# Interpolations

Currently, BasicBSpline.jl doesn't have APIs for interpolations, but it is not hard to implement some basic interpolation algorithms with this package.

## Setup

```
using BasicBSpline
using IntervalSets
using Random; Random.seed!(42)
using Plots
```

## Interpolation with cubic B-spline

```
function interpolate(xs::AbstractVector, fs::AbstractVector{T}) where T
# Cubic open B-spline space
p = 3
k = KnotVector(xs) + KnotVector([xs[1],xs[end]]) * p
P = BSplineSpace{p}(k)
# dimensions
m = length(xs)
n = dim(P)
# The interpolant function has a f''=0 property at bounds.
ddP = BSplineDerivativeSpace{2}(P)
dda = [bsplinebasis(ddP,j,xs[1]) for j in 1:n]
ddb = [bsplinebasis(ddP,j,xs[m]) for j in 1:n]
# Compute the interpolant function (1-dim B-spline manifold)
M = [bsplinebasis(P,j,xs[i]) for i in 1:m, j in 1:n]
M = vcat(dda', M, ddb')
y = vcat(zero(T), fs, zero(T))
return BSplineManifold(M\y, P)
end
# Example inputs
xs = [1, 2, 3, 4, 6, 7]
fs = [1.3, 1.5, 2, 2.1, 1.9, 1.3]
f = interpolate(xs,fs)
# Plot
plotly()
scatter(xs, fs)
plot!(t->f(t))
```

## Interpolation with linear B-spline

```
function interpolate_linear(xs::AbstractVector, fs::AbstractVector{T}) where T
# Linear open B-spline space
p = 1
k = KnotVector(xs) + KnotVector([xs[1],xs[end]])
P = BSplineSpace{p}(k)
# dimensions
m = length(xs)
n = dim(P)
# Compute the interpolant function (1-dim B-spline manifold)
return BSplineManifold(fs, P)
end
# Example inputs
xs = [1, 2, 3, 4, 6, 7]
fs = [1.3, 1.5, 2, 2.1, 1.9, 1.3]
f = interpolate_linear(xs,fs)
# Plot
scatter(xs, fs)
plot!(t->f(t))
```

## Interpolation with periodic B-spline

```
function interpolate_periodic(xs::AbstractVector, fs::AbstractVector, ::Val{p}) where p
# Closed B-spline space, any polynomial degrees can be accepted
n = length(xs) - 1
period = xs[end]-xs[begin]
k = KnotVector(vcat(
xs[end-p:end-1] .- period,
xs,
xs[begin+1:begin+p] .+ period
))
P = BSplineSpace{p}(k)
A = [bsplinebasis(P,j,xs[i]) for i in 1:n, j in 1:n]
for i in 1:p-1, j in 1:i
A[n+i-p+1,j] += bsplinebasis(P,j+n,xs[i+n-p+1])
end
b = A \ fs[begin:end-1]
# Compute the interpolant function (1-dim B-spline manifold)
return BSplineManifold(vcat(b,b[1:p]), P)
end
# Example inputs
xs = [1, 2, 3, 4, 6, 7]
fs = [1.3, 1.5, 2, 2.1, 1.9, 1.3] # fs[1] == fs[end]
f = interpolate_periodic(xs,fs,Val(2))
# Plot
scatter(xs, fs)
plot!(t->f(mod(t-1,6)+1),1,14)
plot!(t->f(t))
```

Note that the periodic interpolation supports any degree of polynomial.

```
xs = 2π*rand(10)
sort!(push!(xs, 0, 2π))
fs = sin.(xs)
f1 = interpolate_periodic(xs,fs,Val(1))
f2 = interpolate_periodic(xs,fs,Val(2))
f3 = interpolate_periodic(xs,fs,Val(3))
f4 = interpolate_periodic(xs,fs,Val(4))
f5 = interpolate_periodic(xs,fs,Val(5))
scatter(xs, fs, label="sampling points")
plot!(sin, label="sine curve", color=:black)
plot!(t->f1(t), label="polynomial degree 1")
plot!(t->f2(t), label="polynomial degree 2")
plot!(t->f3(t), label="polynomial degree 3")
plot!(t->f4(t), label="polynomial degree 4")
plot!(t->f5(t), label="polynomial degree 5")
```