_config[['seg_size']] )


d2 <- cat_data %>% 
    group_by(cat) %>% 
    mutate(bin = cut(rmin, breaks = bins, labels = bins[-1])) 
d3 <- d2 %>%
    group_by(name, metric, host, bin) %>%
    summarise(score = sum(cat))

x_breakpoints <- bins[seq(1, length(bins), graph_config[['x_breakpoint_interval']])]

for (col in graph_config[['cols']]) {
    for (view in graph_config[['views']]) {
        visualize_categories (global, config, d3, view, col, x_breakpoints)
    }
}


## TODO
#visualize_rawdata <- function(data) {
#}

#pickle_data <- rename_metrics(read_pickle_file(config[['raw_fn']])) # raw data
#print(head(pickle_data))
#offset = min(pickle_data$timestamp)
#dat = pickle_data[complete.cases(pickle_data),]
#dat$runtime = dat$timestamp - offset
#dat['rmin'] = dat['runtime'] / 60 # runtime in minutes

#visualize_rawdata(dat)