Stonefly_EAG/Analysis/functions/responseDynamics.R

##' Function to Calculate Various Parameters of Response Dynamics
##' 
##' The following parameters are calculated: 
##' Time to 10% of Response Maximum (Minimum), 
##' Time to 90% of Response Maximum (Minimum),
##' Time to Response Maximum (Minimum), 
##' Time to 90% of Response Maximum (Minimum) after Max (Min),
##' Time to 10% of Response Maximum (Minimum) after Max (Min),
##' Risetime to Maximum (Minimum),
##' Falltime from Maximum (Minimum),
##' Mean Response Maximum (Minimum).
##' 
##' @param x Input dataframe with one row per 5 sec trace
##' @param from Response starting percentage for Risetime calculation,
##'             defaults to 10 percent of response maximum (or minimum, respectively).
##' @param to Response ending percentage for Risetime calculation,
##'           defaults to 90 percent of response maximum (or minimum, respectively).
##' 
##' @return New columns with calculated response dynamic parameters
##' 
##
responseDynamics <- function(x, from=0.1, to=0.9)  {
  
  ## sub_sub is the time window where the Max and Min is expected
  ## starting 50ms after valve opening)
  avo <- 50 ## After Valve Opening
  
  sub_sub <- x[(avo+1) :1000]
  
  ## win is the window size before and after time of Max (or Min) to calculate the mean of the response
  win <- 25
  
  ## Max is the time pint of maximum response
  Max <- which(x==max(sub_sub))[1] + avo
  
  Max10_1 <- which(x <= max(x * from))
  Max10_1 <- Max10_1[which(Max10_1 <= Max)]
  Max10_1 <- Max10_1[length(Max10_1)]
  
  Max90_1 <- which(x <= max(x * to))
  Max90_1 <- Max90_1[which(Max90_1 <= Max)]
  Max90_1 <- Max90_1[length(Max90_1)]
  
  Max10_2 <- which(x <= max(x * from))
  Max10_2 <- Max10_2[which(Max10_2 >= Max)]
  Max10_2 <- Max10_2[1]
  
  Max90_2 <- which(x <= max(x * to))
  Max90_2 <- Max90_2[which(Max90_2 >= Max)]
  Max90_2 <- Max90_2[1]
  
  
  Mean_max <- mean(x[(Max - win + 1) : (Max + win)])
  
  
  ## Min is the time pint of minimum response
  Min <- which(x==min(sub_sub))[1] + avo
  
  Min10_1 <- which(x >= min(x * from))
  Min10_1 <- Min10_1[which(Min10_1 <= Min)]
  Min10_1 <- Min10_1[length(Min10_1)]
  
  Min90_1 <- which(x >= min(x * to))
  Min90_1 <- Min90_1[which(Min90_1 <= Min)]
  Min90_1 <- Min90_1[length(Min90_1)]
  
  Min10_2 <- which(x >= min(x * from))
  Min10_2 <- Min10_2[which(Min10_2 >= Min)]
  Min10_2 <- Min10_2[1]
  
  Min90_2 <- which(x >= min(x * to))
  Min90_2 <- Min90_2[which(Min90_2 >= Min)]
  Min90_2 <- Min90_2[1]
  
  Mean_min <- mean(x[(Min - win + 1) : (Min + win)])
  
  
  
  
  Timeto10Max <- Max10_1
  Timeto10Min <- Min10_1
  
  
  Max_rise <- Max90_1 - Max10_1
  Max_fall <- Max10_2 - Max90_2
  
  Min_rise <- Min90_1 - Min10_1
  Min_fall <- Min10_2 - Min90_2
  
  
  if(length(Max10_1) < 1){
    Max10_1 <- NA
    Max_rise <- NA
  }
  
  if(length(Max90_1) < 1){
    Max90_1 <- NA
    Max_rise <- NA
  }
  
  if(length(Max90_2) < 1){
    Max90_2 <- NA
    Max_fall <- NA
    
  }
  
  if(length(Max10_2) < 1){
    Max10_2 <- NA
    Max_fall <- NA
  }
  
  
  if(length(Min10_1) < 1){
    Min10_1 <- NA
    Min_rise <- NA
  }
  
  if(length(Min90_1) < 1){
    Min90_1 <- NA
    Min_rise <- NA
  }
  
  if(length(Min90_2) < 1){
    Min90_2 <- NA
    Min_fall <- NA
    
  }
  
  if(length(Min10_2) < 1){
    Min10_2 <- NA
    Min_fall <- NA
  }
  
  
  
  df <- c(Max10_1,
          Max90_1,
          Max, 
          Max90_2,
          Max10_2,
          Max_rise,
          Max_fall,
          Min10_1,
          Min90_1,
          Min, 
          Min90_2,
          Min10_2,
          Min_rise,
          Min_fall,
          Mean_max,
          Mean_min)
  
  return(df)
  
}