fix warn messages

Author: TorkelE

test/extensions/structural_identifiability.jl

@@ -316,6 +316,7 @@ end
 ### Other Tests ###
 
 # Checks that identifiability can be assessed for coupled CRN/DAE systems.
+# `remove_conserved = false` is used to remove info print statement from log.
 let
     rs = @reaction_network begin
         @parameters k c1 c2
@@ -329,19 +330,20 @@ let
     @unpack p, d, k, c1, c2 = rs
 
     # Tests identifiability assessment when all unknowns are measured.
-    gi_1 = assess_identifiability(rs; measured_quantities = [:X, :V, :C], loglevel)
-    li_1 = assess_local_identifiability(rs; measured_quantities = [:X, :V, :C], loglevel)
-    ifs_1 = find_identifiable_functions(rs; measured_quantities = [:X, :V, :C], loglevel)
+    remove_conserved = false
+    gi_1 = assess_identifiability(rs; measured_quantities = [:X, :V, :C], loglevel, remove_conserved)
+    li_1 = assess_local_identifiability(rs; measured_quantities = [:X, :V, :C], loglevel, remove_conserved)
+    ifs_1 = find_identifiable_functions(rs; measured_quantities = [:X, :V, :C], loglevel, remove_conserved)
     @test sym_dict(gi_1) == Dict([:X => :globally, :C => :globally, :V => :globally, :k => :globally, 
                       :c1 => :nonidentifiable, :c2 => :nonidentifiable, :p => :globally, :d => :globally])
     @test sym_dict(li_1) == Dict([:X => 1, :C => 1, :V => 1, :k => 1, :c1 => 0, :c2 => 0, :p => 1, :d => 1])
     @test issetequal(ifs_1, [d, p, k, c1 + c2])
 
     # Tests identifiability assessment when only variables are measured. 
     # Checks that a parameter in an equation can be set as known.
-    gi_2 = assess_identifiability(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel)
-    li_2 = assess_local_identifiability(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel)
-    ifs_2 = find_identifiable_functions(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel)
+    gi_2 = assess_identifiability(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel, remove_conserved)
+    li_2 = assess_local_identifiability(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel, remove_conserved)
+    ifs_2 = find_identifiable_functions(rs; measured_quantities = [:V, :C], known_p = [:c1], loglevel, remove_conserved)
     @test sym_dict(gi_2) == Dict([:X => :nonidentifiable, :C => :globally, :V => :globally, :k => :nonidentifiable, 
                       :c1 => :globally, :c2 => :nonidentifiable, :p => :nonidentifiable, :d => :globally])
     @test sym_dict(li_2) == Dict([:X => 0, :C => 1, :V => 1, :k => 0, :c1 => 1, :c2 => 0, :p => 0, :d => 1])

test/simulation_and_solving/solve_nonlinear.jl

@@ -90,7 +90,7 @@ let
     # Creates NonlinearProblem.
     u0 = [steady_state_network_3.X => rand(), steady_state_network_3.Y => rand() + 1.0, steady_state_network_3.Y2 => rand() + 3.0, steady_state_network_3.XY2 => 0.0]
     p = [:p => rand()+1.0, :d => 0.5, :k1 => 1.0, :k2 => 2.0, :k3 => 3.0, :k4 => 4.0]
-    nl_prob_1 = NonlinearProblem(steady_state_network_3, u0, p; remove_conserved = true)
+    nl_prob_1 = NonlinearProblem(steady_state_network_3, u0, p; remove_conserved = true, remove_conserved_warn = false)
     nl_prob_2 = NonlinearProblem(steady_state_network_3, u0, p)
 
     # Solves it using standard algorithm and simulation based algorithm.