Update QuadraticSpline

src/derivatives.jl

@@ -126,9 +126,11 @@ end
 
 # QuadraticSpline Interpolation
 function _derivative(A::QuadraticSpline{<:AbstractVector}, t::Number, iguess)
-    idx = get_idx(A, t, iguess; lb = 2, ub_shift = 0, side = :first)
-    σ = get_parameters(A, idx - 1)
-    A.z[idx - 1] + 2σ * (t - A.t[idx - 1])
+    idx = get_idx(A, t, iguess)
+    α, β = get_parameters(A, idx)
+    Δt = t - A.t[idx]
+    Δt_full = A.t[idx + 1] - A.t[idx]
+    2α * Δt / Δt_full^2 + β / Δt_full
 end
 
 # CubicSpline Interpolation

src/integrals.jl

@@ -71,10 +71,11 @@ function _integral(A::QuadraticInterpolation{<:AbstractVector{<:Number}},
 end
 
 function _integral(A::QuadraticSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number)
-    Cᵢ = A.u[idx]
+    α, β = get_parameters(A, idx)
+    uᵢ = A.u[idx]
     Δt = t - A.t[idx]
-    σ = get_parameters(A, idx)
-    return A.z[idx] * Δt^2 / 2 + σ * Δt^3 / 3 + Cᵢ * Δt
+    Δt_full = (A.t[idx + 1] - A.t[idx])
+    Δt * (α * Δt^2 / (3Δt_full^2) + β * Δt / (2Δt_full) + uᵢ)
 end
 
 function _integral(A::CubicSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number)

src/interpolation_caches.jl

@@ -299,30 +299,30 @@ Extrapolation extends the last quadratic polynomial on each side.
     for a test based on the normalized standard deviation of the difference with respect
     to the straight line (see [`looks_linear`](@ref)). Defaults to 1e-2.
 """
-struct QuadraticSpline{uType, tType, IType, pType, tAType, dType, zType, T} <:
+struct QuadraticSpline{uType, tType, IType, pType, kType, cType, NType, T} <:
        AbstractInterpolation{T}
     u::uType
     t::tType
     I::IType
     p::QuadraticSplineParameterCache{pType}
-    tA::tAType
-    d::dType
-    z::zType
+    k::kType # knot vector
+    c::cType # 1D B-spline control points
+    N::NType # Spline coefficients (preallocated memory)
     extrapolate::Bool
     iguesser::Guesser{tType}
     cache_parameters::Bool
     linear_lookup::Bool
     function QuadraticSpline(
-            u, t, I, p, tA, d, z, extrapolate, cache_parameters, assume_linear_t)
+            u, t, I, p, k, c, N, extrapolate, cache_parameters, assume_linear_t)
         linear_lookup = seems_linear(assume_linear_t, t)
-        new{typeof(u), typeof(t), typeof(I), typeof(p.σ), typeof(tA),
-            typeof(d), typeof(z), eltype(u)}(u,
+        new{typeof(u), typeof(t), typeof(I), typeof(p.α), typeof(k),
+            typeof(c), typeof(N), eltype(u)}(u,
             t,
             I,
             p,
-            tA,
-            d,
-            z,
+            k,
+            c,
+            N,
             extrapolate,
             Guesser(t),
             cache_parameters,
@@ -336,50 +336,47 @@ function QuadraticSpline(
         cache_parameters = false, assume_linear_t = 1e-2) where {uType <:
                                                                  AbstractVector{<:Number}}
     u, t = munge_data(u, t)
-    linear_lookup = seems_linear(assume_linear_t, t)
-    s = length(t)
-    dl = ones(eltype(t), s - 1)
-    d_tmp = ones(eltype(t), s)
-    du = zeros(eltype(t), s - 1)
-    tA = Tridiagonal(dl, d_tmp, du)
-
-    # zero for element type of d, which we don't know yet
-    typed_zero = zero(2 // 1 * (u[begin + 1] - u[begin]) / (t[begin + 1] - t[begin]))
 
-    d = map(i -> i == 1 ? typed_zero : 2 // 1 * (u[i] - u[i - 1]) / (t[i] - t[i - 1]), 1:s)
-    z = tA \ d
+    n = length(t)
+    dtype_N = typeof(t[1] / t[1])
+    N = zeros(dtype_N, n)
+    k, A = quadratic_spline_params(t, N)
+    c = A \ u
 
-    p = QuadraticSplineParameterCache(z, t, cache_parameters)
+    p = QuadraticSplineParameterCache(u, t, k, c, N, cache_parameters)
     A = QuadraticSpline(
-        u, t, nothing, p, tA, d, z, extrapolate, cache_parameters, linear_lookup)
+        u, t, nothing, p, k, c, N, extrapolate, cache_parameters, assume_linear_t)
     I = cumulative_integral(A, cache_parameters)
-    QuadraticSpline(u, t, I, p, tA, d, z, extrapolate, cache_parameters, linear_lookup)
+    QuadraticSpline(u, t, I, p, k, c, N, extrapolate, cache_parameters, assume_linear_t)
 end
 
 function QuadraticSpline(
         u::uType, t; extrapolate = false, cache_parameters = false,
         assume_linear_t = 1e-2) where {uType <:
                                        AbstractVector}
     u, t = munge_data(u, t)
-    linear_lookup = seems_linear(assume_linear_t, t)
-    s = length(t)
-    dl = ones(eltype(t), s - 1)
-    d_tmp = ones(eltype(t), s)
-    du = zeros(eltype(t), s - 1)
-    tA = Tridiagonal(dl, d_tmp, du)
-    d_ = map(
-        i -> i == 1 ? zeros(eltype(t), size(u[1])) :
-             2 // 1 * (u[i] - u[i - 1]) / (t[i] - t[i - 1]),
-        1:s)
-    d = transpose(reshape(reduce(hcat, d_), :, s))
-    z_ = reshape(transpose(tA \ d), size(u[1])..., :)
-    z = [z_s for z_s in eachslice(z_, dims = ndims(z_))]
 
-    p = QuadraticSplineParameterCache(z, t, cache_parameters)
+    n = length(t)
+    dtype_N = typeof(t[1] / t[1])
+    N = zeros(dtype_N, n)
+    k, A = quadratic_spline_params(t, N)
+
+    eltype_c_prototype = one(dtype_N) * first(u)
+    c = [similar(eltype_c_prototype) for _ in 1:n]
+
+    # Assuming u contains arrays of equal shape
+    for j in eachindex(eltype_c_prototype)
+        c_dim = A \ [u_[j] for u_ in u]
+        for (i, c_dim_) in enumerate(c_dim)
+            c[i][j] = c_dim_
+        end
+    end
+
+    p = QuadraticSplineParameterCache(u, t, k, c, N, cache_parameters)
     A = QuadraticSpline(
-        u, t, nothing, p, tA, d, z, extrapolate, cache_parameters, linear_lookup)
+        u, t, nothing, p, k, c, N, extrapolate, cache_parameters, assume_linear_t)
     I = cumulative_integral(A, cache_parameters)
-    QuadraticSpline(u, t, I, p, tA, d, z, extrapolate, cache_parameters, linear_lookup)
+    QuadraticSpline(u, t, I, p, k, c, N, extrapolate, cache_parameters, assume_linear_t)
 end
 
 """

src/interpolation_methods.jl

@@ -147,10 +147,10 @@ end
 # QuadraticSpline Interpolation
 function _interpolate(A::QuadraticSpline{<:AbstractVector}, t::Number, iguess)
     idx = get_idx(A, t, iguess)
-    Cᵢ = A.u[idx]
-    Δt = t - A.t[idx]
-    σ = get_parameters(A, idx)
-    return A.z[idx] * Δt + σ * Δt^2 + Cᵢ
+    α, β = get_parameters(A, idx)
+    uᵢ = A.u[idx]
+    Δt_scaled = (t - A.t[idx]) / (A.t[idx + 1] - A.t[idx])
+    Δt_scaled * (α * Δt_scaled + β) + uᵢ
 end
 
 # CubicSpline Interpolation

src/interpolation_utils.jl

@@ -59,6 +59,37 @@ function spline_coefficients!(N, d, k, u::AbstractVector)
     return nothing
 end
 
+function quadratic_spline_params(t::AbstractVector, N::AbstractVector)
+
+    # Create knot vector
+    # Don't use x[end-1] as knot to match number of degrees of freedom with data
+    k = zeros(length(t) + 3)
+    k[1:3] .= t[1]
+    k[(end - 2):end] .= t[end]
+    k[4:(end - 3)] .= t[2:(end - 2)]
+
+    # Create and solve linear system Ac = u, where:
+    # - A consists of basis function evaulations in t
+    # - c are the 1D control points 
+    n = length(t)
+    dtype_N = typeof(t[1] / t[1])
+
+    diag = Vector{dtype_N}(undef, n)
+    diag_hi = Vector{dtype_N}(undef, n - 1)
+    diag_lo = Vector{dtype_N}(undef, n - 1)
+
+    for (i, tᵢ) in enumerate(t)
+        spline_coefficients!(N, 2, k, tᵢ)
+        diag[i] = N[i]
+        (i > 1) && (diag_lo[i - 1] = N[i - 1])
+        (i < n) && (diag_hi[i] = N[i + 1])
+    end
+
+    A = Tridiagonal(diag_lo, diag, diag_hi)
+
+    return k, A
+end
+
 # helper function for data manipulation
 function munge_data(u::AbstractVector{<:Real}, t::AbstractVector{<:Real})
     return u, t
@@ -159,9 +190,9 @@ end
 
 function get_parameters(A::QuadraticSpline, idx)
     if A.cache_parameters
-        A.p.σ[idx]
+        A.p.α[idx], A.p.β[idx]
     else
-        quadratic_spline_parameters(A.z, A.t, idx)
+        quadratic_spline_parameters(A.u, A.t, A.k, A.c, A.N, idx)
     end
 end
 

src/parameter_caches.jl

@@ -73,23 +73,33 @@ function quadratic_interpolation_parameters(u, t, idx)
 end
 
 struct QuadraticSplineParameterCache{pType}
-    σ::pType
+    α::pType
+    β::pType
 end
 
-function QuadraticSplineParameterCache(z, t, cache_parameters)
+function QuadraticSplineParameterCache(u, t, k, c, N, cache_parameters)
     if cache_parameters
-        σ = quadratic_spline_parameters.(Ref(z), Ref(t), 1:(length(t) - 1))
-        QuadraticSplineParameterCache(σ)
+        parameters = quadratic_spline_parameters.(
+            Ref(u), Ref(t), Ref(k), Ref(c), Ref(N), 1:(length(t) - 1))
+        α, β = collect.(eachrow(stack(collect.(parameters))))
+        QuadraticSplineParameterCache(α, β)
     else
         # Compute parameters once to infer types
-        σ = quadratic_spline_parameters(z, t, 1)
-        QuadraticSplineParameterCache(typeof(σ)[])
+        α, β = quadratic_spline_parameters(u, t, k, c, N, 1)
+        QuadraticSplineParameterCache(typeof(α)[], typeof(β)[])
     end
 end
 
-function quadratic_spline_parameters(z, t, idx)
-    σ = 1 // 2 * (z[idx + 1] - z[idx]) / (t[idx + 1] - t[idx])
-    return σ
+function quadratic_spline_parameters(u, t, k, c, N, idx)
+    tᵢ₊ = (t[idx] + t[idx + 1]) / 2
+    nonzero_coefficient_idxs = spline_coefficients!(N, 2, k, tᵢ₊)
+    uᵢ₊ = zero(first(u))
+    for j in nonzero_coefficient_idxs
+        uᵢ₊ += N[j] * c[j]
+    end
+    α = 2 * (u[idx + 1] + u[idx]) - 4uᵢ₊
+    β = 4 * (uᵢ₊ - u[idx]) - (u[idx + 1] - u[idx])
+    return α, β
 end
 
 struct CubicSplineParameterCache{pType}

test/derivative_tests.jl

@@ -259,8 +259,8 @@ end
     derivexpr2 = expand_derivatives(substitute(D2(A(ω)), Dict(ω => 0.5τ)))
     symfunc1 = Symbolics.build_function(derivexpr1, τ; expression = Val{false})
     symfunc2 = Symbolics.build_function(derivexpr2, τ; expression = Val{false})
-    @test symfunc1(0.5) == 0.5 * 3
-    @test symfunc2(0.5) == 0.5 * 6
+    @test symfunc1(0.5) == 1.5
+    @test symfunc2(0.5) == -3.0
 
     u = [0.0, 1.5, 0.0]
     t = [0.0, 0.5, 1.0]

test/interpolation_tests.jl

@@ -510,39 +510,38 @@ end
     A = QuadraticSpline(u, t; extrapolate = true)
 
     # Solution
-    P₁ = x -> (x + 1)^2 # for x ∈ [-1, 0]
-    P₂ = x -> 2 * x + 1   # for x ∈ [ 0, 1]
+    P₁ = x -> 0.5 * (x + 1) * (x + 2)
 
     for (_t, _u) in zip(t, u)
         @test A(_t) == _u
     end
     @test A(-2.0) == P₁(-2.0)
     @test A(-0.5) == P₁(-0.5)
-    @test A(0.7) == P₂(0.7)
-    @test A(2.0) == P₂(2.0)
+    @test A(0.7) == P₁(0.7)
+    @test A(2.0) == P₁(2.0)
     test_cached_index(A)
 
     u_ = [0.0, 1.0, 3.0]' .* ones(4)
     u = [u_[:, i] for i in 1:size(u_, 2)]
     A = QuadraticSpline(u, t; extrapolate = true)
     @test A(-2.0) == P₁(-2.0) * ones(4)
     @test A(-0.5) == P₁(-0.5) * ones(4)
-    @test A(0.7) == P₂(0.7) * ones(4)
-    @test A(2.0) == P₂(2.0) * ones(4)
+    @test A(0.7) == P₁(0.7) * ones(4)
+    @test A(2.0) == P₁(2.0) * ones(4)
 
     u = [repeat(u[i], 1, 3) for i in 1:3]
     A = QuadraticSpline(u, t; extrapolate = true)
     @test A(-2.0) == P₁(-2.0) * ones(4, 3)
     @test A(-0.5) == P₁(-0.5) * ones(4, 3)
-    @test A(0.7) == P₂(0.7) * ones(4, 3)
-    @test A(2.0) == P₂(2.0) * ones(4, 3)
+    @test A(0.7) == P₁(0.7) * ones(4, 3)
+    @test A(2.0) == P₁(2.0) * ones(4, 3)
 
     # Test extrapolation
     u = [0.0, 1.0, 3.0]
     t = [-1.0, 0.0, 1.0]
     A = QuadraticSpline(u, t; extrapolate = true)
-    @test A(-2.0) == 1.0
-    @test A(2.0) == 5.0
+    @test A(-2.0) == 0.0
+    @test A(2.0) == 6.0
     A = QuadraticSpline(u, t)
     @test_throws DataInterpolations.ExtrapolationError A(-2.0)
     @test_throws DataInterpolations.ExtrapolationError A(2.0)

test/parameter_tests.jl

@@ -20,7 +20,8 @@ end
     u = [1.0, 5.0, 3.0, 4.0, 4.0]
     t = collect(1:5)
     A = QuadraticSpline(u, t; cache_parameters = true)
-    @test A.p.σ ≈ [4.0, -10.0, 13.0, -14.0]
+    @test A.p.α ≈ [-9.5, 3.5, -0.5, -0.5]
+    @test A.p.β ≈ [13.5, -5.5, 1.5, 0.5]
 end
 
 @testset "Cubic Spline" begin

test/zygote_tests.jl

@@ -15,7 +15,7 @@ function test_zygote(method, u, t; args = [], args_after = [], kwargs = [], name
             @test adiff ≈ zdiff
         end
     end
-    if method ∉ [LagrangeInterpolation, BSplineInterpolation, BSplineApprox]
+    if method ∉ [LagrangeInterpolation, BSplineInterpolation, BSplineApprox, QuadraticSpline]
         @testset "$name, derivatives w.r.t. u" begin
             function f(u)
                 A = method(args..., u, t, args_after...; kwargs..., extrapolate = true)
@@ -67,12 +67,6 @@ end
         QuinticHermiteSpline, u, t, args = [ddu, du], name = "Quintic Hermite Spline")
 end
 
-@testset "Quadratic Spline" begin
-    u = [1.0, 4.0, 9.0, 16.0]
-    t = [1.0, 2.0, 3.0, 4.0]
-    test_zygote(QuadraticSpline, u, t, name = "Quadratic Spline")
-end
-
 @testset "Lagrange Interpolation" begin
     u = [1.0, 4.0, 9.0]
     t = [1.0, 2.0, 3.0]