📝 minor edits in the doc

Author: Kevin Cazelles

src/basic_omnivory_module.jl

@@ -3,7 +3,7 @@ using LinearAlgebra: eigvals
 using ForwardDiff
 using QuadGK: quadgk
 
-# # Omnivory preference functions
+# Omnivory preference functions
 function adapt_pref(u, p, t)
     return p.ω * u[1] / (p.ω * u[1] + (1 - p.ω) * u[2])
 end
@@ -16,7 +16,7 @@ end
 @with_kw mutable struct ModelPar
     # Logistic Parameters
     r = 2.0
-    ## `K_base` measures the underyling K outside of any forcing applied
+    # `K_base` measures the underyling K outside of any forcing applied
     K_base = 3
     K = 3
     # Consumer Parameters
@@ -33,10 +33,13 @@ end
     a_RP = 0.2
     h_RP = 0.6
     e_RP = 0.4
+    # if Ω = 0 then we have a food chain
     Ω = 0.1
     # Forcing Function
+    # 'fixed_pref' for passive omnivory
+    # 'adapt_pref' for active omnivory
     pref::Function = fixed_pref
-    ## Used in the adaptive forcing to bias towards C or R
+    # Used in the adaptive forcing to bias towards C or R
     ω = 0.5
 end
 

src/pipeline.jl

@@ -18,9 +18,9 @@ printstyled("Done! fig_pulse.jl created\n", color = :green)
 
 # Supplementary Information 
 
-# The line below is used to run the SI version of the figure: when the 1st
-# argument of 'ARGS' is true, then the parameters used are the one for the SI
-# figure. Note that we actually use `ARGS` because this is specifically
+# The line below is used to run the SI version of the figures: when the 1st
+# argument of 'ARGS' is true, then the model parameters used are the ones for 
+# the SI figure. Note that we actually use `ARGS` because this is specifically
 # the vector that captures external arguments. Thus using it allows us to use 
 # arguments in the command line. So here, to run the SI version of fig_pulse, 
 # I'd use `julia fig_pulse.jl true`. To mimic this we simply add "true" to 

src/press.jl

@@ -1,6 +1,6 @@
 using DifferentialEquations, NLsolve
 
-# Full analysis for one press simulation (1 system)
+# Entire analysis for one press simulation (1 system)
 function press_unit(par, p_strength) 
     # Parameters
     u0 = [1.0, 1.5, 1.5]
@@ -28,8 +28,9 @@ function press_unit(par, p_strength)
     if par.pref == adapt_pref
         # compute ω
         # NB: This may often increases computation time as we may compute
-        # results for 'fixed' twice but the code is much easier to read. 
-        # Here the time is not a major concern as it takes few minutes to run.
+        # results for passive omnivory twice but the code is much easier to
+        # read. Here the time is not a major concern as it takes few minutes to
+        # run.
         par_fix = deepcopy(par)
         par_fix.pref = fixed_pref
         prob_fix = ODEProblem(model!, u0, t_span, deepcopy(par_fix),
@@ -90,7 +91,7 @@ function press_unit(par, p_strength)
 end
 
 # Perform the pulse for the 3 systems: 
-# - Food chain: "chain";C OP (fixed)
+# - Food chain: "chain";
 # - Passive omnivory: "passive";
 # - Active omnivory: "active";
 function press(base_par, Ω, p_strength) 

src/pulse.jl

@@ -31,10 +31,11 @@ function pulse_unit(par, p_length, p_strength)
 
     # run
     if par.pref == adapt_pref
-        # compute w
+        # compute ω
         # NB: This may often increases computation time as we may compute
-        # results for 'fixed' twice but the code is much easier to read. 
-        # Here the time is not a major concern as it takes few minutes to run.
+        # results for passive omnivory twice but the code is much easier to
+        # read. Here the time is not a major concern as it takes few minutes to
+        # run.
         par_fix = deepcopy(par)
         par_fix.pref = fixed_pref
         prob_fix = ODEProblem(model!, u0, t_span, deepcopy(par_fix),