DataFrame docs added

docs/src/basics/solvers.md

@@ -11,10 +11,10 @@ One can also set relative and absolute tolerances (`reltol`, `abstol`) and other
 If no solver is provided the default one `AutoTsit5(Rosenbrock23())` will be used with `reltol=1e-3` and `abstol=1e-6` for simulation problems and `reltol=1e-6` and `abstol=1e-8` for parameters estimation.
 
 The following [DiffEq Docs page](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/) provides general advice on how to choose a solver suitable for your ODE system. 
-For most of ODE systems the following simplified guideline is sufficient:
+For many ODE systems the following simplified guideline is sufficient:
 1. If your system is small (~10 ODEs) go with the default solver 
 2. If the system is large and stiff choose `FBDF()` or `QNDF()`
 3. If `FBDF()` fails to solve the system check the model and try it again :)
-4. If `FBDF()` still fails to solve the system or the integration takes too long, try `CVODE_BDF()`*
+4. If `FBDF()` still fails to solve the system or the integration takes too long try `CVODE_BDF()`*
 
-* You should be caution with `CVODE_BDF()`. With many models it is the most fast solver, however the accuracy of the solution is often the tradeoff. You shouldn't use it with tolerances higher than ~ `reltol=1e-4` and `abstol=1e-7`.  
+*You should be caution with `CVODE_BDF()`. With many models it is the most fast solver, however the accuracy of the solution is often the tradeoff. You shouldn't use it with tolerances higher than ~ `reltol=1e-4` and `abstol=1e-7`.  

src/solution_interface.jl

@@ -41,6 +41,20 @@ function DataFrame(s::Simulation; vars=observables(s), parameters_output::Vector
   return df
 end
 
+"""
+    DataFrame(s::Simulation; 
+      vars=observables(s), parameters_output::Vector{Symbol}=Symbol[], iter::Union{Int,Nothing}=nothing)
+
+Converts simulation results of type `SimResult`, `MCResult`, etc  to `DataFrame`.
+
+Example: `DataFrame(s)`
+
+Arguments:
+
+- `s` : simulation results of type [`SimResult`](@ref), [`MCResult`](@ref) or `Vector` containing them
+- `parameters_output` : parameters provided as kwarg `parameters` to `sim` or `mc` functions, which should be included in `DataFrame`. Default is `empty`
+- `iter` : `Int` iteration id, which should be included in `DataFrame`. Default is `nothing`
+"""
 function DataFrame(sr::SimResult; kwargs...)
   df = DataFrame(sr.sim; kwargs...)