update docs for fit_rate_equation

src/rate_equation_fitting.jl

@@ -13,7 +13,30 @@ using CMAEvolutionStrategy, DataFrames, Statistics
         n_iter = 20
 )
 
-Fit `rate_equation` to `data` and report the loss and best fit parameters.
+Fit `rate_equation` to `data` and return loss and best fit parameters.
+
+# Arguments
+- `rate_equation::Function`: Function that takes a NamedTuple of metabolite concentrations (with `metab_names` keys) and parameters (with `param_names` keys) and returns an enzyme rate.
+- `data::DataFrame`: DataFrame containing the data with column `Rate` and columns for each `metab_names` where each row is one measurement. It also needs to have a column `source` that contains a string that identifies the source of the data. This is used to calculate the weights for each figure in the publication.
+- `metab_names::Tuple`: Tuple of metabolite names that correspond to the metabolites of `rate_equation` and column names in `data`.
+- `param_names::Tuple`: Tuple of parameter names that correspond to the parameters of `rate_equation`.
+- `n_iter::Int`: Number of iterations to run the fitting process.
+
+# Returns
+- `loss::Float64`: Loss of the best fit.
+- `params::NamedTuple`: Best fit parameters with `param_names` keys
+
+# Example
+```julia
+using DataFrames
+data = DataFrame(
+    Rate = [1.0, 2.0, 3.0],
+    A = [1.0, 2.0, 3.0],
+    source = ["Figure 1", "Figure 1", "Figure 2"]
+)
+rate_equation(metabs, params) = params.Vmax * metabs.S / (1 + metabs.S / params.K_S)
+fit_rate_equation(rate_equation, data, (:A,), (:Vmax, :K_S))
+```
 """
 function fit_rate_equation(
         rate_equation::Function,
@@ -34,7 +57,6 @@ function fit_rate_equation(
     return (loss = train_results[1], params = NamedTuple{param_names}(rescaled_params))
 end
 
-
 function train_rate_equation(
         rate_equation::Function,
         data::DataFrame,