### Install photosynthesis from GitHub

Source: https://github.com/cdmuir/photosynthesis/blob/main/README.md

Use this command to install the latest version of the photosynthesis package directly from GitHub using the remotes package.

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

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

### Example: Fitting and Compiling Data

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/compile_data.html

This example demonstrates how to read data, fit multiple curves using fit_many, and then compile the resulting graphs using compile_data for subsequent plotting. It requires data to be loaded and processed into a suitable format for fitting functions.

```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"
)
#> Warning: `fit_many()` was deprecated in photosynthesis 2.1.3. 
  
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |==========================                                            |  38%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |============================================                          |  62%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |======================================================================| 100%

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

# Plot one graph from the compiled list
plot(fits_graphs[[1]])
#> Warning: Removed 12 rows containing missing values or values outside the scale range
#> (`geom_line()`).

```

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

### Example: Running and Graphing a 2-Parameter Sensitivity Analysis

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/analyze_sensitivity.html

This example demonstrates how to use analyze_sensitivity to explore the relationship between gamma_star25, g_mc25, and V_cmax. It involves reading data, preparing it, running the sensitivity analysis, and then visualizing the results using ggplot2.

```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

# Run a sensitivity analysis on gamma_star and mesophyll conductance
# at 25 Celsius for one individual curve
# pars <- analyze_sensitivity(
#   data = data[data$Q_2 == 1500, ],
#   funct = fit_aci_response,
#   varnames = list(
#     A_net = "A",
#     T_leaf = "T_leaf",
#     C_i = "Ci",
#     PPFD = "Qin"
#   ),
#   useg_mct = TRUE,
#   test1 = "gamma_star25",
#   element_out = 1,
#   test2 = "g_mc25",
#   fitTPU = TRUE,
#   Ea_gamma_star = 0,
#   Ea_g_mc = 0,
#   values1 = seq(
#     from = 20,
#     to = 40,
#     by = 2
#   ),
#   values2 = seq(
#     from = 0.5,
#     to = 2,
#     by = 0.1
#   )
# )

# Graph V_cmax
# ggplot(pars, aes(x = gamma_star25, y = g_mc25, z = V_cmax)) +
#   geom_tile(aes(fill = V_cmax)) +
#   labs(
#     x = expression(Gamma * "*"[25] ~ "(" * mu * mol ~ mol^ 
#       {
#         -1
#       } * ")"),
#     y = expression(g[m][25] ~ "(" * mu * mol ~ m^{ 
#       -2
#     } ~ s^{ 
#       -1
#     } ~ Pa^ 
#       { 
#         -1
#       } * ")")
#   ) +
#   scale_fill_distiller(palette = "Greys") +
#   geom_contour(colour = "Black", size = 1) +
#   theme_bw()
# # }
```

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

### Typical Hypostomatous Leaf Model Setup

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/modeling-recommendations.html

Configures parameters for a typical hypostomatous leaf model, setting cuticular conductance to zero and all stomatal and mesophyll conductance to the lower surface. Use this for simplified models where stomata are only on one leaf surface.

```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.")
```

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

### FvCB Function Example

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/FvCB.html

Demonstrates how to set up parameters and use the FvCB function for calculating photosynthetic CO2 assimilation. Ensure all necessary parameter objects are created and baked before calling FvCB.

```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)

pars = c(leaf_par, enviro_par, constants)
C_chl = set_units(246.0161, umol / mol)
FvCB(C_chl, pars)
```

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

### R: Photosynthesis with Tealeaves Package

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/photosynthesis-introduction.html

This example demonstrates how to set up parameters and run the photosynthesis model using the tealeaves package for leaf temperature calculation. It requires setting `use_tealeaves = TRUE` and providing additional parameters for the tealeaves model. The code is not evaluated on CRAN due to potential issues but can be run locally.

```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)

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

ggplot(ph, aes(T_air, T_leaf, color = `g[s]`)) +
  geom_line(size = 2, lineend = "round") +
  geom_abline(slope = 1, intercept = 0, linetype = "dotted") +
  scale_color_discrete(name = expression(g[s])) +
  xlab(expression(paste(T[air], " [K] "))) +
  ylab(expression(paste(T[leaf], " [K] "))) +
  theme_bw() +
  NULL

ggplot(ph, aes(T_air, A, color = `g[s]`)) +
  geom_line(size = 2, lineend = "round") +
  scale_color_discrete(name = expression(g[s])) +
  xlab(expression(paste(T[leaf], " [K] "))) +
  ylab(expression(paste("A [", mu, "mol ", m^-2~s^-1, "]"))) +
  theme_bw() +
  NULL
```

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

### Install photosynthesis from CRAN

Source: https://github.com/cdmuir/photosynthesis/blob/main/README.md

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

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

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

### Example Data Output

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_many.html

This shows the typical output structure for a single fit, including parameters and their associated values. It is often displayed after running a fitting function.

```R
#> 6   0 8.94862 0.5509706 47.01527 16.63315 0.08692268  1000       NA -0.1565895
#>      R_d_se      cost citransition1 citransition2 V_cmax_pts J_max_pts
#> 6 0.1264438 0.1194886      441.2967      1442.493          8         4
#>   V_TPU_pts alpha alpha_g gamma_star25 Ea_gamma_star K_M25   Ea_K_M  g_mc25
#> 6         0  0.24       0        42.75         37830 718.4 65508.28 0.08701
#>   Ea_g_mc Oconc theta_J
#> 6       0    21    0.85
```

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

### Example: Fit and Print Single AQ Response

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/print_graphs.html

Demonstrates reading data, fitting a single CO2 response curve, and printing the model summary, fitted parameters, and the generated graph.

```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"
))

# Fit many AQ curves
# Set your grouping variable
# Here we are grouping by CO2_s and individual
data$C_s <- (round(data$CO2_s, digits = 0))

# For this example we need to round sequentially due to CO2_s setpoints
data$C_s <- as.factor(round(data$C_s, digits = -1))

# To fit one AQ curve
fit <- fit_aq_response(data[data$C_s == 600, ],
  varnames = list(
    A_net = "A",
    PPFD = "Qin"
  )
)

# Print model summary
summary(fit[[1]])
#> 
#> Formula: A_net ~ aq_response(k_sat, phi_J, Q_abs = data$Q_abs, theta_J) - 
#>     Rd
#>
#> Parameters:
#>                 Estimate Std. Error t value Pr(>|t|)    
#> k_sat          21.167200   0.158332  133.69 1.88e-08 ***
#> phi_J.Q_abs     0.051907   0.001055   49.18 1.02e-06 ***
#> theta_J         0.775484   0.014920   51.98 8.20e-07 ***
#> Rd.(Intercept)  0.668495   0.065235   10.25 0.000511 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.05535 on 4 degrees of freedom
#>
#> Number of iterations to convergence: 5 
#> Achieved convergence tolerance: 1.49e-08
#>

# Print fitted parameters
fit[[2]]
#>         A_sat      phi_J   theta_J        Rd      LCP  resid_SSs
#> k_sat 21.1672 0.05190746 0.7754836 0.6684953 12.97289 0.01225491

# Print graph
fit[[3]]

```

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

### Fit a light-response curve using fit_photosynthesis

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_aq_response2.html

Demonstrates fitting a light-response curve using the fit_photosynthesis function with the 'aq_response' photo_fun. This example shows data preparation, including setting grouping variables, and then fitting the model. The resulting 'fit' object is an 'nls' class object.

```R
library(broom)
library(dplyr)
# 
# Attaching package: ‘dplyr’
# The following objects are masked from ‘package:stats’:
# 
#     filter, lag
# The following objects are masked from ‘package:base’:
# 
#     intersect, setdiff, setequal, union
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)
```

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

### Load photosynthesis package

Source: https://github.com/cdmuir/photosynthesis/blob/main/README.md

Load the photosynthesis package into your R session after installation to start using its functions.

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

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

### Fit Stomatal Conductance Models

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_gs_model.html

This example demonstrates how to fit multiple stomatal conductance models using the fit_gs_model function. It includes reading data, converting relative humidity to a proportion, and specifying custom variable names for the model fitting process. Use this when you need to compare different stomatal conductance models for your dataset.

```R
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
      package = "photosynthesis"
    ))
    
    # Convert RH to a proportion
    data$RH <- data$RHcham / 100
    
    # Fit stomatal conductance models
    # Can specify a single model, or all as below
    fits <- fit_gs_model(
      data = data,
      varnames = list(
        A_net = "A",
        C_air = "Ca",
        g_sw = "gsw",
        RH = "RH",
        VPD = "VPDleaf"
      ),
      model = c(
        "BallBerry",
        "Leuning",
        "Medlyn_partial",
        "Medlyn_full"
      )
    )
```

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

### Parallel Photosynthesis Simulation with Future

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/photosynthesis-introduction.html

Accelerate photosynthesis simulations for multiple parameter sets by enabling parallel processing with the `parallel = TRUE` argument in the `photosynthesis` function. This requires setting up a `future::plan()`, such as `plan("multisession")`. Note: This example is commented out due to potential CRAN issues but can be run locally.

```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
```

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

### Example: Reading and Fitting Temperature Response Data

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_t_response.html

Demonstrates how to read temperature response data from a CSV file and then fit various temperature response models using the fit_t_response function. Ensure your data file contains the necessary columns for the parameter of interest and leaf temperature.

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

# Fit temperature response models
fit_t_response(data)

# Fit only the Medlyn model with specific starting parameters
fit_t_response(data, model = "Medlyn", start = list(Ea = 40000, Par25 = 50, Hd = 2e+05, dS = 650))

# Fit the Medlyn model, setting Hd as a constant
fit_t_response(data, model = "Medlyn", setvar = "Hd", hdset = 200000)

# Fit the Medlyn model, setting dS as a constant
fit_t_response(data, model = "Medlyn", setvar = "dS", dSset = 650)

# Fit the MMRT model with custom starting parameters
fit_t_response(data, model = "MMRT", start = list(dCp = 1, dG = 1, dH = 1))

# Fit the Heskel model with custom starting parameters
fit_t_response(data, model = "Heskel", start = list(a = 1, b = 1, c = 1))

# Fit the Kruse model with custom starting parameters
fit_t_response(data, model = "Kruse", start = list(dEa = 1, Ea_ref = 1, Par_ref = 1))

# Fit the Quadratic model
fit_t_response(data, model = "Quadratic")

# Fit the Topt model with custom starting parameters
fit_t_response(data, model = "Topt", start = list(Ea = 40000, Par25 = 50, Hd = 2e+05))

# Fit the Arrhenius model
fit_t_response(data, model = "Arrhenius")
# }
```

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

### Example: Fit Many AQ Responses

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/print_graphs.html

Demonstrates fitting multiple CO2 response curves from a dataset by grouping, using the `fit_many` function.

```R
# Fit many curves
fits <- fit_many(
      data = data,
      varnames = list(
        A_net = "A",
        PPFD = "Qin",
        group = "C_s"
      ),
      funct = fit_aq_response,
      group = "C_s"
    )
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |================                                                      |  22%
  |                                                                            

```

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

### Fit ACI Curve with Provided Data

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_aci_response.html

This example demonstrates how to read data, preprocess it by defining a grouping factor and converting temperature to Kelvin, and then fit an ACI curve using the fit_aci_response function. It shows how to specify variable names for the model.

```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 ACi curve. Note that we are subsetting the dataframe
# here to fit for a single value of Q_2
fit <- fit_aci_response(data[data$Q_2 == 1500, ],
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  )
)
```

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

### Compute Sensitivity Measures

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/compute_sensitivity.html

This example demonstrates how to use the compute_sensitivity function after obtaining sensitivity analysis results. It specifies the data, variable names for the parameters of interest, and their reference values to calculate sensitivity measures.

```R
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
      package = "photosynthesis"
    ))
    
    data$Q_2 <- as.factor((round(data$Qin, digits = 0)))
    
    data$T_leaf <- data$Tleaf + 273.15
    
    # pars <- analyze_sensitivity(
    #   data = data[data$Q_2 == 1500, ],
    #   funct = fit_aci_response,
    #   varnames = list(
    #     A_net = "A",
    #     T_leaf = "T_leaf",
    #     C_i = "Ci",
    #     PPFD = "Qin"
    #   ),
    #   useg_mct = TRUE,
    #   test1 = "gamma_star25",
    #   element_out = 1,
    #   test2 = "g_mc25",
    #   fitTPU = TRUE,
    #   Ea_gamma_star = 0,
    #   Ea_g_mc = 0,
    #   values1 = seq(
    #     from = 20,
    #     to = 60,
    #     by = 2
    #   ),
    #   values2 = seq(
    #     from = 0.2,
    #     to = 2,
    #     by = 0.1
    #   )
    # )
    par2 <- compute_sensitivity(
      data = pars,
      varnames = list(
        Par = "V_cmax",
        test1 = "gamma_star25",
        test2 = "g_mc25"
      ),
      test1_ref = 42,
      test2_ref = 1
    )
```

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

### Amphistomatous Leaf Model Setup

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/modeling-recommendations.html

Configures parameters for an amphistomatous leaf model, allowing for stomatal conductance on both surfaces and explicitly modeling internal airspace and liquid-phase conductances. Use this for models where stomata are present on both the upper and lower leaf surfaces.

```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),
    # Half of conductance through each surface
    k_mc = set_units(0, 1),
    # airspace conductance: define effective distance through airspace
    # See Nobel (2020) pg. 431
    delta_ias_lower = set_units(100, um),
    delta_ias_upper = set_units(100, um),
    # liquid conductance: sum of cell wall, plasma membrane, and cytosol resistance
    # We are implicitly ignoring chloroplast resistance
    # See Nobel (2020) pg. 448-452
    A_mes_A = set_units(20, 1),
    g_liqc25 = set_units(0.02, mol / m^2 / s),
    k_sc = set_units(1, 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, g_iasc_lower, g_iasc_upper, g_liqc, A) |
  knitr::kable(caption = "Stomatal conductance to CO2 (g_sc), internal airspace resistance through lower and upper surfaces (g_iasc_x), liquid-phase conductance (g_liqc), and net photosynthetic carbon assimilation (A) from C3 photosynthesis model.")
```

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

### Simulating Measurement Error with `simulate_error`

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/simulate_error.html

This example demonstrates how to use the `simulate_error` function to add measurement errors to photosynthesis data. It first generates 'real' values using `photosynthesis()` and then applies simulated errors based on specified `chamber_pars`. Ensure parameters like `g_sc`, `g_uc`, `k_mc`, `k_sc`, and `k_uc` are set to meet the assumptions of `simulate_error`.

```R
library(photosynthesis)

# Use photosynthesis() to simulate 'real' values
# `replace = ...` sets parameters to meet assumptions of `simulate_error()`
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)
  

```

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

### Fit many A-Ci curves across light intensity groups

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_many.html

This example demonstrates fitting A-Ci response curves to data grouped by light intensity. It involves reading data, defining a grouping factor, converting temperature units, and then applying `fit_many` with specific variable names and the `fit_aci_response` function. A progress bar is shown during fitting.

```R
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"
)
#>
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |==========================                                            |  38%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |============================================                          |  62%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |======================================================================| 100%

# Print the parameters
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
fits[[3]][[1]]
#>   Num  V_cmax V_cmax_se    J_max        J       J_se V_TPU V_TPU_se        R_d

```

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

### Initialize Photosynthesis Parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/make_parameters.html

Initializes default parameters for bakepar, constants, enviropar, and leafpar. Use use_tealeaves = FALSE for standard leaf simulations.

```R
bake_par = make_bakepar()
constants = make_constants(use_tealeaves = FALSE)
enviropar = make_enviropar(use_tealeaves = FALSE)
leaf_par = make_leafpar(use_tealeaves = FALSE)
```

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

### parameter_names()

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Gets a vector of parameter names used in the photosynthesis package.

```APIDOC
## parameter_names()

### Description
Get vector of parameter names.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
None explicitly documented in this snippet.

### Response
Returns a character vector of parameter names.

### Response Example
Not available in source.
```

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

### Get Citation Information

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/index.html

Retrieve citation information for the photosynthesis package in R.

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

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

### Get Stomatal Conductance to CO2

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/CO2_conductance.html

Calculates the stomatal conductance to CO2 for a specified leaf surface.

```APIDOC
## .get_gsc

### Description
Calculates the stomatal conductance to CO2 (g_sc) for a specified leaf surface (lower or upper).

### Method
Internal R function (not directly callable by users as a public API endpoint).

### Parameters
#### Arguments
*   **pars**: Concatenated parameters (`leaf_par`, `enviro_par`, and `constants`).
*   **surface**: Leaf surface (lower or upper).
*   **unitless** (logical): If TRUE, units are not applied, potentially speeding up calculations but requiring correct input units.
```

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

### Get Mesophyll Conductance to CO2

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/CO2_conductance.html

Calculates the mesophyll conductance to CO2 for a specified leaf surface.

```APIDOC
## .get_gmc

### Description
Calculates the mesophyll conductance to CO2 (g_mc) for a specified leaf surface (lower or upper).

### Method
Internal R function (not directly callable by users as a public API endpoint).

### Parameters
#### Arguments
*   **pars**: Concatenated parameters (`leaf_par`, `enviro_par`, and `constants`).
*   **surface**: Leaf surface (lower or upper).
*   **unitless** (logical): If TRUE, units are not applied, potentially speeding up calculations but requiring correct input units.
```

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

### Get Cuticular Conductance to CO2

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/CO2_conductance.html

Calculates the cuticular conductance to CO2 for a specified leaf surface.

```APIDOC
## .get_guc

### Description
Calculates the cuticular conductance to CO2 (g_uc) for a specified leaf surface (lower or upper).

### Method
Internal R function (not directly callable by users as a public API endpoint).

### Parameters
#### Arguments
*   **pars**: Concatenated parameters (`leaf_par`, `enviro_par`, and `constants`).
*   **surface**: Leaf surface (lower or upper).
*   **unitless** (logical): If TRUE, units are not applied, potentially speeding up calculations but requiring correct input units.
```

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

### Calculate CO2 Supply and Demand

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/A_supply.html

Demonstrates how to calculate CO2 supply and demand rates using the A_supply and A_demand functions. Requires setting up leaf, environmental, and constant parameters.

```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)
#> 12.0831 [umol/m^2/s]

A_demand(C_chl, pars)
#> 31.28078 [umol/m^2/s]
```

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

### Get default model for 'r_light'

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/models.html

Retrieves the default model name for the 'r_light' function. This function is used for fitting light response curves.

```R
get_default_model("r_light")
#> [1] "walker_ort_2015"
```

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

### Get default model for 'aq_response'

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/models.html

Retrieves the default model name for the 'aq_response' function. This function is used for fitting light response curves.

```R
get_default_model("aq_response")
#> [1] "marshall_biscoe_1980"
```

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

### Bake Photosynthesis Parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/bake.html

Demonstrates the process of creating and baking photosynthesis parameters. Use this to set environmental conditions and leaf properties to calculate final photosynthetic rates. Ensure necessary helper functions like `make_bakepar`, `make_constants`, and `make_enviropar` are called.

```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$V_cmax25
#> 150 [umol/m^2/s]
baked_leafpar$V_cmax
#> 104.7069 [umol/m^2/s]
```

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

### analyze_sensitivity()

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Runs two-parameter sensitivity analyses for photosynthesis models.

```APIDOC
## analyze_sensitivity()

### Description
Runs 2-parameter sensitivity analyses.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
None explicitly documented in this snippet.

### Response
Returns results of the sensitivity analysis.

### Response Example
Not available in source.
```

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

### Get Boundary Layer Conductance to CO2

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/CO2_conductance.html

Calculates the boundary layer conductance to CO2 for a specified leaf surface, based on mass and heat transfer principles.

```APIDOC
## .get_gbc

### Description
Calculates the boundary layer conductance to CO2 (g_bc) for a specified leaf surface. Calculations are based on mass and heat transfer principles.

### Method
Internal R function (not directly callable by users as a public API endpoint).

### Parameters
#### Arguments
*   **pars**: Concatenated parameters (`leaf_par`, `enviro_par`, and `constants`).
*   **surface**: Leaf surface (lower or upper).
*   **unitless** (logical): If TRUE, units are not applied, potentially speeding up calculations but requiring correct input units.
*   **use_legacy_version** (logical): If TRUE, uses the legacy model version (<2.1.0). Defaults to FALSE.
```

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

### Get Total Conductance to CO2

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/CO2_conductance.html

Calculates the total conductance to CO2 based on provided parameters. This function sums the conductances of the lower and upper leaf portions.

```APIDOC
## .get_gtc

### Description
Calculates the total conductance to CO2 (g_tc) by summing the conductances of the lower and upper leaf portions.

### Method
Internal R function (not directly callable by users as a public API endpoint).

### Parameters
#### Arguments
*   **pars**: Concatenated parameters (`leaf_par`, `enviro_par`, and `constants`).
*   **unitless** (logical): If TRUE, units are not applied, potentially speeding up calculations but requiring correct input units.
*   **use_legacy_version** (logical): If TRUE, uses the legacy model version (<2.1.0). Defaults to FALSE.
```

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

### make_parameters functions (make_leafpar(), make_enviropar(), make_bakepar(), make_constants())

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Creates lists of parameters for use with the photosynthesis package.

```APIDOC
## make_parameters functions (make_leafpar(), make_enviropar(), make_bakepar(), make_constants())

### Description
Make lists of parameters for the photosynthesis package.

### Method
Not applicable (R functions)

### Endpoint
Not applicable (R functions)

### Parameters
None explicitly documented in this snippet.

### Response
Returns parameter lists.

### Response Example
Not available in source.
```

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

### photosynthesis() and photo()

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Simulates C3 photosynthesis.

```APIDOC
## photosynthesis() and photo()

### Description
Simulate C3 photosynthesis.

### Method
Not applicable (R functions)

### Endpoint
Not applicable (R functions)

### Parameters
None explicitly documented in this snippet.

### Response
Returns simulated C3 photosynthesis values.

### Response Example
Not available in source.
```

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

### Get Leaf Parameter Names

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/parameter_names.html

Retrieves a character vector of parameter names related to leaf properties. This is useful for understanding which parameters are associated with leaf-level calculations when not using tealeaves for leaf temperature.

```r
parameter_names("leaf", use_tealeaves = FALSE)
#>  [1] "A_mes_A"         "J_max25"         "K_C25"           "K_O25"          
#>  [5] "R_d25"           "T_leaf"          "V_cmax25"        "V_tpu25"        
#>  [9] "delta_ias_lower" "delta_ias_upper" "g_liqc25"        "g_mc25"         
#> [13] "g_sc"            "g_uc"            "gamma_star25"    "k_mc"           
#> [17] "k_sc"            "k_uc"            "leafsize"        "phi_J"          
#> [21] "theta_J"
```

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

### Run Minimum Photosynthesis Simulation

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/photosynthesis-introduction.html

This snippet demonstrates how to run a basic photosynthetic rate simulation using default parameters from the 'photosynthesis' package. It requires loading the 'dplyr', 'magrittr', and 'photosynthesis' libraries.

```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)

```

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

### Environmental Parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/make_parameters.html

Creates a list of environmental parameters. Use `replace` to override defaults and `use_tealeaves` to enable leaf energy balance calculations.

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

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

### Get Default Calculated Parameter Function

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/calculated-parameters.html

Use this function to retrieve the default R function associated with a specific calculated parameter name. The function expects a character string representing the parameter's name.

```R
get_f_parameter(.f_name)
```

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

### Baking (Temperature Response) Parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/make_parameters.html

Creates a list of parameters related to temperature response curves. Use `replace` to override defaults.

```R
make_bakepar(replace = NULL)
```

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

### Stomatal conductance models (gs_mod_ballberry(), gs_mod_leuning(), gs_mod_opti(), gs_mod_optifull())

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Provides implementations of various stomatal conductance models.

```APIDOC
## Stomatal conductance models (gs_mod_ballberry(), gs_mod_leuning(), gs_mod_opti(), gs_mod_optifull())

### Description
Implementations of stomatal conductance models.

### Method
Not applicable (R functions)

### Endpoint
Not applicable (R functions)

### Parameters
None explicitly documented in this snippet.

### Response
Returns results from stomatal conductance models.

### Response Example
Not available in source.
```

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

### Fit Photosynthesis with Yin et al. (2011) Model

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/fit_r_light2.html

Estimates parameters using the Yin et al. (2011) model for light respiration. Requires A-phiPSII-Q data and specifies lower and upper bounds for Q.

```R
# Yin et al. (2011) model

fit = fit_photosynthesis(
  .data = acq_data,
  .photo_fun = "r_light",
  .model = "yin_etal_2011",
  .vars = list(.A = A, .phiPSII = PhiPS2, .Q = Qin),
  Q_lower = 20,
  Q_upper = 250
)

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

## Model summary:
summary(fit)
```

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

### Constants for Diffusion and Thermodynamics

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/make_parameters.html

Creates a list of physical constants used in diffusion and thermodynamic calculations. Use `replace` to override defaults and `use_tealeaves` to enable leaf energy balance calculations.

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

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

### photo_parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Defines input parameters for simulating C3 photosynthesis using the photosynthesis() function.

```APIDOC
## photo_parameters

### Description
Input parameters to simulate C3 photosynthesis using the photosynthesis() function.

### Method
Not applicable (R object/structure)

### Endpoint
Not applicable (R object/structure)

### Parameters
None explicitly documented in this snippet.

### Response
Defines the structure for photosynthesis simulation parameters.

### Response Example
Not available in source.
```

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

### FvCB()

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/index.html

Implements the Farquhar-von Caemmerer-Berry (FvCB) C3 photosynthesis model.

```APIDOC
## FvCB()

### Description
Implements the Farquhar-von Caemmerer-Berry (FvCB) C3 photosynthesis model.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
None explicitly documented in this snippet.

### Response
Returns simulation results from the FvCB model.

### Response Example
Not available in source.
```

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

### Leaf Parameters

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/reference/make_parameters.html

Creates a list of leaf-related parameters. Use `replace` to override defaults and `use_tealeaves` to enable leaf energy balance calculations.

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

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

### Run Sensitivity Analysis on GammaStar and Mesophyll Conductance

Source: https://github.com/cdmuir/photosynthesis/blob/main/docs/articles/sensitivity-analysis.html

Performs a sensitivity analysis on GammaStar and mesophyll conductance at 25 Celsius for a single curve. It requires specifying the data, the fitting function, variable names, and the ranges for the parameters to test. The `useg_mct` argument should be set to TRUE when testing mesophyll conductance.

```R
#Run a sensitivity analysis on GammaStar and mesophyll conductance
#at 25 Celsius for one individual curve
pars <- analyze_sensitivity(data = data[data$Q_2 == 1500, ],
                                      funct = fit_aci_response,
                                       varnames = list(A_net = "A",
                                          T_leaf = "T_leaf",
                                          C_i = "Ci",
                                          PPFD = "Qin"),
                                       useg_mct = TRUE,
                                       test1 = "gamma_star25",
                                       element_out = 1,
                                       test2 = "g_mc25",
                                       fitTPU = TRUE,
                                       Ea_gamma_star = 0,
                                       Ea_g_mc = 0,
                                       values1 = seq(from = 20,
                                                   to = 60,
                                                   by = 4),
                                       values2 = seq(from = 0.2,
                                                   to = 2,
                                                   by = 0.1))
```