R/chord.R

In ubitech/Physiognomica: Physiognomica - A Novel prometheus Data Analytics Profiler

#metrics are without dimensions
chord <- function(prometheus_url,periods,step,metrics,enriched){
  
      if (isTRUE(enriched)){
        print("i am enriched")
        metrics_list =data.frame(name=metrics,friendlyName=metrics,dimensions = stringr::str_extract(metrics, stringr::regex("\\{.*\\}")))
      }else{
        #enrichMaestroPrometheusMetricsWithDimensionsWithoutSession
        metrics_list <- Physiognomica::enrichMaestroPrometheusMetricsWithDimensionsWithoutSession(prometheus_url,metrics)
      }
      
      
      #start <- paste("&start=" ,start, sep="")
      #end <- paste("&end=" ,end, sep="")
      step <- paste("&step=" ,step, sep="")
      print("getCorrelogram")
      
      #for(i in metrics_list) {
       # print(i)
      #}
      datalist = list()
      for(i in 1:nrow(periods)) {
        #print(periods[i, "start"])
        start <- paste("&start=" ,periods[i, "start"], sep="")
        end <- paste("&end=" ,periods[i, "end"], sep="")
        #print(start)
        #print(end)
        
          finaldata <- data.frame()
          for(i in 1:nrow(metrics_list)) {
            row <- metrics_list[i,]
            metric_name <- row$name
            metric_friendlyName <- row$friendlyName
            
            dimensions <- row$dimensions
            print("dimensions")
            print(dimensions)
            mydata <- Physiognomica::convertPrometheusDataToTabularFormat(prometheus_url,metric_name,metric_friendlyName,dimensions,start,end,step)
            if (nrow(finaldata) == 0){ finaldata <- mydata
            }else{ 
              if (nrow(mydata) != 0){
                finaldata <- merge(x=finaldata,y=mydata, by.x = "timestamp") 
              }
            }
          }
          print("get final data")
          colnames(finaldata)
          datalist[[i]] <-finaldata
      }
      
      big_data = do.call(rbind, datalist)
      
      
      
      big_data[] <- lapply(big_data, function(x) {
        if(is.factor(x)) as.numeric(as.character(x)) else x
      })
      sapply(big_data, class)
      #big_data <- subset( big_data, select = -c(timestamp) )
      big_data$timestamp <-0
      
      #print("big_data")
      print(big_data)
      big_data <- Filter(function(x) sd(x) != 0, big_data)
      
      if(length(big_data)==0){
        
        stop("Not enough values so as to generate a correlogram!")
      }
      
      final_data_column_names <- colnames(big_data)
      final_data_column_size <- ncol(big_data)
      
      usq <- 0
      for(i in 1:final_data_column_size) {
        #print(i)
        usq[i] <- paste("m_" ,i, sep="")
      }
      print(usq)
      
      colnames(big_data)<-usq
      
      
      metrics_appendix <- data.frame(matrix(unlist(usq)),stringsAsFactors=FALSE)
      names(metrics_appendix) <- c("metric_number")
      metrics_appendix$friendlyName_with_dimensions=final_data_column_names
      metrics_appendix$name <- "NA"
      
      #enrich metrics_apendix
      metrics_name <- 0
      metrics_friendlyname <- 0
      for(i in 1:nrow(metrics_appendix)) {
        row_friendly_name <- metrics_appendix[i,]$friendlyName_with_dimensions
        
        a<- metrics_list[which(metrics_list$friendlyName_with_dimensions== row_friendly_name), ]
        print(toString(a$name))
        metrics_name[i] <- toString(a$name)
        metrics_friendlyname[i] <- toString(a$friendlyName)
      }
      metrics_appendix$name <- metrics_name
      metrics_appendix$friendlyName <- metrics_friendlyname
      
      
      write.csv(metrics_appendix, file = "metrics_appendix.csv")
      
      output = psych::corr.test(big_data)
      A = output$r    # matrix A here contains the correlation coefficients
      B = output$p   # matrix B here contains the corresponding p-values
      
      sig_matrix = ifelse(B < 0.01, A, 0)  
      sig_matrix[sig_matrix ==1] <- 0
      
      #--------ChordDiagram total--------------------------------------
      sig_matrix_total<-abs(sig_matrix)
      #sig_matrix_total[sig_matrix_total < 0.4] <- 0
      sig_matrix_total<-sig_matrix_total*100
      
      
      write.csv(sig_matrix_total, file = "correlation_matrix.csv")
      correlation_matrix_json <- jsonlite::toJSON(sig_matrix_total)
      write(correlation_matrix_json, "correlation_matrix.json")
      
      chorddiag::chorddiag(sig_matrix_total,groupnamePadding = 3,groupnameFontsize = 10,showZeroTooltips = FALSE,margin = 30,showTicks = FALSE)
      
      #---------------------------click_action_string-------------------------------------
      click_action_string = "
      //alert( JSON.stringify(d));  
      var source_index = d.source.index; 
      //alert(source_index);
      var target_index = d.target.index; 
      //alert(target_index);
      var chord_url = new URL(document.location);
      var chord_url_string = chord_url.toString();
      analytics_session_id = chord_url_string.split('tmp/').pop().split('/files/')[0];
      linear_regression_ip = chord_url_string.split('://').pop().split('/ocpu/')[0];
      //alert(analytics_session_id);
      var http = new XMLHttpRequest();
      var url = 'http://212.101.173.35/ocpu/library/Physiognomica/R/linear_regression_from_chord ';
      var params = 'source_index='+source_index+'&target_index='+target_index+'&metrics_appendix='+analytics_session_id;
      http.open('POST', url, true);
      
      //Send the proper header information along with the request
      http.setRequestHeader('Content-type', 'application/x-www-form-urlencoded');
      
      http.onreadystatechange = function() {//Call a function when the state changes.
      if(http.readyState == 4 && http.status == 201) {
      //alert(http.responseText);
      my_response = http.responseText;
      new_session_id = my_response.split('tmp/').pop().split('/files/')[0];
      //alert(new_session_id);
      window.open('http://'+linear_regression_ip+'/ocpu/tmp/'+new_session_id+'/files/linear_regression.html');
      }
      }
      http.send(params);" 
      
      
      
      #--------ChordDiagram positive--------------------------------------
      sig_matrix_positive <-sig_matrix
      sig_matrix_positive[sig_matrix_positive <0] <- 0
      sig_matrix_positive[sig_matrix_positive < 0.4] <- 0
      sig_matrix_positive<-sig_matrix_positive*100
      mychord_positive <- chorddiag::chorddiag(sig_matrix_positive,groupnamePadding = 3,groupnameFontsize = 10,showZeroTooltips = FALSE,margin = 30,showTicks = FALSE,tooltipUnit = " %",precision = 4, clickAction = click_action_string)
      
      #--------ChordDiagram negative--------------------------------------
      sig_matrix_negative <- sig_matrix
      sig_matrix_negative[sig_matrix_negative > 0] <- 0
      sig_matrix_negative[sig_matrix_negative > -0.4] <- 0
      sig_matrix_negative <- sig_matrix_negative*100
      sig_matrix_negative <-abs (sig_matrix_negative)
      
      mychord_negative <- chorddiag::chorddiag(sig_matrix_negative,groupnamePadding = 3,groupnameFontsize = 10,showZeroTooltips = FALSE,margin = 30,showTicks = FALSE,tooltipUnit = " %",precision = 4, clickAction = click_action_string)
      
      
      ititle1 <- 
        shiny::tags$div(shiny::tags$h3("Chord Diagramm with positive and statistical significant correlations"),
                        shiny::tags$h3(mychord_positive))
      
      ititle2 <- 
        shiny::tags$div(shiny::tags$h3("Chord Diagramm with negative and statistical significant correlation"),
                        shiny::tags$h3(mychord_negative))
      
      myvars <- c("metric_number", "friendlyName_with_dimensions")
      newdata <- metrics_appendix[myvars]
      names(newdata) <- c("metric_number", "metric_name")
      
      page_body <- shiny::tags$html(
        shiny::tags$body(
          ititle1,
          ititle2,
          #shiny::tags$div(style="clear:both"),
          tableHTML::tableHTML(newdata)
          #iframes
        )
      ) 
      #https://stackoverflow.com/questions/17748566/how-can-i-turn-an-r-data-frame-into-a-simple-unstyled-html-table
      htmltools::save_html(page_body,file = "correlation_page.html")
      
      newdata$prometheus <- prometheus_url
      newdata$start <- start
      newdata$end <- end
      newdata$step <- step
      
      return (newdata)
 
 
 
}