R/calculateCovariances3DAnemometer.R

In Agriculturist/EmissionsHelper: Emissions Modeling Support Functions Package

#' calculateCovariances3DAnemometer
#'
#' This function is designed to accept raw data from a 3D anemometer and 
#' divide the data into time segments.  This data should consist of a 
#' time, a verticle and two cardinal horizontal wind directions.  
#' 
#' For each time segment the   
#' 
#' @param windDataframe the data frame for wind data
#' @param timeVariableName the name of the time variable
#' @param UVariableName the column name of wind in the X direction 
#' @param VVariableName the column name of wind in the Y direction  
#' @param WVariableName the column name of wind in the Z direction 
#' @param SonicTemperatureVariableName the column name of the sonic temperature 
#' @param timeIntervalType is set to "ceiling" or "floor" depending on where to mark the samples
#' @param timeIntervalValue the value which describes the length of the time segment  
#' @param timeIntervalUnits the units for the length of the time segment
#' @keywords time 3DAnemometer
#' @examples
#'  
#'    calculateCovariances3DAnemometer()
#' @export

calculateCovariances3DAnemometer <- function(windDataframe, 
                                             timeVariableName = "time",
                                             UVariableName = "U",
                                             VVariableName = "V",
                                             WVariableName = "W",
                                             SonicTemperatureVariableName = "T", 
                                             timeIntervalType = "ceiling",
                                             timeIntervalValue = 15, 
                                             timeIntervalUnits = "minutes"
                                             
                                            ){
  
  require(plyr)
  timeIntervalType <- tolower(timeIntervalType)

  if (timeIntervalType == "ceiling"){
    windDataframe$timeInterval <-  timeCeiling(windDataframe[[timeVariableName]],
                                                    timeIntervalValue,
                                                    timeIntervalUnits)
  }else{
    windDataframe$timeInterval <-  timeFloor(windDataframe[[timeVariableName]],
                                                    timeIntervalValue,
                                                    timeIntervalUnits)
  }
  
  outputDataframe <- ddply(windDataframe, .variables = .(timeInterval), 
                           .fun = function(xx){return(
                             data.frame(
                               U = mean(xx[[UVariableName]]),
                               V = mean(xx[[VVariableName]]),
                               W = mean(xx[[WVariableName]]),
                               T = mean(xx[[SonicTemperatureVariableName]]),
                               UU = cov(xx[[UVariableName]],xx[[UVariableName]]),
                               UV = cov(xx[[UVariableName]],xx[[VVariableName]]),
                               UW = cov(xx[[UVariableName]],xx[[WVariableName]]),
                               UT = cov(xx[[UVariableName]],xx[[SonicTemperatureVariableName]]),
                               VV = cov(xx[[VVariableName]],xx[[VVariableName]]),
                               VW = cov(xx[[VVariableName]],xx[[WVariableName]]),
                               VT = cov(xx[[VVariableName]],xx[[SonicTemperatureVariableName]]),
                               WW = cov(xx[[WVariableName]],xx[[WVariableName]]),
                               WT = cov(xx[[WVariableName]],xx[[SonicTemperatureVariableName]]),
                               TT = cov(xx[[SonicTemperatureVariableName]],xx[[SonicTemperatureVariableName]])
                             ))})
  
  
  return(outputDataframe)
}