Merge pull request #411 from isaacsas/fix_test_warnings

Fix test warnings

test/constant_rate.jl

@@ -33,7 +33,7 @@ nums = Int[]
 @time for i in 1:10000
     local jump_prob = JumpProblem(prob, Direct(), jump, jump2; rng = rng)
     local sol = solve(jump_prob, FunctionMap())
-    push!(nums, sol[end])
+    push!(nums, sol.u[end])
 end
 
 @test mean(nums) - 45 < 1
@@ -47,7 +47,7 @@ nums = Int[]
 @time for i in 1:10000
     local jump_prob = JumpProblem(prob, Direct(), jump, jump2; rng = rng)
     local sol = solve(jump_prob, FunctionMap())
-    push!(nums, sol[2])
+    push!(nums, sol.u[2])
 end
 
 @test sum(nums .== 0) / 10000 - 0.33 < 0.02

test/ensemble_uniqueness.jl

@@ -9,12 +9,12 @@ u0 = [0]
 prob = DiscreteProblem(u0, (0.0, 100.0))
 jump_prob = JumpProblem(prob, Direct(), j1, j2; rng = rng)
 sol = solve(EnsembleProblem(jump_prob), FunctionMap(), trajectories = 3)
-@test Array(sol[1]) !== Array(sol[2])
-@test Array(sol[1]) !== Array(sol[3])
-@test Array(sol[2]) !== Array(sol[3])
-@test eltype(sol[1].u[1]) == Int
+@test Array(sol.u[1]) !== Array(sol.u[2])
+@test Array(sol.u[1]) !== Array(sol.u[3])
+@test Array(sol.u[2]) !== Array(sol.u[3])
+@test eltype(sol.u[1].u[1]) == Int
 sol = solve(EnsembleProblem(jump_prob), SSAStepper(), trajectories = 3)
-@test Array(sol[1]) !== Array(sol[2])
-@test Array(sol[1]) !== Array(sol[3])
-@test Array(sol[2]) !== Array(sol[3])
-@test eltype(sol[1].u[1]) == Int
+@test Array(sol.u[1]) !== Array(sol.u[2])
+@test Array(sol.u[1]) !== Array(sol.u[3])
+@test Array(sol.u[2]) !== Array(sol.u[3])
+@test eltype(sol.u[1].u[1]) == Int

test/extinction_test.jl

@@ -58,15 +58,15 @@ sol = solve(jprob, SSAStepper(), callback = cb, save_end = false)
 @test sol.t[end] < 1000.0
 
 # test terminate
-function extinction_condition(u, t, integrator)
+function extinction_condition2(u, t, integrator)
     u[1] == 1
 end
-function extinction_affect!(integrator)
+function extinction_affect!2(integrator)
     (saved, savedexactly) = savevalues!(integrator, true)
     terminate!(integrator)
     nothing
 end
-cb = DiscreteCallback(extinction_condition, extinction_affect!,
+cb = DiscreteCallback(extinction_condition2, extinction_affect!2,
     save_positions = (false, false))
 dprob = DiscreteProblem(u0, (0.0, 1000.0), rates)
 jprob = JumpProblem(dprob, Direct(), majump; save_positions = (false, false), rng = rng)

test/longtimes_test.jl

@@ -13,4 +13,4 @@ dt = tspan[2] / 1000
 dprob = DiscreteProblem(u0, tspan, p)
 jprob = JumpProblem(dprob, Direct(), maj, save_positions = (false, false), rng = rng)
 sol = solve(jprob, SSAStepper(), saveat = tspan[1]:dt:tspan[2])
-@test length(unique(sol[(end - 10):end][:])) > 1
+@test length(unique(sol.u[(end - 10):end][:])) > 1

test/monte_carlo_test.jl

@@ -12,11 +12,11 @@ jump_prob = JumpProblem(prob, Direct(), jump; rng = rng)
 monte_prob = EnsembleProblem(jump_prob)
 sol = solve(monte_prob, SRIW1(), EnsembleSerial(), trajectories = 3,
     save_everystep = false, dt = 0.001, adaptive = false)
-@test sol[1].t[2] != sol[2].t[2] != sol[3].t[2]
+@test sol.u[1].t[2] != sol.u[2].t[2] != sol.u[3].t[2]
 
 jump = ConstantRateJump(rate, affect!)
 jump_prob = JumpProblem(prob, Direct(), jump, save_positions = (true, false), rng = rng)
 monte_prob = EnsembleProblem(jump_prob)
 sol = solve(monte_prob, SRIW1(), EnsembleSerial(), trajectories = 3,
     save_everystep = false, dt = 0.001, adaptive = false)
-@test sol[1].t[2] != sol[2].t[2] != sol[3].t[2]
+@test sol.u[1].t[2] != sol.u[2].t[2] != sol.u[3].t[2]

test/saveat_regression.jl

@@ -18,7 +18,7 @@ sol = JumpProcesses.solve(EnsembleProblem(jprob), SSAStepper(), saveat = ts,
     trajectories = Nsims)
 
 for i in 1:length(sol)
-    NA .+= sol[i][1, :]
+    NA .+= sol.u[i][1, :]
 end
 
 for i in 1:length(ts)
@@ -33,7 +33,7 @@ sol = JumpProcesses.solve(EnsembleProblem(jprob), SSAStepper(), trajectories = N
 
 for i in 1:Nsims
     for n in 1:length(ts)
-        NA[n] += sol[i](ts[n])[1]
+        NA[n] += sol.u[i](ts[n])[1]
     end
 end
 

test/spatial/diffusion.jl

@@ -45,7 +45,7 @@ starting_state = zeros(Int, length(u0), num_nodes)
 center_node = trunc(Int, (num_nodes + 1) / 2)
 starting_state[:, center_node] = copy(u0)
 tspan = (0.0, tf)
-prob = DiscreteProblem(starting_state, tspan, [])
+prob = DiscreteProblem(starting_state, tspan)
 
 hopping_rate = diffusivity * (linear_size / domain_size)^2
 hopping_constants = [hopping_rate for i in starting_state]
@@ -162,7 +162,7 @@ for ci in CartesianIndices(hopping_constants)
 end
 starting_state = 25 * ones(Int, length(u0), num_nodes)
 tspan = (0.0, 10.0)
-prob = DiscreteProblem(starting_state, tspan, [])
+prob = DiscreteProblem(starting_state, tspan)
 
 jp = JumpProblem(prob, NSM(), majumps, hopping_constants = hopping_constants,
     spatial_system = grid, save_positions = (false, false), rng = rng)

test/spatial/spatial_majump.jl

@@ -116,8 +116,10 @@ for spatial_jump_prob in uniform_jump_problems
 end
 
 # birth and death zero outside of center site
-f(u, p, t) = L * u - diagm([0.0, 0.0, death_rate, 0.0, 0.0]) * u + [0.0, 0.0, 1.0, 0.0, 0.0]
-ode_prob = ODEProblem(f, zeros(num_nodes), tspan)
+function f2(u, p, t)
+    L * u - diagm([0.0, 0.0, death_rate, 0.0, 0.0]) * u + [0.0, 0.0, 1.0, 0.0, 0.0]
+end
+ode_prob = ODEProblem(f2, zeros(num_nodes), tspan)
 sol = solve(ode_prob, Tsit5())
 
 solution = solve(non_uniform_jump_problems[1], SSAStepper())
@@ -129,8 +131,8 @@ for (i, d) in enumerate(diff)
 end
 
 # birth everywhere, death only at center site
-f(u, p, t) = L * u - diagm([0.0, 0.0, death_rate, 0.0, 0.0]) * u + ones(num_nodes)
-ode_prob = ODEProblem(f, zeros(num_nodes), tspan)
+f3(u, p, t) = L * u - diagm([0.0, 0.0, death_rate, 0.0, 0.0]) * u + ones(num_nodes)
+ode_prob = ODEProblem(f3, zeros(num_nodes), tspan)
 sol = solve(ode_prob, Tsit5())
 
 solution = solve(non_uniform_jump_problems[2], SSAStepper())
@@ -142,8 +144,10 @@ for (i, d) in enumerate(diff)
 end
 
 # birth on left end, death on right end
-f(u, p, t) = L * u - diagm([0.0, 0.0, 0.0, 0.0, death_rate]) * u + [1.0, 0.0, 0.0, 0.0, 0.0]
-ode_prob = ODEProblem(f, zeros(num_nodes), tspan)
+function f4(u, p, t)
+    L * u - diagm([0.0, 0.0, 0.0, 0.0, death_rate]) * u + [1.0, 0.0, 0.0, 0.0, 0.0]
+end
+ode_prob = ODEProblem(f4, zeros(num_nodes), tspan)
 sol = solve(ode_prob, Tsit5())
 
 solution = solve(non_uniform_jump_problems[3], SSAStepper())

test/variable_rate.jl

@@ -40,8 +40,8 @@ sol = solve(jump_prob, Rosenbrock23())
 # @show sol[end]
 # display(sol[end])
 
-@test maximum([sol[i][2] for i in 1:length(sol)]) <= 1e-12
-@test maximum([sol[i][3] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][2] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][3] for i in 1:length(sol)]) <= 1e-12
 
 g = function (du, u, p, t)
     du[1] = u[1]
@@ -52,8 +52,8 @@ jump_prob = JumpProblem(prob, Direct(), jump, jump2; rng = rng)
 
 sol = solve(jump_prob, SRIW1())
 
-@test maximum([sol[i][2] for i in 1:length(sol)]) <= 1e-12
-@test maximum([sol[i][3] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][2] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][3] for i in 1:length(sol)]) <= 1e-12
 
 function ff(du, u, p, t)
     if p == 0
@@ -95,16 +95,16 @@ jump = ConstantRateJump(rate2, affect2!)
 jump_prob = JumpProblem(prob, Direct(), jump; rng = rng)
 sol = solve(jump_prob, Tsit5())
 sol(4.0)
-sol[4]
+sol.u[4]
 
-rate2(u, p, t) = u[1]
+rate2b(u, p, t) = u[1]
 affect2!(integrator) = (integrator.u[1] = integrator.u[1] / 2)
-jump = VariableRateJump(rate2, affect2!)
+jump = VariableRateJump(rate2b, affect2!)
 jump2 = deepcopy(jump)
 jump_prob = JumpProblem(prob, Direct(), jump, jump2; rng = rng)
 sol = solve(jump_prob, Tsit5())
 sol(4.0)
-sol[4]
+sol.u[4]
 
 function g2(du, u, p, t)
     du[1] = u[1]
@@ -114,7 +114,7 @@ prob = SDEProblem(f2, g2, [0.2], (0.0, 10.0))
 jump_prob = JumpProblem(prob, Direct(), jump, jump2; rng = rng)
 sol = solve(jump_prob, SRIW1())
 sol(4.0)
-sol[4]
+sol.u[4]
 
 function f3(du, u, p, t)
     du .= u
@@ -151,7 +151,7 @@ function drift(x, p, t)
     return p * x
 end
 
-function rate2(x, p, t)
+function rate2c(x, p, t)
     return 3 * max(0.0, x[1])
 end
 
@@ -160,7 +160,7 @@ function affect!2(integrator)
 end
 x0 = rand(2)
 prob = ODEProblem(drift, x0, (0.0, 10.0), 2.0)
-jump = VariableRateJump(rate2, affect!2)
+jump = VariableRateJump(rate2c, affect!2)
 jump_prob = JumpProblem(prob, Direct(), jump)
 
 # test to check lack of dependency graphs is caught in Coevolve for systems with non-maj