updated tests

README.md

@@ -28,46 +28,52 @@ More importantly, it also exposes the user to the core structures used in COBRA,
 
 ## Installation
 
-To install this package: `] add CobraTools`. See the documentation for more information.
+To install this package: `] add https://github.com/stelmo/CobraTools.jl`. See the documentation for more information.
 
 ## Quick Example
-
+Let's use `CobraTools` to perform classic flux balance analysis on an *E. coli* genome-scale metabolic model.
 ```julia
 using CobraTools
 using JuMP
-using GLPK # pick any solver supported by JuMP
+using Tulip # pick any solver supported by JuMP
 
-# import E. coli model
-model = read_model("iJO1366.json") # models have pretty printing
+# Import E. coli model (models have pretty printing)
+model = read_model("iJO1366.json") 
 
-# choose objective to maximize
+# Choose objective to maximize (biomass is a reaction struct, which also has pretty printing)
 biomass = findfirst(model.reactions, "BIOMASS_Ec_iJO1366_WT_53p95M")
 
 # FBA - use convenience functions
-sol = fba(model, biomass, GLPK.Optimizer; solver_attributes=Dict("msg_lev" => GLPK.GLP_MSG_OFF)) # classic flux balance analysis
+sol = fba(model, biomass, Tulip.Optimizer))
+```
 
-# FBA DIY - automatically construct basic JuMP model from a constraint based model
-cbm, v, mb, ubs, lbs = build_cbm(model) # get the constraint based model (cbm) in JuMP format: S*v=b (mb: mass balance constraints) with lbs <= v <= ubs.
-set_optimizer(cbm, GLPK.Optimizer) # use JuMP functions to set optimizer
-set_optimizer_attribute(cbm, "msg_lev", GLPK.GLP_MSG_OFF) # use JuMP functions to set optimizer attributes
-@objective(cbm, Max, v[model[biomass]]) # use index notation to get biomass equation index
-optimize!(cbm)    
-sol = map_fluxes(v, model) # map fluxes to reaction ids. 
-
-# FBA really DIY - manually construct a JuMP model from constraint based model
-S, b, ubvec, lbvec = get_core_model(model) # get S*v = b with lbvec <= v <= ubvec from model
-cbm = JuMP.Model()
-nvars = size(S, 2) # number of variables in model
-v = @variable(cbmodel, v[1:nvars]) # flux variables
-mb = @constraint(cbmodel, mb, S*v .== b) # mass balance
-lbs = @constraint(cbmodel, lbs, lbs .<= v) # lower bounds
-ubs = @constraint(cbmodel, ubs, v .<= ubs) # upper bounds
-
-set_optimizer(cbm, GLPK.Optimizer) # use JuMP functions to set optimizer
-set_optimizer_attribute(cbm, "msg_lev", GLPK.GLP_MSG_OFF) # use JuMP functions to set optimizer attributes
-@objective(cbm, Max, v[model[biomass]]) # use index notation to get biomass equation index
+If you are feeling more adventurous you can perform the optimization yourself using `JuMP`.
+```julia
+# Get the constraint based model (cbm) in JuMP format: S*v=b (mb: mass balance constraints) with lbs <= v <= ubs
+cbm, v, mb, ubs, lbs = build_cbm(model)
+# Use JuMP functions to optimize the constraint based model
+set_optimizer(cbm, Tulip.Optimizer)
+# Use index notation to get biomass equation index
+@objective(cbm, Max, v[model[biomass]])
 optimize!(cbm)    
-sol = map_fluxes(v, model) # map fluxes to reaction ids. 
+# Map fluxes to reaction ids
+sol = map_fluxes(v, model) 
+```
+
+If you are feeling even more adventurous you can do everything yourself!
+```julia
+# Get S*v = b with lbvec <= v <= ubvec from model
+S, b, ubvec, lbvec = get_core_model(model) 
+# Manually define the model in JuMP
+cbm = JuMP.Model(Tulip.Optimizer)
+nvars = size(S, 2)
+v = @variable(cbmodel, v[1:nvars]) 
+mb = @constraint(cbmodel, mb, S*v .== b) 
+lbs = @constraint(cbmodel, lbs, lbs .<= v) 
+ubs = @constraint(cbmodel, ubs, v .<= ubs) 
+@objective(cbm, Max, v[model[biomass]])
+optimize!(cbm)
+sol = map_fluxes(v, model)
 ```
 More funcionality is described in the documention.
 

docs/src/index.md

@@ -17,7 +17,7 @@
 
 ## Installation
 
-To install this package: `] add CobraTools`.
+To install this package: `] add https://github.com/stelmo/CobraTools.jl`.
 
 Some of the optional features used in this package require external programs and/or data to be available. These are described below:
 

test/base/gene_test.jl

@@ -5,16 +5,15 @@
     g.notes = Dict("notes"=>["blah", "blah"])
     g.annotation = Dict("sboterm" => "sbo", "ncbigene" => ["ads", "asds"])
 
-    # @test repr("text/plain", g) == "Gene ID: gene1\nGene name: gene_name\n"
     @test sprint(show, MIME("text/plain"), g) == "Gene ID: gene1\nGene name: gene_name\n"
     
     g2 = Gene("gene2")
    
     genes = [g, g2]
-    @test repr("text/plain", genes) == "Gene set of length: 2\n"
+    @test sprint(show, MIME("text/plain"), genes) == "Gene set of length: 2\n"
 
     gene_list = [[g], [g2]]
-    @test repr("text/plain", gene_list) == "(gene1) or (gene2)\n"
+    @test sprint(show, MIME("text/plain"), gene_list) == "(gene1) or (gene2)\n"
 
     @test genes[g] == 1
     
@@ -26,4 +25,11 @@
     
     dup, ind = check_duplicate_annotations(genes, g3)
     @test dup && ind == 1
+
+    @test isnothing(findfirst(genes, "nope"))
+
+    g4 = Gene("g4")
+    g4.annotation = Dict("ncbigene" => "sbo", "ncbigene" => ["ads22", "asd22s"])
+    dup, ind = check_duplicate_annotations(genes, g4)
+    @test !dup && ind == -1
 end

test/base/metabolite_test.jl

@@ -8,7 +8,7 @@
     m1.notes = Dict("notes"=>["blah", "blah"])
     m1.annotation = Dict("sboterm" => "sbo", "kegg.compound" => ["ads", "asds"])
     
-    @test repr("text/plain", m1) == "Metabolite ID: met1\nMetabolite name: metabolite 1\nFormula: C6H12O6N\nCharge: 1\n"
+    @test sprint(show, MIME("text/plain"), m1) == "Metabolite ID: met1\nMetabolite name: metabolite 1\nFormula: C6H12O6N\nCharge: 1\n"
 
     m2 = Metabolite("met2")
     m2.formula = "C6H12O6N"
@@ -19,7 +19,7 @@
     
     mets = [m1, m2, m3]
 
-    @test repr("text/plain", mets) == "Metabolite set of length: 3\n"
+    @test sprint(show, MIME("text/plain"), mets) == "Metabolite set of length: 3\n"
     
     @test mets[m2] == 2
     
@@ -34,4 +34,13 @@
     
     ats = get_atoms(mms[1])
     @test ats["C"] == 6 && ats["N"] == 1 
+
+    @test isnothing(findfirst(mets, "nope"))
+
+    m4 = Metabolite("met4")
+    m4.formula = "X"
+    m4.annotation = Dict("sboterm" => "sbo", "kegg.compound" => ["adxxx2s", "asdxxxs"])
+
+    dup, ind = check_duplicate_annotations(mets, m4)
+    @test !dup && ind == -1
 end
\ No newline at end of file

test/base/model_test.jl

@@ -37,7 +37,7 @@
     model.metabolites = mets
     model.genes = genes
     
-    @test repr("text/plain", model) == "Constraint based model: model\nNumber of reactions: 4\nNumber of metabolites: 4\nNumber of genes: 3\n"
+    @test sprint(show, MIME("text/plain"), model) == "Constraint based model: model\nNumber of reactions: 4\nNumber of metabolites: 4\nNumber of genes: 3\n"
 
     @test model[r2] == 2
 

test/base/reaction_test.jl

@@ -5,6 +5,14 @@
     m2.formula = "H3C2"
     m3 = Metabolite("m3")
     m4 = Metabolite("m4")
+    m5 = Metabolite("m5")
+    m6 = Metabolite("m6")
+    m7 = Metabolite("m7")
+    m8 = Metabolite("m8")
+    m9 = Metabolite("m9")
+    m10 = Metabolite("m10")
+    m11 = Metabolite("m11")
+    m12 = Metabolite("m12")
     
     g1 = Gene("g1")
     g2 = Gene("g2")
@@ -22,8 +30,16 @@
     r1.annotation = Dict("sboterm" => "sbo", "biocyc" => ["ads", "asds"])
     r1.objective_coefficient = 1.0
 
-    @test repr("text/plain", r1) == "Reaction ID: r1\nReaction name: reaction 1\nReaction subsystem: glycolysis\n1.0 m1 ⟷  1.0 m2\nLower bound: -100.0\nUpper bound: 100.0\nGenes: (g1 and g2) or (g3)\nE.C. number: \n"
+    @test sprint(show, MIME("text/plain"), r1) == "Reaction ID: r1\nReaction name: reaction 1\nReaction subsystem: glycolysis\n1.0 m1 ⟷  1.0 m2\nLower bound: -100.0\nUpper bound: 100.0\nGenes: (g1 and g2) or (g3)\nE.C. number: \n"
     
+    rlongfor = Reaction("rlongfor", Dict(m1 => -1.0, m2 => -1.0, m3 => -1.0, m4 => -1.0, m5 => -1.0, m6 => -1.0,
+    m7 => 1.0, m8 => 1.0, m9 => 1.0, m10 => 1.0, m11 => 1.0, m12 => 1.0,), "for")
+    @test occursin("...", sprint(show, MIME("text/plain"), rlongfor)) # from dictionaries so order is not consistent.
+
+    rlongrev = Reaction("rlongrev", Dict(m1 => -1.0, m2 => -1.0, m3 => -1.0, m4 => -1.0, m5 => -1.0, m6 => -1.0,
+    m7 => 1.0, m8 => 1.0, m9 => 1.0, m10 => 1.0, m11 => 1.0, m12 => 1.0,), "rev")
+    @test occursin("...", sprint(show, MIME("text/plain"), rlongrev))
+
     r2 = Reaction("r2", Dict(m1 => -2.0, m4 => 1.0), "rev")
     @test r2.lb == -1000.0 && r2.ub == 0.0
      
@@ -32,7 +48,7 @@
     
     rxns = [r1, r2, r3]
 
-    @test repr("text/plain", rxns) == "Reaction set of length: 3\n"
+    @test sprint(show, MIME("text/plain"), rxns) == "Reaction set of length: 3\n"
     
     @test rxns[r3] == 3
     
@@ -58,4 +74,8 @@
 
     bal, d = is_mass_balanced(r1)
     @test bal
+
+    @test isnothing(findfirst(rxns, "nope"))
+
+
 end

test/construction/construction_overloading_test.jl

@@ -30,4 +30,10 @@
     rxn = 1.0*nadh + 4.0*h_c + 1.0*q8 ⟶  1.0*q8h2 + 1.0*nad + 3.0*h_p
     @test prod(values(rxn.metabolites)) == -12
     @test ("q8h2_c" in [x.id for x  in keys(rxn.metabolites)])
+
+    rxn = nadh + 4.0*h_c + 1.0*q8 ⟶  1.0*q8h2 + 1.0*nad + 3.0*h_p
+    @test rxn.lb == 0.0 && rxn.ub > 0.0
+
+    @test length(h_p + h_p) == 2
+    @test length(h_p + h_p + h_p) == 3
 end
\ No newline at end of file

test/optimization_analysis/basic_analysis_test.jl

@@ -6,6 +6,10 @@
     biomass = findfirst(model.reactions, "BIOMASS_Ecoli_core_w_GAM")
     optimizer = Tulip.Optimizer # quiet by default
     sol = fba(model, biomass, optimizer)
+
+    flux_vec = [sol[rxn.id] for rxn in model.reactions]
+    sol_mapped = map_fluxes(flux_vec, model)
+    @test isapprox(sol_mapped["BIOMASS_Ecoli_core_w_GAM"], 0.8739215022678488, atol=1e-6)
     @test isapprox(sol["BIOMASS_Ecoli_core_w_GAM"], 0.8739215022678488, atol=1e-6)
 
     # exchange trackers