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test: re-introducing @allocations test #213

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test: re-introducing @allocations test #213

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10 changes: 5 additions & 5 deletions test/1_test_sim_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -108,11 +108,11 @@ end
u, d = [10, 50], Float64[]
@test updatestate!(linmodel1, u) ≈ zeros(2)
@test updatestate!(linmodel1, u, d) ≈ zeros(2)
@test_skip @allocations(updatestate!(linmodel1, u)) == 0
@test @allocations(updatestate!(linmodel1, u)) == 0
@test linmodel1.x0 ≈ zeros(2)
@test evaloutput(linmodel1) ≈ linmodel1() ≈ [50,30]
@test evaloutput(linmodel1, Float64[]) ≈ linmodel1(Float64[]) ≈ [50,30]
@test_skip @allocations(evaloutput(linmodel1)) == 0
@test @allocations(evaloutput(linmodel1)) == 0
x = initstate!(linmodel1, [10, 60])
@test evaloutput(linmodel1) ≈ [50 + 19.0, 30 + 7.4]
@test preparestate!(linmodel1) ≈ x # new method
Expand Down Expand Up @@ -283,11 +283,11 @@ end
u, d = zeros(2), Float64[]
@test updatestate!(nonlinmodel, u) ≈ zeros(2)
@test updatestate!(nonlinmodel, u, d) ≈ zeros(2)
@test_skip @allocations(updatestate!(nonlinmodel, u)) == 0
@test @allocations(updatestate!(nonlinmodel, u)) == 0
@test nonlinmodel.x0 ≈ zeros(2)
@test evaloutput(nonlinmodel) ≈ nonlinmodel() ≈ zeros(2)
@test evaloutput(nonlinmodel, d) ≈ nonlinmodel(Float64[]) ≈ zeros(2)
@test_skip @allocations(evaloutput(nonlinmodel)) == 0
@test @allocations(evaloutput(nonlinmodel)) == 0

x = initstate!(nonlinmodel, [0, 10]) # do nothing for NonLinModel
@test evaloutput(nonlinmodel) ≈ [0, 0]
Expand Down Expand Up @@ -387,7 +387,7 @@ end
# return nothing (see this issue : https://github.com/JuliaLang/julia/issues/51112):
linearize2!(linmodel, model) = (linearize!(linmodel, model); nothing)
linearize2!(linmodel4, nonlinmodel4)
@test_skip @allocations(linearize2!(linmodel4, nonlinmodel4)) == 0
@test @allocations(linearize2!(linmodel4, nonlinmodel4)) == 0
end

@testitem "NonLinModel real time simulations" setup=[SetupMPCtests] begin
Expand Down
36 changes: 18 additions & 18 deletions test/2_test_state_estim.jl
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Expand Up @@ -71,12 +71,12 @@ end
preparestate!(manual1, y)
@test updatestate!(manual1, u, y, d) ≈ zeros(4)
@test manual1.x̂0 ≈ zeros(4)
@test_skip @allocations(preparestate!(manual1, y)) == 0
@test_skip @allocations(updatestate!(manual1, u, y)) == 0
@test @allocations(preparestate!(manual1, y)) == 0
@test @allocations(updatestate!(manual1, u, y)) == 0
preparestate!(manual1, y)
@test evaloutput(manual1) ≈ manual1() ≈ [50, 30]
@test evaloutput(manual1, d) ≈ manual1(d) ≈ [50, 30]
@test_skip @allocations(evaloutput(manual1, d)) == 0
@test @allocations(evaloutput(manual1, d)) == 0
@test initstate!(manual1, [10, 50], [50, 30+1]) ≈ [zeros(3); [1]]
linmodel2 = LinModel(append(tf(1, [1, 0]), tf(2, [10, 1])), 1.0)
manual2 = SteadyKalmanFilter(linmodel2, nint_u=[1, 1], direct=false)
Expand Down Expand Up @@ -205,12 +205,12 @@ end
preparestate!(kalmanfilter1, y)
@test updatestate!(kalmanfilter1, u, y, d) ≈ zeros(4)
@test kalmanfilter1.x̂0 ≈ zeros(4)
@test_skip @allocations(preparestate!(kalmanfilter1, y)) == 0
@test_skip @allocations(updatestate!(kalmanfilter1, u, y)) == 0
@test @allocations(preparestate!(kalmanfilter1, y)) == 0
@test @allocations(updatestate!(kalmanfilter1, u, y)) == 0
preparestate!(kalmanfilter1, y)
@test evaloutput(kalmanfilter1) ≈ kalmanfilter1() ≈ [50, 30]
@test evaloutput(kalmanfilter1, d) ≈ kalmanfilter1(d) ≈ [50, 30]
@test_skip @allocations(evaloutput(kalmanfilter1, d)) == 0
@test @allocations(evaloutput(kalmanfilter1, d)) == 0
@test initstate!(kalmanfilter1, [10, 50], [50, 30+1]) ≈ [zeros(3); [1]]
setstate!(kalmanfilter1, [1,2,3,4], diagm(.1:.1:.4))
@test kalmanfilter1.x̂0 ≈ [1,2,3,4]
Expand Down Expand Up @@ -326,12 +326,12 @@ end
preparestate!(lo1, y)
@test updatestate!(lo1, u, y, d) ≈ zeros(4)
@test lo1.x̂0 ≈ zeros(4)
@test_skip @allocations(preparestate!(lo1, y)) == 0
@test_skip @allocations(updatestate!(lo1, u, y)) == 0
@test @allocations(preparestate!(lo1, y)) == 0
@test @allocations(updatestate!(lo1, u, y)) == 0
preparestate!(lo1, y)
@test evaloutput(lo1) ≈ lo1() ≈ [50, 30]
@test evaloutput(lo1, d) ≈ lo1(d) ≈ [50, 30]
@test_skip @allocations(evaloutput(lo1, d)) == 0
@test @allocations(evaloutput(lo1, d)) == 0
@test initstate!(lo1, [10, 50], [50, 30+1]) ≈ [zeros(3); [1]]
setstate!(lo1, [1,2,3,4])
@test lo1.x̂0 ≈ [1,2,3,4]
Expand Down Expand Up @@ -457,11 +457,11 @@ end
@test updatestate!(internalmodel1, u, y, d) ≈ zeros(2)
@test internalmodel1.x̂d ≈ internalmodel1.x̂0 ≈ zeros(2)
@test internalmodel1.x̂s ≈ ones(2)
@test_skip @allocations(preparestate!(internalmodel1, y)) == 0
@test_skip @allocations(updatestate!(internalmodel1, u, y)) == 0
@test @allocations(preparestate!(internalmodel1, y)) == 0
@test @allocations(updatestate!(internalmodel1, u, y)) == 0
preparestate!(internalmodel1, y)
@test evaloutput(internalmodel1, d) ≈ [51,31]
@test_skip @allocations(evaloutput(internalmodel1, d)) == 0
@test @allocations(evaloutput(internalmodel1, d)) == 0
@test initstate!(internalmodel1, [10, 50], [50, 30]) ≈ zeros(2)
@test internalmodel1.x̂s ≈ zeros(2)
setstate!(internalmodel1, [1,2])
Expand Down Expand Up @@ -593,12 +593,12 @@ end
preparestate!(ukf1, y)
@test updatestate!(ukf1, u, y, d) ≈ zeros(4) atol=1e-9
@test ukf1.x̂0 ≈ zeros(4) atol=1e-9
@test_skip @allocations(preparestate!(ukf1, y)) == 0
@test_skip @allocations(updatestate!(ukf1, u, y)) == 0
@test @allocations(preparestate!(ukf1, y)) == 0
@test @allocations(updatestate!(ukf1, u, y)) == 0
preparestate!(ukf1, y)
@test evaloutput(ukf1) ≈ ukf1() ≈ [50, 30]
@test evaloutput(ukf1, d) ≈ ukf1(d) ≈ [50, 30]
@test_skip @allocations(evaloutput(ukf1, d)) == 0
@test @allocations(evaloutput(ukf1, d)) == 0
@test initstate!(ukf1, [10, 50], [50, 30+1]) ≈ zeros(4) atol=1e-9
setstate!(ukf1, [1,2,3,4], diagm(.1:.1:.4))
@test ukf1.x̂0 ≈ [1,2,3,4]
Expand Down Expand Up @@ -752,12 +752,12 @@ end
preparestate!(ekf1, y)
@test updatestate!(ekf1, u, y, d) ≈ zeros(4) atol=1e-9
@test ekf1.x̂0 ≈ zeros(4) atol=1e-9
@test_skip @allocations(preparestate!(ekf1, y)) == 0
@test_skip @allocations(updatestate!(ekf1, u, y)) == 0
@test @allocations(preparestate!(ekf1, y)) == 0
@test @allocations(updatestate!(ekf1, u, y)) == 0
preparestate!(ekf1, y)
@test evaloutput(ekf1) ≈ ekf1() ≈ [50, 30]
@test evaloutput(ekf1, d) ≈ ekf1(d) ≈ [50, 30]
@test_skip @allocations(evaloutput(ekf1, d)) == 0
@test @allocations(evaloutput(ekf1, d)) == 0
@test initstate!(ekf1, [10, 50], [50, 30+1]) ≈ zeros(4);
setstate!(ekf1, [1,2,3,4], diagm(.1:.1:.4))
@test ekf1.x̂0 ≈ [1,2,3,4]
Expand Down
2 changes: 1 addition & 1 deletion test/3_test_predictive_control.jl
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Expand Up @@ -496,7 +496,7 @@ end
@test u ≈ [1] atol=1e-2
u = mpc1(r)
@test u ≈ [1] atol=1e-2
@test_skip @allocations(moveinput!(mpc1, r)) == 0
@test @allocations(moveinput!(mpc1, r)) == 0
info = getinfo(mpc1)
@test info[:u] ≈ u
@test info[:Ŷ][end] ≈ r[1] atol=1e-2
Expand Down
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