Add single-step leaf-level P-model function

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: rsofun
 Title: The P-Model and BiomeE Modelling Framework
-Version: 5.1.0
+Version: 5.1.0.9000
 Authors@R: c(
             person(
               family = "Stocker",
@@ -82,6 +82,7 @@ Suggests:
     ggplot2,
     knitr,
     sensitivity,
+    rpmodel,
     gridExtra,
     patchwork
 VignetteBuilder: knitr

NAMESPACE

@@ -8,6 +8,7 @@ export(cost_rmse_biomee)
 export(cost_rmse_pmodel)
 export(run_biomee_f_bysite)
 export(run_pmodel_f_bysite)
+export(run_pmodel_onestep_f_bysite)
 export(runread_biomee_f)
 export(runread_pmodel_f)
 import(BayesianTools)

NEWS.md

@@ -1,3 +1,7 @@
+# rsofun (development version)
+
+* New `run_pmodel_onestep_f_bysite()` adds single-step leaf-level simulations of the P-model.
+
 # rsofun 5.1.0
 
 ## New features

R/calib_sofun.R

@@ -95,18 +95,18 @@ calib_sofun <- function(
   lower <- upper <- out_optim <- NULL
 
   # check input variables
-  if(missing(obs) | missing(drivers) | missing(settings)){
+  if (missing(obs) | missing(drivers) | missing(settings)){
     stop("missing input arguments, please check all parameters")
   }
 
   # check data structure
-  if(is.data.frame(obs)){
+  if (is.data.frame(obs)){
     if (nrow(obs) == 0){
       warning("no validation data available, returning NA parameters")
       return(lapply(settings$par,
                     function(x) NA))
     }
-  }else{
+  } else {
     stop("obs must be a (nested) data.frame")
   }
 
@@ -131,9 +131,10 @@ calib_sofun <- function(
       drivers = drivers,
       ...
     )
-    if(optim_out){
+
+    if (optim_out){
       out_optim <- list(par = out$par, mod = out)
-    }else{
+    } else {
       out_optim <- list(par = out$par)
     }
 
@@ -163,14 +164,15 @@ calib_sofun <- function(
     # setup the bayes run, no message forwarding is provided
     # so wrap the function in a do.call
     setup <- BayesianTools::createBayesianSetup(
-      likelihood = function(
-    random_par) {
-        do.call("cost",
-                list(
-                  par = random_par,
-                  obs = obs,
-                  drivers = drivers
-                ))
+      likelihood = function(random_par){
+        do.call(
+          "cost",
+          list(
+            par = random_par,
+            obs = obs,
+            drivers = drivers
+          )
+        )
       },
     prior = priors,
     names = names(settings$par)
@@ -189,9 +191,10 @@ calib_sofun <- function(
     # drop last value
     bt_par <- BayesianTools::MAP(out)$parametersMAP
     bt_par <- bt_par[1:(length(bt_par))]
-    if(optim_out){
+
+    if (optim_out){
       out_optim <- list(par = bt_par, mod = out)
-    }else{
+    } else {
       out_optim <- list(par = bt_par)
     }
 

R/data.R

@@ -26,7 +26,7 @@
 #'       \item{tmax}{Daily maximum air temperature in \eqn{^\circ}C.}
 #'       \item{fapar}{Fraction of photosynthetic active radiation (fAPAR), taking
 #'      values between 0 and 1.}
-#'       \item{co2}{Atmospheric CO\eqn{_2} concentration.}
+#'       \item{co2}{Atmospheric CO\eqn{_2} concentration in ppm.}
 #'       \item{ccov}{Cloud coverage in \%. This is only used when either PPFD or
 #'       net radiation are not prescribed.}
 #'       }
@@ -214,13 +214,13 @@
 #'     }}
 #'   \item{forcing}{Forcing data.frame used as input
 #'     \describe{
-#'       \item{ppfd}{Photosynthetic photon flux density (mol s-1 m-2)}
+#'       \item{ppfd}{Photosynthetic photon flux density (mol m-2 s-1)}
 #'       \item{tair}{Air temperature (deg C)}
 #'       \item{vpd}{Vapor pressure deficit (Pa)}
 #'       \item{rain}{Precipitation (kgH2O m-2 s-1 == mm s-1)}
 #'       \item{wind}{Wind velocity (m s-1)}
-#'       \item{pair}{Atmospheric pressure (pa)}
-#'       \item{co2}{CO2 atmospheric concentration (ppm)}
+#'       \item{pair}{Atmospheric pressure (Pa)}
+#'       \item{co2}{Atmospheric CO\eqn{_2} concentration in ppm.}
 #'     }}
 #'   \item{params_tile}{Tile-level model parameters, into a single row data.frame, including the following data:
 #'     \describe{

R/run_biomee_f_bysite.R

@@ -271,13 +271,13 @@ run_biomee_f_bysite <- function(
     n_annual_trans   = as.integer(n_annual_trans)
   )
 
-  out <- build_out(biomeeout, init_lu$name, sitename, params_siml$do_daily_diagnostics)
+  out <- build_out_biomee(biomeeout, init_lu$name, sitename, params_siml$do_daily_diagnostics)
 
   return(out)
 }
 
 # Build R output
-build_out <- function(biomeeout, lu_names, sitename, do_daily_diagnostics){
+build_out_biomee <- function(biomeeout, lu_names, sitename, do_daily_diagnostics){
   if (do_daily_diagnostics) {
     # If simulation is very long, output gets massive.
     # E.g., In a 3000 years-simulation 'biomeeout' is 11.5 GB.