use pkg relativity in test

test/analysis/fba.jl

@@ -2,19 +2,19 @@
     cp = test_simpleLP()
     (lp, x) = flux_balance_analysis(cp, GLPK.Optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test sol ≈ [1.0, 2.0]
 
     (lp, x) = flux_balance_analysis(cp, Clp.Optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test sol ≈ [1.0, 2.0]
 
     # test the maximization of the objective
     cp = test_simpleLP2()
     (lp, x) = flux_balance_analysis(cp, GLPK.Optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test sol ≈ [-1.0, 2.0]
 
     # test with a more biologically meaningfull model
@@ -29,7 +29,7 @@
 
     (lp, x) = flux_balance_analysis(cp, GLPK.Optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test objective_value(lp) ≈ expected_optimum
     @test cp.c' * sol ≈ expected_optimum
 
@@ -54,7 +54,7 @@ end
     # FBA
     biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
     cons = Dict("EX_glc__D_e" => (-12.0, -12.0))
-    optimizer = Tulip.Optimizer # quiet by default
+    optimizer = COBREXA.Tulip.Optimizer # quiet by default
     sol = fba(model, optimizer; objective_func = biomass, constraints = cons)
     pfl = findfirst(model.reactions, "PFL")
     solmulti = fba(model, optimizer; objective_func = [biomass, pfl], weights = [0.8, 0.2]) # classic flux balance analysis

test/analysis/fva.jl

@@ -74,7 +74,7 @@ end
     pfl = findfirst(model.reactions, "PFL")
 
     # FVA
-    optimizer = Tulip.Optimizer
+    optimizer = COBREXA.Tulip.Optimizer
     atts = Dict("IPM_IterationsLimit" => 500)
     cons = Dict("EX_glc__D_e" => (-10.0, -10.0))
     fva_max, fva_min =

test/analysis/pfba.jl

@@ -27,7 +27,7 @@
     ) # just see if it works - OSQP is a terrible LP solver
     sol = pfba(
         model,
-        [Tulip.Optimizer, OSQP.Optimizer];
+        [COBREXA.Tulip.Optimizer, COBREXA.OSQP.Optimizer];
         objective_func = biomass,
         solver_attributes = Dict("opt1" => Dict{Any,Any}(), "opt2" => atts),
     ) # try two optimizers

test/base/solver.jl

@@ -4,12 +4,12 @@
     optimizer = GLPK.Optimizer
     (lp, x) = optimize_model(cp, optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test sol ≈ [1.0, 2.0]
 
     optimizer = Clp.Optimizer
     (lp, x) = optimize_model(cp, optimizer)
     @test termination_status(lp) === MOI.OPTIMAL
-    sol = JuMP.value.(x)
+    sol = COBREXA.JuMP.value.(x)
     @test sol ≈ [1.0, 2.0]
 end

test/runtests.jl

@@ -14,7 +14,6 @@ using OSQP
 using Statistics
 using JSON
 using Measurements
-using Downloads
 
 function run_test_file(path...)
     fn = joinpath(path...)
@@ -42,7 +41,7 @@ function download_data_file(url, path, hash)
         return path
     end
 
-    Downloads.download(url, path)
+    COBREXA.Downloads.download(url, path)
     check_data_file_hash(path, hash)
     return path
 end

test/sampling/sampling_tools_test.jl

@@ -8,7 +8,7 @@
         ),
     )
 
-    optimizer = Tulip.Optimizer
+    optimizer = COBREXA.Tulip.Optimizer
     biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
     cons = Dict("EX_glc__D_e" => (-12.0, -12.0))
     atts = Dict("IPM_IterationsLimit" => 110)