fixed remaining tests

src/analysis/flux_variability_analysis.jl

@@ -165,7 +165,7 @@ function flux_variability_analysis(
         λmin <= COBREXA.JuMP.objective_function(opt_model) <= λmax # in case there is a negative bound
     )
 
-    for i = 1:length(v)
+    for (i, r_id) in enumerate(reactions(model))
         COBREXA.JuMP.@objective(opt_model, Max, v[i])
         COBREXA.JuMP.optimize!(opt_model)
         status = (
@@ -173,11 +173,11 @@ function flux_variability_analysis(
             COBREXA.JuMP.termination_status(opt_model) == MOI.LOCALLY_SOLVED
         )
         if status
-            fva_max[model.reactions[i].id] =
+            fva_max[r_id] =
                 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
+            fva_max[r_id] = nothing
         end
 
         @objective(opt_model, Min, v[i])
@@ -187,11 +187,11 @@ function flux_variability_analysis(
             termination_status(opt_model) == MOI.LOCALLY_SOLVED
         )
         if status
-            fva_min[model.reactions[i].id] =
+            fva_min[r_id] =
                 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
+            fva_min[r_id] = nothing
         end
     end
 

src/analysis/loopless_flux_balance_analysis.jl

 function _get_boundary_reaction_ids(model::StandardModel)::Array{String,1}
-    return [i.id for i in model.reactions if length(i.metabolites) == 1]
+    return [r.id for r in values(model.reactions) if is_boundary(r)]
 end
 
 function add_cycle_free(fluxes::Dict{String,Float64})
@@ -8,7 +8,7 @@ function add_cycle_free(fluxes::Dict{String,Float64})
         old_objective = objective_function(opt_model)
         boundary_ids = _get_boundary_reaction_ids(model)
         min_objectives = zeros(Int64, 1, length(v))
-        for (i, reaction) in enumerate(model.reactions)
+        for (i, reaction) in enumerate(values(model.reactions))
             id = reaction.id
             if v[i] in old_objective.terms.keys
                 continue

test/analysis/flux_balance_analysis.jl

@@ -50,8 +50,8 @@ end
 
     model = read_model(model_path, StandardModel)
 
-    biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
-    glucose = findfirst(model.reactions, "EX_glc__D_e")
+    biomass = model.reactions["BIOMASS_Ecoli_core_w_GAM"]
+    glucose = model.reactions["EX_glc__D_e"]
     sol = flux_balance_analysis_dict(
         model,
         Tulip.Optimizer;
@@ -68,7 +68,7 @@ end
         atol = TEST_TOLERANCE,
     )
 
-    pfl = findfirst(model.reactions, "PFL")
+    pfl = model.reactions["PFL"]
     pfl_frac = 0.8
     biomass_frac = 0.2
     sol_multi = flux_balance_analysis_dict(

test/analysis/flux_variability_analysis.jl

@@ -69,9 +69,9 @@ end
 
     model = read_model(model_path, StandardModel)
 
-    biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
-    glucose = findfirst(model.reactions, "EX_glc__D_e")
-    oxygen = findfirst(model.reactions, "EX_o2_e")
+    biomass = model.reactions["BIOMASS_Ecoli_core_w_GAM"]
+    glucose = model.reactions["EX_glc__D_e"]
+    oxygen = model.reactions["EX_o2_e"]
     fva_max, fva_min = flux_variability_analysis(
         model,
         Tulip.Optimizer;

test/analysis/parsimonious_flux_balance_analysis.jl

@@ -7,8 +7,8 @@
 
     model = read_model(model_path, StandardModel)
 
-    biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
-    glucose = findfirst(model.reactions, "EX_glc__D_e")
+    biomass = model.reactions["BIOMASS_Ecoli_core_w_GAM"]
+    glucose = model.reactions["EX_glc__D_e"]
 
     d = parsimonious_flux_balance_analysis_dict(
         model,

test/analysis/test_loopless.jl

@@ -45,7 +45,7 @@ end
     model = create_loopless_test_model()
     optimizer = Tulip.Optimizer
     # first test that fba in fact does not give the loopless solution
-    objective = findfirst(model.reactions, "v5")
+    objective = model.reactions["v5"]
     res_fba = flux_balance_analysis_dict(
         model,
         optimizer,

test/runtests.jl

@@ -58,10 +58,10 @@ run_test_file("data", "test_models.jl")
 
 # import base files
 @testset "COBREXA test suite" begin
-    # run_test_dir("types", "Data structures")
-    # run_test_dir("base", "Base functionality")
-    # run_test_dir("io", "I/O functions")
+    run_test_dir("types", "Data structures")
+    run_test_dir("base", "Base functionality")
+    run_test_dir("io", "I/O functions")
     run_test_dir("reconstruction")
-    # run_test_dir("analysis")
-    # run_test_dir("sampling")
+    run_test_dir("analysis")
+    run_test_dir("sampling")
 end

test/sampling/artificially_centered_hit_and_run.jl

@@ -8,8 +8,8 @@
 
     model = read_model(model_path, StandardModel)
 
-    biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
-    glucose = findfirst(model.reactions, "EX_glc__D_e")
+    biomass = model.reactions["BIOMASS_Ecoli_core_w_GAM"
+    glucose = model.reactions["EX_glc__D_e"
     opt_model = flux_balance_analysis(
         model,
         Tulip.Optimizer;
@@ -19,7 +19,7 @@
             change_solver_attribute("IPM_IterationsLimit", 500),
         ],
     )
-    biomass_index = model[biomass]
+    biomass_index = index_of(biomass, model)
     λ = JuMP.value(opt_model[:x][biomass_index])
     change_constraint(biomass, 0.99 * λ, λ)(model, opt_model)
 

test/sampling/hit_and_run.jl

 @testset "Sampling Tests" begin
-    # these tests are not very good - sampling needs work
+    # # these tests are not very good - sampling needs work
     model_path = download_data_file(
         "http://bigg.ucsd.edu/static/models/e_coli_core.json",
         joinpath("data", "e_coli_core.json"),
@@ -8,8 +8,8 @@
 
     model = read_model(model_path, StandardModel)
 
-    biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
-    glucose = findfirst(model.reactions, "EX_glc__D_e")
+    biomass = model.reactions["BIOMASS_Ecoli_core_w_GAM"]
+    glucose = model.reactions["EX_glc__D_e"]
     opt_model = flux_balance_analysis(
         model,
         Tulip.Optimizer;
@@ -19,13 +19,13 @@
             change_solver_attribute("IPM_IterationsLimit", 500),
         ],
     )
-    biomass_index = model[biomass]
+    biomass_index = index_of(biomass, reactions(model))
     λ = JuMP.value(opt_model[:x][biomass_index])
     change_constraint(biomass, 0.99 * λ, λ)(model, opt_model)
 
     samples = hit_and_run(100_000, opt_model, keepevery = 10, samplesize = 5000)
 
-    # The sampling converges very randomly and extremely approximately, so only
-    # test a rough result
+    # # The sampling converges very randomly and extremely approximately, so only
+    # # test a rough result
     @test isapprox(mean(samples[64, :]), 8.9, atol = 0.1)
 end