source("packrat/init.R")

library(dplyr)
library(entropy)
library(robustbase)

filter_by_day_segment <- function(data, day_segment) {
  if(day_segment %in% c("morning", "afternoon", "evening", "night"))
    data <- data %>% filter(local_day_segment == day_segment)

  return(data %>% group_by(local_date))
}

compute_call_feature <- function(calls, metric, day_segment){
  calls <- calls %>% filter_by_day_segment(day_segment)
  feature <- switch(metric,
       "count" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := n()),
       "distinctcontacts" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := n_distinct(trace)),
       "meanduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := mean(call_duration)),
       "sumduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := sum(call_duration)),
       "hubermduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := huberM(call_duration)$mu),
       "varqnduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := Qn(call_duration)),
       "entropyduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := entropy.MillerMadow(call_duration)))
  return(feature)
}

calls <-  read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
day_segment <- snakemake@params[["day_segment"]]
metrics <-  snakemake@params[["metrics"]]
type <- snakemake@params[["call_type"]]
features = data.frame(local_date = character(), stringsAsFactors = FALSE)

calls <- calls %>% filter(call_type == ifelse(type == "incoming", "1", ifelse(type == "outgoing", "2", "3")))

for(metric in metrics){
  feature <- compute_call_feature(calls, metric, day_segment)
  features <- merge(features, feature, by="local_date", all = TRUE)
}

write.csv(features, snakemake@output[[1]], row.names = FALSE)