library(DBI)
library(tidyr)
library(dplyr)
library(lubridate)
library(R.utils)


get_freq_df <- function(con, startDate, endDate) {
  startStr <- strftime(startDate, "%Y-%m-%d %H:%M:%S", tz="UTC")
  endStr <- strftime(endDate, "%Y-%m-%d %H:%M:%S", tz="UTC")
  
  res <-dbSendQuery(con, "select time, location, freq from mainsfrequency where valid=1 and time >= $1 and time < $2")
  dbBind(res, list(startStr, endStr))
  frequencies <- dbFetch(res)
  dbClearResult(res)
  
  freq_wide <- frequencies %>% 
    pivot_wider(names_from = location, 
                values_from = freq, 
                values_fn = mean)
  
  THRESHOLD <- 0.5
  
  for (colIdx in 2:length(freq_wide)) {
    last <- freq_wide[[1, colIdx]]
    for (rowIdx in 1:length(freq_wide[[colIdx]])) {
      current <- freq_wide[[rowIdx, colIdx]]
      if (!is.na(current) && !is.na(last) && (abs(current - last) > THRESHOLD)) {
        freq_wide[[rowIdx, colIdx]] = NA
      }
      last <- current
    }
  }

  return (freq_wide)  
}

con <- dbConnect(RPostgres::Postgres(), 
                 dbname='mainscnt', 
                 host='db.mainscnt.eu', 
                 user='wn')


START <- "2021-08-03 07:00:00"
INTERVAL <- 3600

for (offset in 0:0) {
  startDate <- ymd_hms(START) + INTERVAL * offset
  endDate <- startDate + INTERVAL

  freq_wide <- get_freq_df(con, startDate, endDate)
  freq_wide_names <- names(freq_wide)
  
  for (colIdx in 2:length(freq_wide)) {
    colName.mean <- paste("mean.w.o.", freq_wide_names[colIdx], sep="")
    colName.diff <- paste(freq_wide_names[colIdx], ".to.mean", sep="")
    freq_wide <- freq_wide %>%
      rowwise() %>%
      mutate(!!colName.mean := mean(c_across(freq_wide_names[c(-1, - colIdx)]), na.rm=TRUE)) %>%
      mutate(!!colName.diff := abs(eval(as.name(colName.mean)) - eval(as.name(freq_wide_names[colIdx]))) * 100)
    
  }

  means <- freq_wide %>% select(ends_with(".to.mean"))
  means.means <- apply(means, 2, mean, na.rm=TRUE)
  
  
    # print(summary(freq_wide))
}
  
dbDisconnect(con)