R/reconstituteTable.R

In poales/msuRACiFit: Fitting A/Ci data

#' Reconstitute data and pick limitations
#' 
#' Take fit data and send it back through to build a table with limiting curves
#' @param data The original A/Ci data
#' @param fitParams The returned params data from the fitting function
#' @param name_assimilation The name given to assimilation column in "data"
#' @param name_ci The name given to the internal CO2 concentration column in "data"
#' @param gammastar The Cc compensation point. Default: Tobacco.
#' @param O2 The oxygen concentration in parts per hundred.
#' @param pressure Atmospheric pressure in kPa
#' @param tleaf The leaf temperature, in celsius
#' @param ignoreTPU Whether to fit TPU or not. Leave false if you don't know what you're doing!
#' @param Kc Michaelis-menten kinetic parameter for carboxylation
#' @param Ko Michaelis-menten kinetic parameter for oxygenation
#' @name reconstituteTable
#' @export


reconstituteTable <- function(data,fitParams,name_assimilation="A", name_ci=c("pCi","Ci"),gammastar=3.52,O2=21,pressure=101,tleaf=25,ignoreTPU=F,Kc=exp(35.9774-(80.99 / (0.008314*(273.15 + tleaf)))),
                              Ko=exp(12.3772-(23.72 / (0.008314*(273.15 + tleaf))))){
  if(!tibble::is_tibble(data)){
    data <- tibble::tibble(data)
  }
  locs <- match(tolower(name_ci),tolower(colnames(data)))
  loc <- min(stats::na.omit(locs))
  pCi <- data[,loc]
  
  if(!grepl(pattern="p",tolower(colnames(pCi)))){
    pCi <- pCi /1000000*1000*pressure
  }
  pCi <- pCi[[1]]
  AData <- data[name_assimilation]
  coef <- with(fitParams,{
    CoefFunc(ag, gammastar, unlist(pCi) - unlist(AData)/gm)
  })
  data2 <- with(fitParams,{
    tibble::tibble(A = unlist(AData), "pCi" = pCi,"Cc"=unlist(pCi) - unlist(AData)/gm)
  })
  cdat <- with(fitParams,{
    tibble::tibble(A=AcFunc(data2$Cc,rL,VcMax,Kc,Ko,O2,coef),Cc = data2$Cc)
  })
  jdat <- with(fitParams,{
    tibble::tibble(A=AjFunc(data2$Cc,ag,as,rL,J,gammastar,coef),Cc=data2$Cc)
  })
  pdat <- with(fitParams,{
    tibble::tibble(A=ApFunc(data2$Cc,ag,as,rL,TPU,gammastar,coef),Cc=data2$Cc)
  })
  # data2 <- tibble::tibble(A = unlist(AData), "pCi" = pCi,"Cc"=unlist(pCi) - unlist(AData)/gm)
  # cdat <- tibble::tibble(A=AcFunc(data2$Cc,ag,as,rd,vcmax,j,tpu,gm,Kc,Ko,O2,gammastar),Cc = data2$Cc)
  # jdat <- tibble::tibble(A=AjFunc(data2$Cc,ag,as,rd,vcmax,j,tpu,gm,gammastar),Cc=data2$Cc)
  # pdat <- tibble::tibble(A=ApFunc(data2$Cc,ag,as,rd,vcmax,j,tpu,gm,gammastar),Cc=data2$Cc)
  #remap data rq
  #now I have to pick limitations and create a column with that data
  if(ignoreTPU){
    data2 <- tibble::add_column(data2,"Rubisco Limited" = cdat$A,"ET Limited" = jdat$A)
    data2 <- tibble::add_column(data2, "Limiting process" = apply(data2[,4:5],1,FUN = which.min))
  }else{
    data2 <- tibble::add_column(data2,"Rubisco Limited" = cdat$A,"ET Limited" = jdat$A, "TPU Limited" = pdat$A)
    data2 <- tibble::add_column(data2, "Limiting process" = apply(data2[,4:6],1,FUN = which.min))
  }
  
  #now we have to show individual residuals
  limiting_values <-c()
  for(i in 1:nrow(data2)){
    limiting_values[i] <- unname(data2[i,data2$`Limiting process`[i]+3])
  }
  limiting_values <- dplyr::bind_cols(unlist(limiting_values))
  data2 <- tibble::add_column(data2,residual = data2$A-limiting_values$...1)
  data2 <- tibble::add_column(data2,`res^2`=data2$residual^2)
  
  return(data2)
}