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https://github.com/JuliaApproximation/ContinuumArrays.jl
A package for representing quasi arrays with continuous indices
https://github.com/JuliaApproximation/ContinuumArrays.jl
Last synced: 27 days ago
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A package for representing quasi arrays with continuous indices
- Host: GitHub
- URL: https://github.com/JuliaApproximation/ContinuumArrays.jl
- Owner: JuliaApproximation
- License: mit
- Created: 2018-10-15T12:53:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-22T08:10:08.000Z (about 1 month ago)
- Last Synced: 2024-05-22T12:03:16.248Z (about 1 month ago)
- Language: Julia
- Homepage:
- Size: 593 KB
- Stars: 26
- Watchers: 9
- Forks: 5
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-stars - JuliaApproximation/ContinuumArrays.jl - A package for representing quasi arrays with continuous indices (Julia)
README
# ContinuumArrays.jl
A package for representing quasi arrays with continuous dimensions
[](https://github.com/JuliaApproximation/ContinuumArrays.jl/actions)
[](https://codecov.io/gh/JuliaApproximation/ContinuumArrays.jl)
[](https://JuliaApproximation.github.io/ContinuumArrays.jl/dev)
A quasi array as implemented in [QuasiArrays.jl](https://github.com/JuliaApproximation/QuasiArrays.jl) is a
generalization of an array that allows non-integer indexing via general axes. This package adds support for
infinite-dimensional axes, including continuous intervals. Thus it plays the same role as [InfiniteArrays.jl](https://github.com/JuliaArrays/InfiniteArrays.jl) does for standard arrays but now for quasi arrays.
A simple example is the identity function on the interval `0..1`. This can be created using `Inclusion(d)`,
which returns `x` if `x in d` is true, otherwise throws an error:
```julia
julia> using ContinuumArrays, IntervalSets
julia> x = Inclusion(0..1.0)
Inclusion(0.0..1.0)
julia> size(x) # uncountable (aleph-1)
(ℵ₁,)
julia> axes(x) # axis is itself
(Inclusion(0.0..1.0),)
julia> x[0.1] # returns the input
0.1
julia> x[1.1] # throws an error
ERROR: BoundsError: attempt to access Inclusion(0.0..1.0)
at index [1.1]
Stacktrace:
[1] throw_boundserror(::Inclusion{Float64,Interval{:closed,:closed,Float64}}, ::Tuple{Float64}) at ./abstractarray.jl:538
[2] checkbounds at /Users/solver/Projects/QuasiArrays.jl/src/abstractquasiarray.jl:287 [inlined]
[3] getindex(::Inclusion{Float64,Interval{:closed,:closed,Float64}}, ::Float64) at /Users/solver/Projects/QuasiArrays.jl/src/indices.jl:158
[4] top-level scope at REPL[14]:1
```
An important usage is representing bases and function approximation, and this package contains
a basic implementation of linear splines and heaviside functions. For example, we can construct splines
with evenly spaced nodes via:
```julia
julia> L = LinearSpline(0:0.2:1);
julia> size(L) # uncountable (alepha-1) by 11
(ℵ₁, 6)
julia> axes(L) # The interval 0.0..1.0 by 1:6.
(Inclusion(0.0..1.0), Base.OneTo(6))
julia> L[[0.15,0.25,0.45],1:6] # can index like an array
3×6 Array{Float64,2}:
0.25 0.75 0.0 0.0 0.0 0.0
0.0 0.75 0.25 0.0 0.0 0.0
0.0 0.0 0.75 0.25 0.0 0.0
```
Functions in this basis are represented by a lazy multiplication by a basis
and a vector of coefficients:
```julia
julia> f = L*[1,2,3,4,5,6]
QuasiArrays.ApplyQuasiArray{Float64,1,typeof(*),Tuple{Spline{1,Float64},Array{Int64,1}}}(*, (Spline{1,Float64}([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), [1, 2, 3, 4, 5, 6]))
julia> axes(f)
(Inclusion(0.0..1.0),)
julia> f[0.1]
1.5
```
Creating a finite element method is possible using standard array terminology.
We always take the Lebesgue inner product associated with an axes, so in this
case the mass matrix is just `L'L`. Combined with a differentiation operator allows
us to form the weak Laplacian.
```julia
julia> B = L[:,2:end-1]; # drop boundary terms to impose zero Dirichlet
julia> D = Derivative(L); # Differentiation operator
julia> Δ = (D*B)'D*B # weak Laplacian
4×4 BandedMatrices.BandedMatrix{Float64,Array{Float64,2},Base.OneTo{Int64}}:
10.0 -5.0 ⋅ ⋅
-5.0 10.0 -5.0 ⋅
⋅ -5.0 10.0 -5.0
⋅ ⋅ -5.0 10.0
julia> B'f # right-hand side
4-element Array{Float64,1}:
0.4
0.6
0.8
1.0
julia> c = Δ \ B'f # coefficients of Poisson
4-element Array{Float64,1}:
0.24
0.4
0.43999999999999995
0.3199999999999999
julia> u = B*c; # expand in basis
julia> u[0.1] # evaluate at 0.1
0.12
```