##############################################################################################
#' @title Compute combined and expanded (95% confidence) temporally aggregated L1 uncertainty 
#' due to natural variation, calibration (multiplier), and voltage-based FDAS measurement
#' @author
#' Mija Choi \email{choim@battelleecology.org}

#' @description
#' Wrapper function. Compute the combined and expanded (95% confidence) temporally aggregated L1 
#' uncertainty for a set of values subject to natural variation, calibration uncertainty, and 
#' voltage-based FDAS uncertainty. Uncertainty due to  natural variation is estimated from the 
#' standard error of the mean. Uncertainty due to calibration is represented by a multiplier 
#' (U_CVALA3 (%) coefficient provided by CVAL) to the maximum L0' value. Uncertainty due to 
#' voltage-based FDAS measurement is computed from NEONprocIS.stat::def.ucrt.dp01.fdas.

#' @param data Data frame of L0' (calibrated) data. Must contain columns \code{readout_time} (POSIX) and 
#' whatever variable is specified in input parameter \code{VarUcrt} (numeric).
#' A single aggregated uncertainty for the selected variable \code{VarUcrt} will be computed over the full timeseries.
#' @param VarUcrt A character string of the target variable (column) in the data frame \code{data} for 
#' which uncertainty data will be computed (all other columns will be ignored in this function). 
#' @param ucrtCoef A list of uncertainty coefficients, each a list containing at a minimum the list 
#' elements: term (name of L0' term for which the coefficient applies - string), start_date (POSIX), 
#' end_date(POSIX), Name (of the coefficient - string), and 
#' Value (of the coefficient - string or numeric, to be interpreted as numeric). 
#' This will be passed into the calibration and FDAS uncertainty functions. Calibration uncertainty 
#' requires the U_CVALA3 coefficient. Voltage-based FDAS uncertainty requires U_CVALV3 and U_CVALV4 
#' coefficients.
#' @param ucrtData A data frame of L0' individual measurement uncertainty data generated by the 
#' calibration step. This will be passed into the FDAS uncertainty function NEONprocIS.stat::def.ucrt.dp01.fdas. 
#' See that function's inputs for required columns.
#' @param log A logger object as produced by NEONprocIS.base::def.log.init to produce structured log
#' output. Defaults to NULL, in which the logger will be created and used within the function.

#' @return A single numeric value representing the aggregated L1 calibration uncertainty over the full record. 
#' Numeric NA is returned if no applicable coefficient is found, with warning.

#' @references
#' License: (example) GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007
#' NEON.DOC.000785 TIS Level 1 Data products Uncertainty Budget Estimation Plan
#' NEON.DOC.000746 Calibration Fixture and Sensor Uncertainty Analysis: CVAL 2014 Uncertainty Manual

#' @keywords calibration, uncertainty, fdas L1, average

#' @examples
#' data <- data.frame(readout_time=as.POSIXct(c('2019-01-01 00:00','2019-01-01 00:01','2019-01-01 00:02'),tz='GMT'),
#'                    linePAR=c(827.7,827.8,831.3))
#' ucrtCoef <- list(list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALA3',Value='0.0388'),
#'                  list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALV3',Value='0.000374'),
#'                  list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALV4',Value='0.0000020'))
#' ucrtData <- data.frame(readout_time=as.POSIXct(c('2019-01-01 00:00','2019-01-01 00:01','2019-01-01 00:02'),tz='GMT'),
#'                        linePAR_raw=c(0.0064085,0.006409,0.0064097),
#'                        linePAR_dervCal=c(129153.7,129153.7,129153.7),
#'                        linePAR_ucrtComb=c(0.57992,0.57998,0.57998),
#'                        stringsAsFactors=FALSE)
#' ucrt <- NEONprocIS.stat::wrap.ucrt.dp01.cal.mult.fdas.volt(data=data,VarUcrt='linePAR',ucrtCoef=ucrtCoef,ucrtData=ucrtData)

#' @seealso \link[NEONprocIS.stat]{wrap.ucrt.dp01}
#' @seealso \link[NEONprocIS.stat]{def.ucrt.dp01.cal.mult}
#' @seealso \link[NEONprocIS.stat]{def.ucrt.dp01.fdas}
#'
# changelog and author contributions / copyrights
#   Mija Choi (2020-12-02)
#     Original Creation
##############################################################################################
# Define test context
context("\n                       Unit test of wrap.ucrt.dp01.cal.cnst.fdas.volt.R\n")

# Unit test of wrap.ucrt.dp01.cal.cnst.fdas.volt.R
test_that("Unit test of wrap.ucrt.dp01.cal.cnst.fdas.volt.R", {
   data <- data.frame(readout_time=as.POSIXct(c('2019-01-01 00:00','2019-01-01 00:01','2019-01-01 00:02'),tz='GMT'),
                     linePAR=c(827.7,827.8,831.3))
   ucrtCoef <- list(list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALA3',Value='0.0388'),
                    list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALV3',Value='0.000374'),
                    list(term='linePAR',start_date=as.POSIXct('2019-01-01',tz='GMT'),end_date=as.POSIXct('2019-01-02',tz='GMT'),Name='U_CVALV4',Value='0.0000020'))
   ucrtData <- data.frame(readout_time=as.POSIXct(c('2019-01-01 00:00','2019-01-01 00:01','2019-01-01 00:02'),tz='GMT'),
                          linePAR_raw=c(0.0064085,0.006409,0.0064097),
                          linePAR_dervCal=c(129153.7,129153.7,129153.7),
                          linePAR_ucrtComb=c(0.57992,0.57998,0.57998),
                         stringsAsFactors=FALSE)
   ucrt_volt_returned <- NEONprocIS.stat::wrap.ucrt.dp01.cal.mult.fdas.volt(data=data,VarUcrt='linePAR',ucrtCoef=ucrtCoef,ucrtData=ucrtData)
  
   testthat::expect_true(!is.na(ucrt_volt_returned))
  
  # Sad Path 1 - - column readout_time missing
  
  data_oneLessCol <- subset(data, select = -readout_time)
  
  ucrt_volt_returned <- try(NEONprocIS.stat::wrap.ucrt.dp01.cal.mult.fdas.volt(
      data = data_oneLessCol,
      VarUcrt = 'linePAR',
      ucrtCoef = ucrtCoef,
      ucrtData = ucrtData
    ),silent = TRUE)
  
  testthat::expect_true(class(ucrt_volt_returned)[1] == "try-error")
  
  # Sad Path 2 - data input is not numeric
  
  data_notNumeric <- data
  data_notNumeric$linePAR <- as.character(data_notNumeric$linePAR)
  
  ucrt_volt_returned <- try(NEONprocIS.stat::wrap.ucrt.dp01.cal.mult.fdas.volt(
    data = data_notNumeric,
    VarUcrt = 'linePAR',
    ucrtCoef = ucrtCoef,
    ucrtData = ucrtData
  ),silent = TRUE)
  
  testthat::expect_true(class(ucrt_volt_returned)[1] == "try-error")
  
  
  # Sad Path 3 - When all parameters are NULL, returns NA
  
  data_empty <- c()
  ucrt_volt_returned <- try(NEONprocIS.stat::wrap.ucrt.dp01.cal.mult.fdas.volt(
    data = data_empty,
    VarUcrt = 'linePAR',
    ucrtCoef = NULL,
    ucrtData = NULL
  ), silent = TRUE)
  testthat::expect_true(class(ucrt_volt_returned)[1] == "try-error")
})
