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## ---- message = FALSE---------------------------------------------------------
library(dplyr)
library(photosynthesis)
round_to_nearest = function(x, values) {
sapply(x, function(y, values) {
values[which.min(abs(y - values))]
}, values = values)
}
# Read in data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
read.csv() |>
# Convert RH to a proportion
mutate(
RH = RHcham / 100,
PPFD = round_to_nearest(Qin, c(25, 50, 75, 100, 125, 375, 750, 1500))
) |>
rename(A_net = A, C_air = Ca, g_sw = gsw, VPD = VPDleaf)
# Fit the Ball-Berry stomatal conductance models
fit = fit_gs_model(
data = filter(dat, PPFD == 750),
model = c("BallBerry")
)
# Look at BallBerry model summary:
summary(fit[["BallBerry"]][["Model"]])
# Look at BallBerry parameters
fit[["BallBerry"]][["Parameters"]]
# Look at BallBerry plot
# fit[["BallBerry"]][["Graph"]]
# Fit many g_sw models
fits = fit_many(dat, funct = fit_gs_model, group = "PPFD", progress = FALSE)
# Look at the Medlyn_partial outputs at 750 PAR
# Model summary
summary(fits[["750"]][["Medlyn_partial"]][["Model"]])
# Model parameters
fits[["750"]][["Medlyn_partial"]][["Parameters"]]
# Graph
# fits[["750"]][["Medlyn_partial"]][["Graph"]]
# Compile parameter outputs for BallBerry model
# Note that it's the first element for each PAR value
# First compile list of BallBerry fits
bbmods = compile_data(data = fits, output_type = "list", list_element = 1)
# Now compile the parameters (2nd element) into a dataframe
bbpars = compile_data(data = bbmods, output_type = "dataframe", list_element = 2)
#Compile graphs
graphs = compile_data(data = bbmods, output_type = "list", list_element = 3)
# Look at 3rd graph
# graphs[[3]]
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