format and remove Gurobi

Project.toml

@@ -12,7 +12,6 @@ DistributedData = "f6a0035f-c5ac-4ad0-b410-ad102ced35df"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
-Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

src/analysis/flux_variability_analysis.jl

@@ -141,7 +141,7 @@ fva_max, fva_min = fva(model, biomass, optimizer; solver_attributes=atts)
 function flux_variability_analysis(
     model::StandardModel,
     optimizer;
-    optimum_bound = 1.0-DEFAULT_FVA_TOL,
+    optimum_bound = 1.0 - DEFAULT_FVA_TOL,
     modifications = [(model, opt_model) -> nothing],
 )
     # Run FBA
@@ -157,14 +157,12 @@ function flux_variability_analysis(
     v = opt_model[:x]
 
     λ = COBREXA.JuMP.objective_value(opt_model) # objective value
-    λmin = min(optimum_bound*λ, λ * 1.0 / optimum_bound)
-    λmax = max(optimum_bound*λ, λ * 1.0 / optimum_bound)
+    λmin = min(optimum_bound * λ, λ * 1.0 / optimum_bound)
+    λmax = max(optimum_bound * λ, λ * 1.0 / optimum_bound)
 
     COBREXA.JuMP.@constraint(
         opt_model,
-        λmin <=
-        COBREXA.JuMP.objective_function(opt_model) <=
-        λmax # in case there is a negative bound
+        λmin <= COBREXA.JuMP.objective_function(opt_model) <= λmax # in case there is a negative bound
     )
 
     for i = 1:length(v)
@@ -176,7 +174,7 @@ function flux_variability_analysis(
         )
         if status
             fva_max[model.reactions[i].id] =
-                Dict(zip(reactions(model), value.(opt_model[:x]) ))
+                Dict(zip(reactions(model), value.(opt_model[:x])))
         else
             @warn "Error maximizing index: $i with error $(termination_status(opt_model))"
             fva_max[model.reactions[i].id] = nothing
@@ -190,7 +188,7 @@ function flux_variability_analysis(
         )
         if status
             fva_min[model.reactions[i].id] =
-                Dict(zip(reactions(model), value.(opt_model[:x]) ))
+                Dict(zip(reactions(model), value.(opt_model[:x])))
         else
             @warn "Error minimizing index: $i with error $(termination_status(opt_model))"
             fva_min[model.reactions[i].id] = nothing

src/analysis/parsimonious_flux_balance_analysis.jl

@@ -49,8 +49,8 @@ function parsimonious_flux_balance_analysis(
     @objective(opt_model, Min, sum(dot(v, v)))
 
     for lbconval in [1.0, 0.999999, 0.99999, 0.9999, 0.999, 0.99] # sequentially relax bound for stability
-        λmin = min(lbconval*λ, λ * 1.0 / lbconval)
-        λmax = max(lbconval*λ, λ * 1.0 / lbconval)
+        λmin = min(lbconval * λ, λ * 1.0 / lbconval)
+        λmax = max(lbconval * λ, λ * 1.0 / lbconval)
         @constraint(
             opt_model,
             pfbacon,

test/analysis/flux_variability_analysis.jl

@@ -75,14 +75,14 @@ end
     fva_max, fva_min = flux_variability_analysis(
         model,
         Tulip.Optimizer;
-        optimum_bound=0.99,
+        optimum_bound = 0.99,
         modifications = [
             change_solver_attribute("IPM_IterationsLimit", 500),
             change_constraint(glucose, -10, -10),
             change_constraint(oxygen, 0.0, 0.0),
         ],
     )
-    
+
     @test isapprox(fva_max["EX_ac_e"]["EX_ac_e"], 8.518549434876208, atol = 1e-6)
     @test isapprox(fva_min["EX_ac_e"]["EX_ac_e"], 7.448388738973361, atol = 1e-6)
 end