### Install photosynthesis from GitHub

Source: https://cran.r-project.org/web/packages/photosynthesis/readme/README.html

Install the photosynthesis package from GitHub using the 'remotes' package. Ensure 'remotes' is installed first.

```r
install.packages("remotes")
remotes::install_github("cdmuir/photosynthesis")
```

--------------------------------

### Install photosynthesis from CRAN

Source: https://cran.r-project.org/web/packages/photosynthesis/readme/README.html

Use this command to install the photosynthesis package from the Comprehensive R Archive Network (CRAN).

```r
install.packages("photosynthesis")
```

--------------------------------

### Minimum Worked Example with Default Parameters

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

Demonstrates how to simulate photosynthetic rate using default parameter settings with the `make_*()` functions and `photo()` from the photosynthesis package. Ensure 'dplyr', 'magrittr', and 'photosynthesis' libraries are loaded.

```R
library(dplyr)
library(magrittr)
library(photosynthesis)

# Leaving the make_* functions empty will automatically default to defaults
# parameters.
bake_par   = make_bakepar()                       # temperature response parameters
constants  = make_constants(use_tealeaves = FALSE) # physical constants
leaf_par   = make_leafpar(use_tealeaves = FALSE)   # leaf parameters
enviro_par = make_enviropar(use_tealeaves = FALSE) # environmental parameters

photo(leaf_par, enviro_par, bake_par, constants, quiet = TRUE,
      use_tealeaves = FALSE)
#>                 C_chl         value convergence                  g_tc
#> 1 258.1218 [umol/mol] -1.363227e-06           0 0.1726157 [mol/m^2/s]
#>                       A            J_max          J_max25              K_C
#> 1 27.94273 [umol/m^2/s] 200 [umol/m^2/s] 200 [umol/m^2/s] 268.3 [umol/mol]
#>              K_C25                 K_O               K_O25            R_d
#> 1 268.3 [umol/mol] 165084.2 [umol/mol] 165084.2 [umol/mol] 2 [umol/m^2/s]
#>            R_d25     T_leaf           V_cmax         V_cmax25            V_tpu
#> 1 2 [umol/m^2/s] 298.15 [K] 150 [umol/m^2/s] 150 [umol/m^2/s] 200 [umol/m^2/s]
#>            V_tpu25            g_mc          g_mc25            g_sc
#> 1 200 [umol/m^2/s] 0.4 [mol/m^2/s] 0.4 [mol/m^2/s] 0.4 [mol/m^2/s]
#>               g_uc         gamma_star       gamma_star25  k_mc  k_sc  k_uc
#> 1 0.01 [mol/m^2/s] 37.9258 [umol/mol] 37.9258 [umol/mol] 1 [1] 1 [1] 1 [1]
#>   leafsize     phi_J   theta_J          C_air              O              P
#> 1  0.1 [m] 0.331 [1] 0.825 [1] 420 [umol/mol] 0.21 [mol/mol] 101.3246 [kPa]
#>                PPFD      RH    wind          Ds_Jmax           Ds_gmc
#> 1 1500 [umol/m^2/s] 0.5 [1] 2 [m/s] 388.04 [J/K/mol] 487.29 [J/K/mol]
#>            Ea_Jmax            Ea_KC            Ea_KO            Ea_Rd
#> 1 56095.18 [J/mol] 80989.78 [J/mol] 23719.97 [J/mol] 40446.75 [J/mol]
#>           Ea_Vcmax          Ea_Vtpu     Ea_gammastar           Ea_gmc
#> 1 52245.78 [J/mol] 52245.78 [J/mol] 24459.97 [J/mol] 68901.56 [J/mol]
#>            Ed_Jmax           Ed_gmc             D_c0            D_h0
#> 1 121244.8 [J/mol] 148788.6 [J/mol] 1.29e-05 [m^2/s] 1.9e-05 [m^2/s]
#>               D_m0             D_w0           G                 R       eT
#> 1 1.33e-05 [m^2/s] 2.12e-05 [m^2/s] 9.8 [m/s^2] 8.31446 [J/K/mol] 1.75 [1]
#>     epsilon                sigma                 C_i
#> 1 0.622 [1] 5.67e-08 [W/K^4/m^2] 350.1432 [umol/mol]

```

--------------------------------

### Load photosynthesis package

Source: https://cran.r-project.org/web/packages/photosynthesis/readme/README.html

Load the installed photosynthesis package into your R session to use its functions.

```r
library("photosynthesis")
```

--------------------------------

### Setting up and Baking Leaf Parameters

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Demonstrates how to initialize and bake leaf parameters using various make functions. This is a prerequisite for calculating photosynthetic rates. The example shows both direct function calls and piping with the magrittr library.

```R
bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
leaf_par = bake(leaf_par, enviro_par, bake_par, constants)
# Or bake with piping (need library(magrittr))
# leaf_par %<>% bake(enviro_par, bake_par, constants)
enviro_par$T_air = leaf_par$T_leaf

pars = c(leaf_par, enviro_par, constants)
C_chl = set_units(350, umol/mol)

A_supply(C_chl, pars)

A_demand(C_chl, pars)
```

--------------------------------

### Data Manipulation and Plotting Setup

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

Prepares the data by dropping units and categorizing 'g[s]' based on 'g_sc' before plotting. This snippet sets up the data frame for visualization.

```r
ph %<>%
  mutate_if(~ is(.x, "units"), drop_units) %>%
  mutate(`g[s]` = ifelse(g_sc == 0.2, "low", "high"))
```

--------------------------------

### parameter_names

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Get a vector of parameter names for leaf, environmental, bake, or constants. This helps in understanding the available parameters for simulation.

```APIDOC
## parameter_names

### Description
Get vector of parameter names 

### Usage
```
parameter_names(which, use_tealeaves)
```

### Arguments
`which` |  A character string indicating which parameter names to retrieve: "leaf", "enviro", "bake", or "constants". Partial matching allowed.  
`use_tealeaves` |  Logical. Should leaf energy balance be used to calculate leaf temperature (T_leaf)? If TRUE, `tleaf()` calculates T_leaf. If FALSE, user-defined T_leaf is used. Additional parameters and constants are required, see `make_parameters()`.  

### Value
A character vector with parameter names associated with each type, "leaf", "enviro", "bake", or "constants". 

### Examples
```
parameter_names("leaf", use_tealeaves = FALSE)
```
```

--------------------------------

### Load and Prepare A-Ci Curve Data

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Reads A-Ci curve data from the package's example files and preprocesses it for fitting. This includes creating a grouping factor for light intensity and converting leaf temperature to Kelvin.

```R
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15
```

--------------------------------

### Fit Many Functions Across Groups (ACi Example)

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Fits a specified function (e.g., `fit_aci_response`) across different groups in the data. This example demonstrates fitting ACi response curves grouped by light intensity.

```R
# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data = read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 = as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf = data$Tleaf + 273.15

# Fit many curves
fits = fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  funct = fit_aci_response,
  group = "Q_2"
)

# Print the parameters
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
fits[[3]][[1]]

# Print the graph
fits[[3]][[2]]

# Compile graphs into a list for plotting
fits_graphs = compile_data(fits,
  list_element = 2
)


# Compile parameters into dataframe for analysis
fits_pars = compile_data(fits,
  output_type = "dataframe",
  list_element = 1
)
```

--------------------------------

### Parallel Processing with photosynthesis

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

To speed up simulations of many parameter sets, use `parallel = TRUE` in the `photosynthesis` function. Ensure you have set up a `future::plan()` before calling the function. This example simulates an A-CcA-Cc curve.

```r
# NOTE: parallel example is not evaluated because it was causing an issue with CRAN, but you can copy-and-paste the code to run on your own machine.

library(future)
plan("multisession") # Set up plan

# We'll use the `replace` argument to enter multiple atmospheric CO2 concentrations

bake_par = make_bakepar()
constants  = make_constants(use_tealeaves = FALSE)

enviro_par = make_enviropar(
  replace = list(
    C_air = set_units(seq(10, 2000, length.out = 20), umol / mol)
    ), use_tealeaves = FALSE
  )

leaf_par  = make_leafpar(use_tealeaves = FALSE)

ph = photosynthesis(leaf_par, enviro_par, bake_par, constants, 
                     use_tealeaves = FALSE, progress = FALSE, 
                     quiet = TRUE, parallel = TRUE)

# Plot C_c versus A
library(ggplot2)

## Drop units for plotting
ph %<>% mutate_if(~ is(.x, "units"), drop_units)
ggplot(ph, aes(C_chl, A)) +
  geom_line(size = 2) +
  xlab(expression(paste(C[chl], " [ppm] "))) +
  ylab(expression(paste("A [", mu, "mol ", m^-2~s^-1, "]"))) +
  theme_bw() +
  NULL

```

--------------------------------

### Get default model

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Get the name of the default model used for different plant ecophysiological data analysis methods implemented in photosynthesis. Currently only used for fit_aq_response2() and fit_r_light2().

```APIDOC
## get_default_model

### Description
Get the name of the default model used for different plant ecophysiological data analysis methods implemented in **photosynthesis**. Currently only used for `fit_aq_response2()` and `fit_r_light2()`.
**Light response models:**
  * `marshall_biscoe_1980()`: Non-rectangular hyperbolic model of light responses 
  * `photoinhibition()`: Non-rectangular hyperbolic model of light responses with photoinhibition of `k_sat` at increasing Q_abs 

### Usage
```R
get_default_model(.photo_fun)

get_all_models(method)

marshall_biscoe_1980(Q_abs, k_sat, phi_J, theta_J)

photoinhibition(Q_abs, k_sat, phi_J, theta_J, b_inh)
```

### Arguments
`.photo_fun` |  A character string of **photosynthesis** function to call. One of: `⁠aq_response, r_light`.  
---|---
`method` |  A character string of the statistical method to use: 'ls' for least-squares and 'brms' for Bayesian model using `brms::brm()`. Default is 'ls'.  
`Q_abs` |  Absorbed light intensity (`μ
μ
μ`mol m`−2^{-2}−2` s`−1^{-1}−1`)  
`k_sat` |  Light saturated rate of process k  
`phi_J` |  Quantum efficiency of process k  
`theta_J` |  Curvature of the light response  
`b_inh` |  Inhibition parameter  
### Value
A character string with name of model. 
### Examples
```R
get_default_model("aq_response")
get_default_model("r_light")
```
```

--------------------------------

### Get All Available Models

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Retrieves all available models for a given statistical method. The 'method' argument can be 'ls' for least-squares or 'brms' for Bayesian models.

```R
get_all_models(method)
```

--------------------------------

### Get Cuticular Conductance to CO2

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the cuticular conductance to CO2 (g_uc). Requires parameters and the leaf surface (lower or upper). Unitless calculations can be enabled.

```R
.get_guc(pars, surface, unitless)
```

--------------------------------

### Initialize Photosynthesis Parameters

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Sets up default parameters for leaf, environment, and constants for photosynthesis simulations. Allows for overriding specific leaf parameters like stomatal conductance (g_sc) and Vcmax at 25°C.

```r
bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)

leaf_par = make_leafpar(
  replace = list(
    g_sc = set_units(0.3, mol / m^2 / s),
    V_cmax25 = set_units(100, umol / m^2 / s)
  ), use_tealeaves = FALSE
)

```

--------------------------------

### Baking Leaf Parameters with Temperature Response

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Demonstrates how to initialize leaf, environmental, and bake parameters, then use the `bake` function to incorporate temperature sensitivity into leaf parameters. This is useful for simulating photosynthesis under varying temperatures.

```R
bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(
  replace = list(T_leaf = set_units(293.15, K)),
  use_tealeaves = FALSE
)
baked_leafpar = bake(leaf_par, enviro_par, bake_par, constants)

baked_leafpar$V_cmax25
baked_leafpar$V_cmax

```

--------------------------------

### Single Parameter Set with 'photo'

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Demonstrates how to use the 'photo' function with a single set of parameters to simulate photosynthesis.

```R
# Single parameter set with 'photo'

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviro_par = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
photo(leaf_par, enviro_par, bake_par, constants,
  use_tealeaves = FALSE
)
```

--------------------------------

### Get citation information

Source: https://cran.r-project.org/web/packages/photosynthesis/readme/README.html

Retrieve citation information for the photosynthesis package in R. This is useful for academic referencing.

```r
citation(package = 'photosynthesis')
```

--------------------------------

### Multiple Parameter Sets with 'photosynthesis'

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Illustrates using the 'photosynthesis' function to simulate gas exchange with multiple parameter sets, specifically varying leaf temperature.

```R
# Multiple parameter sets with 'photosynthesis'

leaf_par = make_leafpar(
  replace = list(
    T_leaf = set_units(c(293.14, 298.15), "K")
  ), use_tealeaves = FALSE
)
photosynthesis(leaf_par, enviro_par, bake_par, constants,
  use_tealeaves = FALSE
)
```

--------------------------------

### Replacing Default Photosynthesis Parameters

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

Demonstrates how to replace default parameters for photosynthesis simulation using the `replace` argument in `make_*()` functions. Ensure units are correctly specified using `set_units()`.

```R
# Use the `replace` argument to replace defaults. This must be a named list, and
# each named element must have the proper units specified. See `?make_parameters`
# for all parameter names and proper units.

# Temperature response parameters can be updated (but we won't do that here)
bake_par = make_bakepar()

# Physical constants probably do not need to be replaced in most cases,
# that's why we call them 'constants'!
constants  = make_constants(use_tealeaves = FALSE)

# First, we'll change photosynthetic photon flux density to 1000 umol / (m^2 s)
enviro_par = make_enviropar(
  replace = list(
    PPFD = set_units(1000, "umol/m^2/s")
    ), use_tealeaves = FALSE
  )

# Next, we'll change stomatal conductance to 0.3 mol / m^2 / s.
leaf_par = make_leafpar(
  replace = list(
    g_sc = set_units(0.3, mol / m^2 / s)
    ), use_tealeaves = FALSE
  )

photo <- photo(leaf_par, enviro_par, bake_par, constants, quiet = TRUE,
               use_tealeaves = FALSE)

photo |>
  select(PPFD, C_chl, A) |>
  knitr::kable()

```

--------------------------------

### Get Required Variables for Photosynthesis Models

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Retrieves the list of variables required for a specified photosynthesis model. Allows suppressing informational messages.

```R
required_variables(.model, quiet)
```

--------------------------------

### Get Default Functions for Calculated Parameters

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Retrieves default functions for calculated parameters in photosynthesis. This function is used internally or for advanced customization.

```r
get_f_parameter(.f_name)
```

--------------------------------

### Get Stomatal Conductance to CO2

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the stomatal conductance to CO2 (g_sc). It requires parameters and the leaf surface, and offers a unitless calculation option.

```R
.get_gsc(pars, surface, unitless)
```

--------------------------------

### Get Mesophyll Conductance to CO2

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the mesophyll conductance to CO2 (g_mc). This function takes parameters and the leaf surface as input, with an option for unitless calculations.

```R
.get_gmc(pars, surface, unitless)
```

--------------------------------

### Read and Prepare Data for Photosynthesis Analysis

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/light-respiration.html

Reads a CSV file and prepares the data by rounding CO2 concentrations and renaming columns for use in photosynthesis models.

```r
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |> 
  read.csv() |> 
  mutate(group = photosynthesis:::round_to_nearest(CO2_s, values = c(50, 100, 200, 300, 410, 600, 820, 1200, 1600))) |> 
  rename(A_net = A, PPFD = Qin, phi_PSII = PhiPS2, C_i = Ci)
```

--------------------------------

### Make Lists of Parameters for Photosynthesis

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Functions to create parameter lists for various components used in the photosynthesis package. Includes options to replace default values and specify whether to use tealeaves for calculations.

```R
make_leafpar(replace = NULL, use_tealeaves)

make_enviropar(replace = NULL, use_tealeaves)

make_bakepar(replace = NULL)

make_constants(replace = NULL, use_tealeaves)
```

--------------------------------

### Get Total Conductance to CO2

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the total conductance to CO2 (g_tc). This function can optionally use a legacy version and allows control over unit handling for performance.

```R
.get_gtc(pars, unitless, use_legacy_version)
```

--------------------------------

### Fitting Multiple Stomatal Conductance Models

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/stomatal-conductance.html

Fits various stomatal conductance models to the dataset, grouped by PPFD, and suppresses the progress bar for cleaner output.

```R
# Fit many g_sw models
fits = fit_many(dat, funct = fit_gs_model, group = "PPFD", progress = FALSE)
```

--------------------------------

### photosynthesis

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Simulate C3 photosynthesis over multiple parameter sets using the Farquhar-von Caemmerer-Berry model.

```APIDOC
## photosynthesis

### Description
`photosynthesis`: simulate C3 photosynthesis over multiple parameter sets 
`photo`: simulate C3 photosynthesis over a single parameter set 

### Usage
```
photosynthesis(
  leaf_par,
  enviro_par,
  bake_par,
  constants,
  use_tealeaves,
  progress = TRUE,
  quiet = FALSE,
  assert_units = TRUE,
  check = TRUE,
  parallel = FALSE,
  use_legacy_version = FALSE
)

photo(
  leaf_par,
  enviro_par,
  bake_par,
  constants,
  use_tealeaves,
  quiet = FALSE,
  assert_units = TRUE,
  check = TRUE,
  prepare_for_tleaf = use_tealeaves,
  use_legacy_version = FALSE
)
```

### Arguments
`leaf_par` |  A list of leaf parameters inheriting class `leaf_par`. This can be generated using the `make_leafpar` function.  
`enviro_par` |  A list of environmental parameters inheriting class `enviro_par`. This can be generated using the `make_enviropar` function.  
`bake_par` |  A list of temperature response parameters inheriting class `bake_par`. This can be generated using the `make_bakepar` function.  
`constants` |  A list of physical constants inheriting class `constants`. This can be generated using the `make_constants` function.  
`use_tealeaves` |  Logical. Should leaf energy balance be used to calculate leaf temperature (T_leaf)? If TRUE, `tleaf()` calculates T_leaf. If FALSE, user-defined T_leaf is used. Additional parameters and constants are required, see `make_parameters()`.  
`progress` |  Logical. Should a progress bar be displayed?  
`quiet` |  Logical. Should messages be displayed?  
`assert_units` |  Logical. Should parameter `units` be checked? The function is faster when FALSE, but input must be in correct units or else results will be incorrect without any warning.  
`check` |  Logical. Should arguments checks be done? This is intended to be disabled when `photo()` is called from `photosynthesis()` Default is TRUE.  
`parallel` |  Logical. Should parallel processing be used via `furrr::future_map()`?  
`use_legacy_version` |  Logical. Should legacy model (<2.1.0) be used? See NEWS for further information. Default is FALSE.  
`prepare_for_tleaf` |  Logical. Should arguments additional calculations for `tleaf()`? This is intended to be disabled when `photo()` is called from `photosynthesis()`. Default is `use_tealeaves`.  

### Details
`photo`: This function takes simulates photosynthetic rate using the Farquhar-von Caemmerer-Berry (`FvCB()`) model of C3 photosynthesis for single combined set of leaf parameters (`leaf_par()`), environmental parameters (`enviro_par()`), and physical constants (`constants()`). Leaf parameters are provided at reference temperature (25 °C) and then "baked" to the appropriate leaf temperature using temperature response functions (see `bake()`).   
  
`photosynthesis`: This function uses `photo` to simulate photosynthesis over multiple parameter sets that are generated using `cross_df()`.   
```

--------------------------------

### Get Photosynthesis Parameter Names

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Retrieves a vector of parameter names for a specified category (leaf, enviro, bake, or constants). Useful for understanding the available parameters within each group.

```r
parameter_names("leaf", use_tealeaves = FALSE)

```

--------------------------------

### Get Default Model for Photosynthesis Functions

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Retrieves the default model name used for specific photosynthesis functions like fit_aq_response2() and fit_r_light2(). Supported functions are 'aq_response' and 'r_light'.

```R
get_default_model("aq_response")
get_default_model("r_light")
```

--------------------------------

### Fit Single and Multiple Photosynthesis Light-Response Curves

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Demonstrates fitting a single light-response curve using `fit_photosynthesis` and then fitting multiple curves by splitting the data. Requires the `broom`, `dplyr`, and `photosynthesis` packages.

```R
library(broom)
library(dplyr)
library(photosynthesis)

# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |>
  read.csv() |>
  # Set grouping variable
  mutate(group = round(CO2_s, digits = 0)) |>
  # For this example, round sequentially due to CO2_s set points
  mutate(group = as.factor(round(group, digits = -1)))

# Fit one light-response curve
fit = fit_photosynthesis(
  .data = filter(dat, group == 600),
  .photo_fun = "aq_response",
  .vars = list(.A = A, .Q = Qabs)
)

# The 'fit' object inherits class 'nls' and many methods can be used

## Model summary:
summary(fit)

## Estimated parameters:
coef(fit)

## 95% confidence intervals:
confint(fit)

## Tidy summary table using 'broom::tidy()'
tidy(fit, conf.int = TRUE, conf.level = 0.95)

# Fit multiple curves with **photosynthesis** and **purrr**

library(purrr)

fits = dat |
  split(~ group) |
  map(fit_photosynthesis, .photo_fun = "aq_response", .vars = list(.A = A, .Q = Qabs))



```

--------------------------------

### Get Boundary Layer Conductance to CO2

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the boundary layer conductance to CO2 (g_bc). Supports an option to use a legacy model version and allows control over unitless calculations.

```R
.get_gbc(pars, surface, unitless, use_legacy_version)
```

--------------------------------

### Constructor for enviro_par class

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Use this function to construct a list into enviro_par. It ensures environmental parameter inputs are properly formatted.

```R
enviro_par(.x, use_tealeaves)
```

--------------------------------

### Fit Mesophyll Conductance (Variable J Method)

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/mesophyll-conductance.html

This snippet demonstrates how to fit mesophyll conductance using the `fit_g_mc_variableJ` function. Ensure your data is correctly formatted and that parameters like `gamma_star`, `R_d`, `alpha_Q`, `beta_Q`, and `P` are accurately set. Note that some calculated `g_mc` values may be unreliable.

```R
library(dplyr)
library(photosynthesis)

# Read in your data
dat = system.file("extdata", "A_Ci_Q_data_1.csv", package = "photosynthesis") |> 
  read.csv() |>
  rename(A_net = A, J_etr = ETR, C_i = Ci, PPFD = Qin, phi_PSII = PhiPS2)

# Note: there will be issues here if the alpha value used for calculating ETR is 
# off, if GammaStar is incorrect, if Rd is incorrect.
fit = fit_g_mc_variableJ(dat, gamma_star = 46, R_d = 0.153,  usealpha_Q = TRUE,
                         alpha_Q = 0.84, beta_Q = 0.5, P = 84)

# Note that many g_mc values from this method can be unreliable
# ggplot(fit, aes(x = CO2_s, y = g_mc, colour = reliable)) + 
#   labs(x = expression(CO[2]~"("~mu*mol~mol^{-1}*")"),
#        y = expression(g[m]*"("~mol~m^{-2}~s^{-1}~Pa^{-1}*")")) + 
#   geom_point(size = 2) +
#   theme_bw() +
#   theme(legend.position = 'bottom')

# Plot QAQC graph according to Harley et al. 1992
# ggplot(fit, aes(x = CO2_s, y = dCcdA, colour = reliable)) + 
#   labs(x = expression(CO[2]~"("~mu*mol~mol^{-1}*")"),
#        y = expression(delta*C[c]*"/"*delta*A)) + 
#   geom_hline(yintercept = 10) +
#   geom_point(size = 2) +
#   theme_bw() +
#   theme(legend.position = 'bottom')

# ggplot(fit, aes(x = dCcdA, y = g_mc, colour = reliable)) + 
#   labs(x = expression(delta*C[c]*"/"*delta*A),
#        y = expression(g[m]*"("~mol~m^{-2}~s^{-1}~Pa^{-1}*")")) + 
#   geom_point(size = 2) +
#   theme_bw() +
#   theme(legend.position = 'bottom')
```

--------------------------------

### Fit Light Respiration using Kok Method

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/light-respiration.html

Fits the light respiration model using the Kok method within a specified PPFD range. Ensure data is prepared before use.

```r
r_light = fit_r_light_kok(data = dat, PPFD_lower = 20, PPFD_upper = 150)
```

--------------------------------

### Model typical hypostomatous leaf

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/modeling-recommendations.html

Implements a simplified C3 photosynthesis model for a hypostomatous leaf. Assumes high boundary layer conductance, zero cuticular conductance, all stomatal conductance through the lower surface, and a single mesophyll conductance. Use this for basic modeling when complexities are not critical.

```r
library(photosynthesis)
bake_par   = make_bakepar()                       # temperature response parameters
constants  = make_constants(use_tealeaves = FALSE) # physical constants
# leaf parameters
leaf_par   = make_leafpar(
  replace = list(
    # Set cuticular conductance to 0
    g_uc = set_units(0, mol / m^2 / s),
    # All conductance through lower stomata and mesophyll
    k_mc = set_units(0, 1),
    k_sc = set_units(0, 1)
  ),
  use_tealeaves = FALSE
  )   
enviro_par = make_enviropar(use_tealeaves = FALSE) # environmental parameters

photo(leaf_par, enviro_par, bake_par, constants, use_tealeaves = FALSE) |
  dplyr::select(g_sc, A) |
  knitr::kable(caption = "Stomatal conductance to CO2 (g_sc) and net photosynthetic carbon assimilation (A) from C3 photosynthesis model.")
```

--------------------------------

### Make lists of parameters for photosynthesis

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Functions to create parameter lists for various components of the photosynthesis package.

```APIDOC
## Make lists of parameters for `photosynthesis`

### Description
Make lists of parameters for `photosynthesis`
make_leafpar 
make_enviropar 
make_bakepar 
make_constants 

### Usage
```R
make_leafpar(replace = NULL, use_tealeaves)

make_enviropar(replace = NULL, use_tealeaves)

make_bakepar(replace = NULL)

make_constants(replace = NULL, use_tealeaves)

```

```

--------------------------------

### Fit a Single CO2 Response Curve

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/co2-response.html

Fits a single ACi curve using the `fit_aci_response` function from the photosynthesis package. This example filters the data to include only measurements for a specific light level (group == 1500).

```R
# Fit ACi curve. 
# Note that we are filtering the data.frame to fit for a single light value
fit = fit_aci_response(filter(dat, group == 1500))
```

--------------------------------

### Fit Bayesian Light-Response Curve with brms

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/light-response.html

Use this function to fit a single Bayesian light-response curve using the brms package. It's suitable for complex datasets where Bayesian methods offer advantages. Ensure brms and Stan are installed.

```R
fit = fit_photosynthesis(
  .data = filter(dat, group == 600),
  .photo_fun = "aq_response",
  .vars = list(.A = A, .Q = Qabs),
  .method = "brms",
  brm_options = list(chains = 1)
)

# The 'fit' object inherits class 'brmsfit' and many methods can be used
summary(fit)
```

--------------------------------

### Read and Prepare Photosynthesis Data

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/sensitivity-analysis.Rmd

Reads a CSV dataset and prepares it for analysis by creating a grouping factor based on light intensity and converting leaf temperature to Kelvin. This is a prerequisite for running sensitivity analyses.

```r
#Read in your data
#Note that this data is coming from data supplied by the package
#hence the complicated argument in read.csv()
#This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv", 
                             package = "photosynthesis"))

#Define a grouping factor based on light intensity to split the ACi
#curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

#Convert data temperature to K
data$T_leaf <- data$Tleaf + 273.15
```

--------------------------------

### Incorporating Leaf Temperature with tealeaves

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

Set `use_tealeaves = TRUE` in `make_constants`, `make_enviropar`, and `make_leafpar` to calculate leaf temperature using the `tealeaves` package's energy balance model within `photosynthesis`. This example varies air temperature and stomatal conductance.

```r
# NOTE: parallel example is not evaluated because it was causing an issue with CRAN, but you can copy-and-paste the code to run on your own machine.

# You will need to set use_tealeaves = TRUE when making parameters because additional parameters are needed for tealeaves.

bake_par = make_bakepar()
constants  = make_constants(use_tealeaves = TRUE)

enviro_par = make_enviropar(
  replace = list(
    T_air = set_units(seq(288.15, 313.15, 1), K)
    ), use_tealeaves = TRUE
  )

leaf_par = make_leafpar(replace = list(
    g_sc = set_units(c(0.2, 0.4), mol / m^2 / s)
    ), use_tealeaves = TRUE
  )

ph = photosynthesis(leaf_par, enviro_par, bake_par, constants, 
                     use_tealeaves = TRUE, progress = FALSE, 
                   quiet = TRUE, parallel = TRUE)

# Plot temperature and photosynthesis
library(ggplot2)


```

--------------------------------

### Load photosynthesis package

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/modeling-recommendations.html

Loads the 'photosynthesis' R package. This is a prerequisite for using any functions within the package.

```r
library(photosynthesis)
```

--------------------------------

### Solving for Multiple Parameter Sets with photosynthesis

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/photosynthesis-introduction.html

Use the `photosynthesis` function to solve for multiple parameter sets by specifying multiple values for leaf or environmental parameters. The function uses `purrr::cross` to fit all combinations. This example demonstrates crossing PPFD and stomatal conductance.

```r
# As before, use the `replace` argument to replace defaults, but this time we
# enter multiple values

bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)

# First, we'll change the PPFD to 1000 and 1500 umol / (m^2 s)
enviro_par = make_enviropar(
  replace = list(
    PPFD = set_units(c(1000, 1500), umol / m^2 / s)
    ), use_tealeaves = FALSE
  )

# Next, we'll change stomatal conductance to to 0.2 and 0.4 mol / m^2 / s
leaf_par  = make_leafpar(
  replace = list(
    g_sc = set_units(c(0.2, 0.4), mol / m^2 / s)
    ), use_tealeaves = FALSE
  )

# Now there should be 4 combinations (high and low g_sc crossed with high and low PPFD)
ph = photosynthesis(leaf_par, enviro_par, bake_par, constants, 
                     use_tealeaves = FALSE, progress = FALSE, quiet = TRUE)

ph |> 
  select(g_sc, PPFD, A) |> 
  knitr::kable()

```

--------------------------------

### Conductance Partitioning Formula

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Formulas for partitioning total cuticular and stomatal conductances between upper and lower surfaces using partitioning factors.

```mathematica
gx,lower=gx(1/(1+kx))
```

```mathematica
gx,upper=gx(kx/(1+kx))
```

```mathematica
gx=gx,lower+gx,upper
```

--------------------------------

### Compiling Model Outputs

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/stomatal-conductance.html

Compiles the results from multiple fitted stomatal conductance models. It first compiles the BallBerry models into a list and then extracts their parameters into a dataframe.

```R
# 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)
```

--------------------------------

### Fit Light Respiration using Yin Method

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/light-respiration.html

Fits the light respiration model using the Yin method within a specified PPFD range. Ensure data is prepared before use.

```r
r_light = fit_r_light_yin(data = dat, PPFD_lower = 20, PPFD_upper = 250)
```

--------------------------------

### Data Preparation and Ball-Berry Model Fitting

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/stomatal-conductance.html

Reads CSV data, preprocesses it by converting RH and rounding PPFD, and fits the Ball-Berry stomatal conductance model for a specific PPFD value.

```R
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"]])
```

--------------------------------

### Simulate Photosynthesis with Error

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

This snippet demonstrates setting leaf, environmental, and background parameters, then simulating photosynthesis and adding simulated error based on chamber conditions. It's useful for generating realistic data for analysis.

```R
lp = make_leafpar(replace = list(
  g_sc = set_units(0.1, mol/m^2/s),
  g_uc = set_units(0, mol/m^2/s),
  k_mc = set_units(0, 1),
  k_sc = set_units(0, 1),
  k_uc = set_units(0, 1)
  ),
  use_tealeaves = FALSE)
   
 ep = make_enviropar(replace = list(
   wind = set_units(Inf, m/s)
 ), use_tealeaves = FALSE) 
 bp = make_bakepar()
 cs = make_constants(use_tealeaves = FALSE)
 
 chamber_pars = data.frame(
   flow = set_units(600, umol / s),
   leaf_area = set_units(6, cm ^ 2),
   sigma_CO2_s = 0.1,
   sigma_CO2_r = 0.1,
   sigma_H2O_s = 0.1,
   sigma_H2O_r = 0.1
 )
   
ph = photosynthesis(lp, ep, bp, cs, use_tealeaves = FALSE, quiet = TRUE) |>
  simulate_error(chamber_pars, n = 1L)
```

--------------------------------

### Accessing Fitted Temperature Response Graphs

Source: https://cran.r-project.org/web/packages/photosynthesis/vignettes/temperature-response.html

Illustrates how to access the generated graphs for different temperature response models from the 'fit' object. The graphs are stored within a list structure.

```R
# Graphs
# fit[["Arrhenius"]][["Graph"]]
# fit[["Heskel"]][["Graph"]]
# fit[["Kruse"]][["Graph"]]
# fit[["Medlyn"]][["Graph"]]
# fit[["MMRT"]][["Graph"]]
# fit[["Quadratic"]][["Graph"]]
# fit[["Topt"]][["Graph"]]
```

--------------------------------

### Data Preparation and CO2 Response Curve Fitting

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

This snippet demonstrates reading CSV data, performing temperature rounding and grouping, creating unique IDs, calculating mean temperatures for groups, and fitting multiple CO2 response curves using the `fit_many` function. It also shows how to extract fitted parameters and graphs.

```R
# Read in data
data <- read.csv(system.file("extdata", "A_Ci_T_data.csv",
  package = "photosynthesis"
),
stringsAsFactors = FALSE
)

library(tidyr)

# Round temperatures to group them appropriately
# Use sequential rounding
data$T2 <- round(data$Tleaf, 1)
data$T2 <- round(data$Tleaf, 0)

# Look at unique values to detect rounding issues
unique(data$T2)

# Some still did not round correctly,
# manually correct
for (i in 1:nrow(data)) {
  if (data$T2[i] == 18) {
    data$T2[i] <- 17
  }
  if (data$T2[i] == 23) {
    data$T2[i] <- 22
  }
  if (data$T2[i] == 28) {
    data$T2[i] <- 27
  }
  if (data$T2[i] == 33) {
    data$T2[i] <- 32
  }
  if (data$T2[i] == 38) {
    data$T2[i] <- 37
  }
}

# Make sure it is a character string for grouping
data$T2 <- as.character(data$T2)

# Create grouping variable by ID and measurement temperature
data <- unite(
  data,
  col = "ID2", c("ID", "T2"),
  sep = "_"
)

# Split by temperature group
data <- split(data, data$ID2)

# Obtain mean temperature for group so temperature
# response fitting is acceptable later, round to
# 2 decimal places
for (i in 1:length(data)) {
  data[[i]]$Curve_Tleaf <- round(mean(data[[i]]$Tleaf), 2)
}

# Convert from list back to dataframe
data <- do.call("rbind", data)

# Parse grouping variable by ID and measurement temperature
data <- separate(
  data,
  col = "ID2", into = c("ID", "T2"),
  sep = "_"
)

# Make sure number of values matches number of measurement
# temperatures. May vary slightly if plants had slightly
# different leaf temperatures during the measurements
unique(data$Curve_Tleaf)

# Create ID column to curve fit by ID and temperature
data <- unite(
  data,
  col = "ID2", c("ID", "Curve_Tleaf"),
  sep = "_"
)

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Fit many CO2 response curves
fits2 <- fit_many(
  data = data,
  group = "ID2",
  varnames = list(
    A_net = "A",
    C_i = "Ci",
    T_leaf = "T_leaf",
    PPFD = "Qin",
    g_mc = "g_mc"
  ),
  funct = fit_aci_response,
  alphag = 0
)

# Extract ACi parameters
pars <- compile_data(
  fits2,
  output_type = "dataframe",
  list_element = 1
)

# Extract ACi graphs
graphs <- compile_data(
  fits2,
  output_type = "list",
  list_element = 2
)

```

--------------------------------

### Calculate Entropy Parameter (dS)

Source: https://cran.r-project.org/web/packages/photosynthesis/refman/photosynthesis.html

Calculates the entropy parameter (dS) from fitted optimum temperature (Topt) model parameters. Requires activation energy, deactivation energy, and optimum temperature.

```R
t_response_calc_dS(Ea, Hd, Topt)
```