changes

secondDbSteps.r

@@ -9,18 +9,20 @@ 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")
   
+  # get from database
   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)
   
+  # get values from all location at one time in a row
   freq_wide <- frequencies %>% 
     pivot_wider(names_from = location, 
                 values_from = freq, 
                 values_fn = mean)
   
+  # remove measurement error (frequency gradient greater than THRESHOLD)
   THRESHOLD <- 0.5
-  
   for (colIdx in 2:length(freq_wide)) {
     last <- freq_wide[[1, colIdx]]
     for (rowIdx in 1:length(freq_wide[[colIdx]])) {
@@ -37,37 +39,56 @@ get_freq_df <- function(con, startDate, endDate) {
 
 con <- dbConnect(RPostgres::Postgres(), 
                  dbname='mainscnt', 
-                 host='db.mainscnt.eu', 
+                 host='172.16.10.27', 
                  user='wn')
 
 
-START <- "2021-08-03 07:00:00"
+START <- "2021-08-02 00:00:00"
 INTERVAL <- 3600
 
-for (offset in 0:0) {
+freq_deviation_integrals <- data.frame()
+
+for (offset in 0:23) {
   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="")
+  # get prepared and sanitized data from database
+  freq_wide <- get_freq_df(con, startDate, endDate)
+  # 
+  location_names <- names(freq_wide)[-1]
+  
+  for (colIdx in 1:length(location_names)) {
+    colName.mean <- paste("mean.w.o.", location_names[colIdx], sep="")
+    colName.diff <- paste(location_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)
+      mutate(!!colName.mean := mean(c_across(location_names[- colIdx]), na.rm=TRUE)) %>%
+      mutate(!!colName.diff := abs(eval(as.name(colName.mean)) - eval(as.name(location_names[colIdx]))) * 100)
     
   }
 
   means <- freq_wide %>% select(ends_with(".to.mean"))
-  means.means <- apply(means, 2, mean, na.rm=TRUE)
+  sum.means <- apply(means, 2, sum, na.rm=TRUE)
+  #printf("start: %s, end: %s\n", startDate, endDate)
+  #print(sum.means)
+  #printf("\n")
+
+  next.row.no <- nrow(freq_deviation_integrals) + 1
+  freq_deviation_integrals[next.row.no, c(1, 2)] <- c(strftime(startDate, "%Y-%m-%d %H:%M:%S", tz="UTC"), strftime(endDate, "%Y-%m-%d %H:%M:%S", tz="UTC"))
+  freq_deviation_integrals[next.row.no, c(3:(2 + length(sum.means)))] <- sum.means[order(names(sum.means))]
   
-  
-    # print(summary(freq_wide))
 }
-  
+
+names(freq_deviation_integrals) <- c("startDate", "endDate", sort(location_names))
+
+
+for (colIdx in 1:length(location_names)) {
+  freq_deviation_integrals[,ncol(freq_deviation_integrals)+1] <- c(0, diff(freq_deviation_integrals[,2+colIdx],1))
+  names(freq_deviation_integrals)[length(location_names)+2+colIdx] = paste("diff", sort(location_names)[colIdx], sep=".")
+}
+
+
 dbDisconnect(con)