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symbolic_indexing_interface.jl
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using ModelingToolkit, SymbolicIndexingInterface, SciMLBase
using ModelingToolkit: t_nounits as t, D_nounits as D, ParameterIndex
using SciMLStructures: Tunable
@testset "ODESystem" begin
@parameters a b
@variables x(t)=1.0 y(t)=2.0 xy(t)
eqs = [D(x) ~ a * y + t, D(y) ~ b * t]
@named odesys = ODESystem(eqs, t, [x, y], [a, b]; observed = [xy ~ x + y])
odesys = complete(odesys)
@test SymbolicIndexingInterface.supports_tuple_observed(odesys)
@test all(is_variable.((odesys,), [x, y, 1, 2, :x, :y]))
@test all(.!is_variable.((odesys,), [a, b, t, 3, 0, :a, :b]))
@test variable_index.((odesys,), [x, y, a, b, t, 1, 2, :x, :y, :a, :b]) ==
[1, 2, nothing, nothing, nothing, 1, 2, 1, 2, nothing, nothing]
@test isequal(variable_symbols(odesys), [x, y])
@test all(is_parameter.((odesys,), [a, b, ParameterIndex(Tunable(), 1), :a, :b]))
@test all(.!is_parameter.((odesys,), [x, y, t, 3, 0, :x, :y]))
@test parameter_index(odesys, a) == parameter_index(odesys, :a)
@test parameter_index(odesys, a) isa ParameterIndex{Tunable, Int}
@test parameter_index(odesys, b) == parameter_index(odesys, :b)
@test parameter_index(odesys, b) isa ParameterIndex{Tunable, Int}
@test parameter_index.(
(odesys,), [x, y, t, ParameterIndex(Tunable(), 1), :x, :y]) ==
[nothing, nothing, nothing, ParameterIndex(Tunable(), 1), nothing, nothing]
@test isequal(
Set(parameter_symbols(odesys)), Set([a, b, Initial(x), Initial(y), Initial(xy)]))
@test all(is_independent_variable.((odesys,), [t, :t]))
@test all(.!is_independent_variable.((odesys,), [x, y, a, :x, :y, :a]))
@test isequal(independent_variable_symbols(odesys), [t])
@test is_time_dependent(odesys)
@test constant_structure(odesys)
@test !isempty(default_values(odesys))
@test default_values(odesys)[x] == 1.0
@test default_values(odesys)[y] == 2.0
@test isequal(default_values(odesys)[xy], x + y)
prob = ODEProblem(odesys, [], (0.0, 1.0), [a => 1.0, b => 2.0])
getter = getu(odesys, (x + 1, x + 2))
@test getter(prob) isa Tuple
@test_nowarn @inferred getter(prob)
getter = getp(odesys, (a + 1, a + 2))
@test getter(prob) isa Tuple
@test_nowarn @inferred getter(prob)
@named odesys = ODESystem(
eqs, t, [x, y], [a, b]; defaults = [xy => 3.0], observed = [xy ~ x + y])
odesys = complete(odesys)
@test default_values(odesys)[xy] == 3.0
pobs = parameter_observed(odesys, a + b)
@test isempty(get_all_timeseries_indexes(odesys, a + b))
@test pobs(
ModelingToolkit.MTKParameters(odesys, [a => 1.0, b => 2.0]), 0.0) ≈ 3.0
pobs = parameter_observed(odesys, [a + b, a - b])
@test isempty(get_all_timeseries_indexes(odesys, [a + b, a - b]))
@test pobs(
ModelingToolkit.MTKParameters(odesys, [a => 1.0, b => 2.0]), 0.0) ≈ [3.0, -1.0]
end
# @testset "Clock system" begin
# dt = 0.1
# dt2 = 0.2
# @variables x(t)=0 y(t)=0 u(t)=0 yd1(t)=0 ud1(t)=0 yd2(t)=0 ud2(t)=0
# @parameters kp=1 r=1
# eqs = [
# # controller (time discrete part `dt=0.1`)
# yd1 ~ Sample(t, dt)(y)
# ud1 ~ kp * (r - yd1)
# # controller (time discrete part `dt=0.2`)
# yd2 ~ Sample(t, dt2)(y)
# ud2 ~ kp * (r - yd2)
# # plant (time continuous part)
# u ~ Hold(ud1) + Hold(ud2)
# D(x) ~ -x + u
# y ~ x]
# @mtkbuild cl = ODESystem(eqs, t)
# partition1_params = [Hold(ud1), Sample(t, dt)(y), ud1, yd1]
# partition2_params = [Hold(ud2), Sample(t, dt2)(y), ud2, yd2]
# @test all(
# Base.Fix1(is_timeseries_parameter, cl), vcat(partition1_params, partition2_params))
# @test allequal(timeseries_parameter_index(cl, p).timeseries_idx
# for p in partition1_params)
# @test allequal(timeseries_parameter_index(cl, p).timeseries_idx
# for p in partition2_params)
# tsidx1 = timeseries_parameter_index(cl, partition1_params[1]).timeseries_idx
# tsidx2 = timeseries_parameter_index(cl, partition2_params[1]).timeseries_idx
# @test tsidx1 != tsidx2
# ps = ModelingToolkit.MTKParameters(cl, [kp => 1.0, Sample(t, dt)(y) => 1.0])
# pobs = parameter_observed(cl, Shift(t, 1)(yd1))
# @test pobs.timeseries_idx == tsidx1
# @test pobs.observed_fn(ps, 0.0) == 1.0
# pobs = parameter_observed(cl, [Shift(t, 1)(yd1), Shift(t, 1)(ud1)])
# @test pobs.timeseries_idx == tsidx1
# @test pobs.observed_fn(ps, 0.0) == [1.0, 0.0]
# pobs = parameter_observed(cl, [Shift(t, 1)(yd1), Shift(t, 1)(ud2)])
# @test pobs.timeseries_idx === nothing
# @test pobs.observed_fn(ps, 0.0) == [1.0, 1.0]
# end
@testset "Nonlinear system" begin
@variables x y z
@parameters σ ρ β
eqs = [0 ~ σ * (y - x),
0 ~ x * (ρ - z) - y,
0 ~ x * y - β * z]
@named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
ns = complete(ns)
@test SymbolicIndexingInterface.supports_tuple_observed(ns)
@test !is_time_dependent(ns)
ps = ModelingToolkit.MTKParameters(ns, [σ => 1.0, ρ => 2.0, β => 3.0])
pobs = parameter_observed(ns, σ + ρ)
@test isempty(get_all_timeseries_indexes(ns, σ + ρ))
@test pobs(ps) == 3.0
pobs = parameter_observed(ns, [σ + ρ, ρ + β])
@test isempty(get_all_timeseries_indexes(ns, [σ + ρ, ρ + β]))
@test pobs(ps) == [3.0, 5.0]
prob = NonlinearProblem(
ns, [x => 1.0, y => 2.0, z => 3.0], [σ => 1.0, ρ => 2.0, β => 3.0])
getter = getu(ns, (x + 1, x + 2))
@test getter(prob) isa Tuple
@test_nowarn @inferred getter(prob)
getter = getp(ns, (σ + 1, σ + 2))
@test getter(prob) isa Tuple
@test_nowarn @inferred getter(prob)
end
@testset "PDESystem" begin
@parameters x
@variables u(..)
Dxx = Differential(x)^2
Dtt = Differential(t)^2
Dt = D
#2D PDE
C = 1
eq = Dtt(u(t, x)) ~ C^2 * Dxx(u(t, x))
# Initial and boundary conditions
bcs = [u(t, 0) ~ 0.0,# for all t > 0
u(t, 1) ~ 0.0,# for all t > 0
u(0, x) ~ x * (1.0 - x), #for all 0 < x < 1
Dt(u(0, x)) ~ 0.0] #for all 0 < x < 1]
# Space and time domains
domains = [t ∈ (0.0, 1.0),
x ∈ (0.0, 1.0)]
@named pde_system = PDESystem(eq, bcs, domains, [t, x], [u])
@test pde_system.ps == SciMLBase.NullParameters()
@test parameter_symbols(pde_system) == []
@parameters x
@constants h = 1
@variables u(..)
Dt = D
Dxx = Differential(x)^2
eq = Dt(u(t, x)) ~ h * Dxx(u(t, x))
bcs = [u(0, x) ~ -h * x * (x - 1) * sin(x),
u(t, 0) ~ 0, u(t, 1) ~ 0]
domains = [t ∈ (0.0, 1.0),
x ∈ (0.0, 1.0)]
analytic = [u(t, x) ~ -h * x * (x - 1) * sin(x) * exp(-2 * h * t)]
analytic_function = (ps, t, x) -> -ps[1] * x * (x - 1) * sin(x) * exp(-2 * ps[1] * t)
@named pdesys = PDESystem(eq, bcs, domains, [t, x], [u], [h], analytic = analytic)
@test isequal(pdesys.ps, [h])
@test isequal(parameter_symbols(pdesys), [h])
@test isequal(parameters(pdesys), [h])
end
# Issue#2767
using ModelingToolkit
using ModelingToolkit: t_nounits as t, D_nounits as D
using SymbolicIndexingInterface
@parameters p1[1:2]=[1.0, 2.0] p2[1:2]=[0.0, 0.0]
@variables x(t) = 0
@named sys = ODESystem(
[D(x) ~ sum(p1) * t + sum(p2)],
t;
)
prob = ODEProblem(complete(sys))
get_dep = @test_nowarn getu(prob, 2p1)
@test get_dep(prob) == [2.0, 4.0]
@testset "Observed functions with variables as `Symbol`s" begin
@variables x(t) y(t) z(t)[1:2]
@parameters p1 p2[1:2, 1:2]
@mtkbuild sys = ODESystem([D(x) ~ x * t + p1, y ~ 2x, D(z) ~ p2 * z], t)
prob = ODEProblem(
sys, [x => 1.0, z => ones(2)], (0.0, 1.0), [p1 => 2.0, p2 => ones(2, 2)])
@test getu(prob, x)(prob) == getu(prob, :x)(prob)
@test getu(prob, [x, y])(prob) == getu(prob, [:x, :y])(prob)
@test getu(prob, z)(prob) == getu(prob, :z)(prob)
@test getu(prob, p1)(prob) == getu(prob, :p1)(prob)
@test getu(prob, p2)(prob) == getu(prob, :p2)(prob)
end
@testset "Parameter dependencies as symbols" begin
@variables x(t) = 1.0
@parameters a=1 b
@named model = ODESystem(D(x) ~ x + a - b, t, parameter_dependencies = [b ~ a + 1])
sys = complete(model)
prob = ODEProblem(sys, [], (0.0, 1.0))
@test prob.ps[b] == prob.ps[:b]
end
@testset "`get_all_timeseries_indexes` with non-split systems" begin
@variables x(t) y(t) z(t)
@parameters a
@named sys = ODESystem([D(x) ~ a * x, y ~ 2x, z ~ 0.0], t)
sys = structural_simplify(sys, split = false)
for sym in [x, y, z, x + y, x + a, y / x]
@test only(get_all_timeseries_indexes(sys, sym)) == ContinuousTimeseries()
end
@test isempty(get_all_timeseries_indexes(sys, a))
end
@testset "`timeseries_parameter_index` on unwrapped scalarized timeseries parameter" begin
@variables x(t)[1:2]
@parameters p(t)[1:2, 1:2]
ev = [x[1] ~ 2.0] => [p ~ -ones(2, 2)]
@mtkbuild sys = ODESystem(D(x) ~ p * x, t; continuous_events = [ev])
p = ModelingToolkit.unwrap(p)
@test timeseries_parameter_index(sys, p) === ParameterTimeseriesIndex(1, (1, 1))
@test timeseries_parameter_index(sys, p[1, 1]) ===
ParameterTimeseriesIndex(1, (1, 1, 1, 1))
end