Restructure testsets

Author: serenity4

test/incidence.jl

@@ -148,150 +148,152 @@ dependencies(row) = sort(rowvals(row) .=> nonzeros(row), by = first)
     @test_throws "absence of state dependence for time" Incidence(Const(0.0), IncidenceValue[linear, linear_time_dependent])
   end
 
-  incidence = @infer_incidence t
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [1 => 1]
-  @test incidence == incidence"t"
-
-  incidence = @infer_incidence 5. +ᵢ u₁
-  @test incidence.typ === Const(5.0)
-  @test dependencies(incidence.row) == [2 => 1]
-  @test incidence == incidence"5.0 + u₁"
-
-  incidence = @infer_incidence 5.0 +ᵢ t +ᵢ u₁
-  @test incidence.typ === Const(5.0)
-  @test dependencies(incidence.row) == [1 => 1, 2 => 1]
-  @test incidence == incidence"5.0 + t + u₁"
-
-  incidence = @infer_incidence t *ᵢ u₁
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => linear_time_dependent]
-  @test incidence == incidence"f(∝ₛt, ∝ₜu₁)"
-
-  incidence = @infer_incidence u₁
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [2 => 1]
-  @test incidence == incidence"u₁"
-
-  incidence = @infer_incidence u₁ +ᵢ u₂
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [2 => 1, 3 => 1]
-  @test incidence == incidence"u₁ + u₂"
-
-  incidence = @infer_incidence u₁ *ᵢ u₂
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
-  @test incidence == incidence"f(∝ₛu₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₂)
-  @test incidence.typ === Const(6.0)
-  @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
-  @test incidence == incidence"6.0 + f(∝ₛu₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₁ *ᵢ u₂)
-  @test incidence.typ === Const(6.0)
-  @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent]
-  @test incidence == incidence"6.0 + f(u₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₁ *ᵢ u₂) +ᵢ u₃
-  @test incidence.typ === Const(6.0)
-  @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent, 4 => 1.0]
-  @test incidence == incidence"6.0 + u₃ + f(u₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence (u₁ +ᵢ u₂) *ᵢ t
-  @test incidence.typ === Const(0.0)
-  @test dependencies(incidence.row) == [1 => linear_state_dependent, (2:3 .=> linear_time_dependent)...]
-  @test incidence == incidence"f(∝ₛt, ∝ₜu₁, ∝ₜu₂)"
-
-  incidence = @infer_incidence u₁ *ᵢ u₂ +ᵢ (u₁ +ᵢ t) *ᵢ u₃
-  @test dependencies(incidence.row) == [(1:3 .=> linear_state_dependent)..., 4 => linear_time_and_state_dependent]
-  @test incidence == incidence"f(∝ₛt, ∝ₛu₁, ∝ₛu₂, ∝ₜₛu₃)"
-
-  # IPO
-
-  # NOTE: Most of additive terms (`.typ`) can't be precise given the current IPO representation.
-  # We expect `Const(0.0)` in most cases, but widen to `Float64`.
-
-  incidence = @infer_incidence 1.0t * 3.0
-  @test dependencies(incidence.row) == [1 => linear]
-  @test incidence == incidence"a + ∝t"
-
-  incidence = @infer_incidence (1.0 + u₁ +ᵢ u₂) * 1.0
-  @test incidence.typ === Float64
-  @test dependencies(incidence.row) == (2:3 .=> linear_state_dependent)
-  @test incidence == incidence"a + f(∝ₛu₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence (2.0 + u₁) * (3.0 + u₂)
-  @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
-  @test incidence == incidence"a + f(∝ₛu₁, ∝ₛu₂)"
-
-  incidence = @infer_incidence 5.0 + u₁
-  @test incidence.typ === Const(5.0)
-  @test dependencies(incidence.row) == [2 => 1]
-  @test incidence == incidence"5.0 + u₁"
-
-  incidence = @infer_incidence u₁ * u₁
-  @test dependencies(incidence.row) == [2 => nonlinear]
-  @test incidence == incidence"f(u₁)"
-
-  incidence = @infer_incidence t * t
-  @test dependencies(incidence.row) == [1 => nonlinear]
-  @test incidence == incidence"f(t)"
-
-  mul3(a, b, c) = a *ᵢ (b *ᵢ c)
-  incidence = @infer_incidence mul3(t, u₁, u₂)
-  @test dependencies(incidence.row) == [1 => linear_state_dependent, (2:3 .=> linear_time_and_state_dependent)...]
-  @test incidence == incidence"f(∝ₛt, ∝ₜₛu₁, ∝ₜₛu₂)"
-
-  incidence = @infer_incidence mul3(t, u₁, u₁)
-  @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => nonlinear]
-  @test incidence == incidence"f(∝ₛt, u₁)"
-
-  incidence = @infer_incidence mul3(t, u₁, t)
-  # If we knew which state is used for state dependence,
-  # state should be inferred as linear_time_dependent.
-  @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_and_state_dependent]
-  @test incidence == incidence"f(t, ∝ₜₛu₁)"
-
-  incidence = @infer_incidence mul3(u₂, u₁, u₂)
-  @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => nonlinear]
-  @test incidence == incidence"f(∝ₛu₁, u₂)"
-
-  _muladd(a, b, c) = a +ᵢ b *ᵢ c
-  incidence = @infer_incidence _muladd(u₁, u₁, u₂)
-  # We widen to `nonlinear` because we can't yet infer that `b := u₁` is
-  # not multiplied by `a := u₁`. The solution would be to see that `a`
-  # is linear but state-independent and therefore can't be a factor of `b`.
-  @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent]
-  @test incidence == incidence"f(u₁, ∝ₛu₂)"
-
-  # Here we still wouldn't be able to use the above solution because `a := u₁` is state-dependent.
-  # So `c := u₁` having a state-dependent coefficient might be multiplied by `a` a.k.a itself
-  # which would make it nonlinear, so IPO can only infer `u₁` as nonlinear.
-  _muladd2(a, b, c, d) = d *ᵢ a +ᵢ b *ᵢ c
-  incidence = @infer_incidence _muladd2(u₁, u₂, u₁, u₃)
-  @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent, 4 => linear_state_dependent]
-  @test incidence == incidence"f(u₁, ∝ₛu₂, ∝ₛu₃)"
-
-  incidence = @infer_incidence exp(u₁)
-  @test dependencies(incidence.row) == [2 => nonlinear]
-  @test incidence == incidence"a + f(u₁)"
-
-  incidence = @infer_incidence t * exp(u₁)
-  @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => nonlinear]
-  @test incidence == incidence"a + f(∝ₛt, u₁)"
-
-  incidence = @infer_incidence u₁ * exp(t)
-  @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_dependent]
-  @test incidence == incidence"a + f(t, ∝ₜu₁)"
-
-  incidence = @infer_incidence u₁ * exp(t + u₂)
-  @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_and_state_dependent, 3 => nonlinear]
-  @test incidence == incidence"a + f(t, ∝ₜₛu₁, u₂)"
-
-  incidence = @infer_incidence atan(u₁, u₂)
-  @test dependencies(incidence.row) == [2 => nonlinear, 3 => nonlinear]
-  @test incidence == incidence"a + f(u₁, u₂)"
+  @testset "Basic lattice operations with intrinsic tfuncs" begin
+    incidence = @infer_incidence t
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [1 => 1]
+    @test incidence == incidence"t"
+
+    incidence = @infer_incidence 5. +ᵢ u₁
+    @test incidence.typ === Const(5.0)
+    @test dependencies(incidence.row) == [2 => 1]
+    @test incidence == incidence"5.0 + u₁"
+
+    incidence = @infer_incidence 5.0 +ᵢ t +ᵢ u₁
+    @test incidence.typ === Const(5.0)
+    @test dependencies(incidence.row) == [1 => 1, 2 => 1]
+    @test incidence == incidence"5.0 + t + u₁"
+
+    incidence = @infer_incidence t *ᵢ u₁
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => linear_time_dependent]
+    @test incidence == incidence"f(∝ₛt, ∝ₜu₁)"
+
+    incidence = @infer_incidence u₁
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [2 => 1]
+    @test incidence == incidence"u₁"
+
+    incidence = @infer_incidence u₁ +ᵢ u₂
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [2 => 1, 3 => 1]
+    @test incidence == incidence"u₁ + u₂"
+
+    incidence = @infer_incidence u₁ *ᵢ u₂
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
+    @test incidence == incidence"f(∝ₛu₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₂)
+    @test incidence.typ === Const(6.0)
+    @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
+    @test incidence == incidence"6.0 + f(∝ₛu₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₁ *ᵢ u₂)
+    @test incidence.typ === Const(6.0)
+    @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent]
+    @test incidence == incidence"6.0 + f(u₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence (2.0 +ᵢ u₁) *ᵢ (3.0 +ᵢ u₁ *ᵢ u₂) +ᵢ u₃
+    @test incidence.typ === Const(6.0)
+    @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent, 4 => 1.0]
+    @test incidence == incidence"6.0 + u₃ + f(u₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence (u₁ +ᵢ u₂) *ᵢ t
+    @test incidence.typ === Const(0.0)
+    @test dependencies(incidence.row) == [1 => linear_state_dependent, (2:3 .=> linear_time_dependent)...]
+    @test incidence == incidence"f(∝ₛt, ∝ₜu₁, ∝ₜu₂)"
+
+    incidence = @infer_incidence u₁ *ᵢ u₂ +ᵢ (u₁ +ᵢ t) *ᵢ u₃
+    @test dependencies(incidence.row) == [(1:3 .=> linear_state_dependent)..., 4 => linear_time_and_state_dependent]
+    @test incidence == incidence"f(∝ₛt, ∝ₛu₁, ∝ₛu₂, ∝ₜₛu₃)"
+  end
+
+  @testset "IPO" begin
+    # NOTE: Most of additive terms (`.typ`) can't be precise given the current IPO representation.
+    # We expect `Const(0.0)` in most cases, but widen to `Float64`.
+
+    incidence = @infer_incidence 1.0t * 3.0
+    @test dependencies(incidence.row) == [1 => linear]
+    @test incidence == incidence"a + ∝t"
+
+    incidence = @infer_incidence (1.0 + u₁ +ᵢ u₂) * 1.0
+    @test incidence.typ === Float64
+    @test dependencies(incidence.row) == (2:3 .=> linear_state_dependent)
+    @test incidence == incidence"a + f(∝ₛu₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence (2.0 + u₁) * (3.0 + u₂)
+    @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => linear_state_dependent]
+    @test incidence == incidence"a + f(∝ₛu₁, ∝ₛu₂)"
+
+    incidence = @infer_incidence 5.0 + u₁
+    @test incidence.typ === Const(5.0)
+    @test dependencies(incidence.row) == [2 => 1]
+    @test incidence == incidence"5.0 + u₁"
+
+    incidence = @infer_incidence u₁ * u₁
+    @test dependencies(incidence.row) == [2 => nonlinear]
+    @test incidence == incidence"f(u₁)"
+
+    incidence = @infer_incidence t * t
+    @test dependencies(incidence.row) == [1 => nonlinear]
+    @test incidence == incidence"f(t)"
+
+    mul3(a, b, c) = a *ᵢ (b *ᵢ c)
+    incidence = @infer_incidence mul3(t, u₁, u₂)
+    @test dependencies(incidence.row) == [1 => linear_state_dependent, (2:3 .=> linear_time_and_state_dependent)...]
+    @test incidence == incidence"f(∝ₛt, ∝ₜₛu₁, ∝ₜₛu₂)"
+
+    incidence = @infer_incidence mul3(t, u₁, u₁)
+    @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => nonlinear]
+    @test incidence == incidence"f(∝ₛt, u₁)"
+
+    incidence = @infer_incidence mul3(t, u₁, t)
+    # If we knew which state is used for state dependence,
+    # state should be inferred as linear_time_dependent.
+    @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_and_state_dependent]
+    @test incidence == incidence"f(t, ∝ₜₛu₁)"
+
+    incidence = @infer_incidence mul3(u₂, u₁, u₂)
+    @test dependencies(incidence.row) == [2 => linear_state_dependent, 3 => nonlinear]
+    @test incidence == incidence"f(∝ₛu₁, u₂)"
+
+    _muladd(a, b, c) = a +ᵢ b *ᵢ c
+    incidence = @infer_incidence _muladd(u₁, u₁, u₂)
+    # We widen to `nonlinear` because we can't yet infer that `b := u₁` is
+    # not multiplied by `a := u₁`. The solution would be to see that `a`
+    # is linear but state-independent and therefore can't be a factor of `b`.
+    @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent]
+    @test incidence == incidence"f(u₁, ∝ₛu₂)"
+
+    # Here we still wouldn't be able to use the above solution because `a := u₁` is state-dependent.
+    # So `c := u₁` having a state-dependent coefficient might be multiplied by `a` a.k.a itself
+    # which would make it nonlinear, so IPO can only infer `u₁` as nonlinear.
+    _muladd2(a, b, c, d) = d *ᵢ a +ᵢ b *ᵢ c
+    incidence = @infer_incidence _muladd2(u₁, u₂, u₁, u₃)
+    @test dependencies(incidence.row) == [2 => nonlinear, 3 => linear_state_dependent, 4 => linear_state_dependent]
+    @test incidence == incidence"f(u₁, ∝ₛu₂, ∝ₛu₃)"
+
+    incidence = @infer_incidence exp(u₁)
+    @test dependencies(incidence.row) == [2 => nonlinear]
+    @test incidence == incidence"a + f(u₁)"
+
+    incidence = @infer_incidence t * exp(u₁)
+    @test dependencies(incidence.row) == [1 => linear_state_dependent, 2 => nonlinear]
+    @test incidence == incidence"a + f(∝ₛt, u₁)"
+
+    incidence = @infer_incidence u₁ * exp(t)
+    @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_dependent]
+    @test incidence == incidence"a + f(t, ∝ₜu₁)"
+
+    incidence = @infer_incidence u₁ * exp(t + u₂)
+    @test dependencies(incidence.row) == [1 => nonlinear, 2 => linear_time_and_state_dependent, 3 => nonlinear]
+    @test incidence == incidence"a + f(t, ∝ₜₛu₁, u₂)"
+
+    incidence = @infer_incidence atan(u₁, u₂)
+    @test dependencies(incidence.row) == [2 => nonlinear, 3 => nonlinear]
+    @test incidence == incidence"a + f(u₁, u₂)"
+  end
 end;
 
 end