RecursiveArrayTools.jl is a set of tools for dealing with recursive arrays like arrays of arrays. The current functionality includes:

```
VectorOfArray(u::AbstractVector)
```

A `VectorOfArray`

is an array which has the underlying data structure `Vector{AbstractArray{T}}`

(but, hopefully, concretely typed!). This wrapper over such data structures allows one to lazily
act like it's a higher-dimensional vector, and easily convert to different forms. The indexing
structure is:

```
A[i] # Returns the ith array in the vector of arrays
A[j,i] # Returns the jth component in the ith array
A[j1,...,jN,i] # Returns the (j1,...,jN) component of the ith array
```

which presents itself as a column-major matrix with the columns being the arrays from the vector.
The `AbstractArray`

interface is implemented, giving access to `copy`

, `push`

, `append!`

, etc. functions,
which act appropriately. Points to note are:

- The length is the number of vectors, or
`length(A.u)`

where`u`

is the vector of arrays. - Iteration follows the linear index and goes over the vectors

Additionally, the `convert(Array,VA::AbstractVectorOfArray)`

function is provided, which transforms
the `VectorOfArray`

into a matrix/tensor. Also, `vecarr_to_vectors(VA::AbstractVectorOfArray)`

returns a vector of the series for each component, that is, `A[i,:]`

for each `i`

.
A plot recipe is provided, which plots the `A[i,:]`

series.

Related to the `VectorOfArray`

is the `DiffEqArray`

```
DiffEqArray(u::AbstractVector,t::AbstractVector)
```

This is a `VectorOfArray`

, which stores `A.t`

that matches `A.u`

. This will plot
`(A.t[i],A[i,:])`

. The function `tuples(diffeq_arr)`

returns tuples of `(t,u)`

.

To construct a DiffEqArray

```
t = 0.0:0.1:10.0
f(t) = t - 1
f2(t) = t^2
vals = [[f(tval) f2(tval)] for tval in t]
A = DiffEqArray(vals, t)
A[1,:] # all time periods for f(t)
A.t
```

```
ArrayPartition(x::AbstractArray...)
```

An `ArrayPartition`

`A`

is an array, which is made up of different arrays `A.x`

.
These index like a single array, but each subarray may have a different type.
However, broadcast is overloaded to loop in an efficient manner, meaning that
`A .+= 2.+B`

is type-stable in its computations, even if `A.x[i]`

and `A.x[j]`

do not match types. A full array interface is included for completeness, which
allows this array type to be used in place of a standard array where
such a type stable broadcast may be needed. One example is in heterogeneous
differential equations for DifferentialEquations.jl.

An `ArrayPartition`

acts like a single array. `A[i]`

indexes through the first
array, then the second, etc., all linearly. But `A.x`

is where the arrays are stored.
Thus, for:

```
using RecursiveArrayTools
A = ArrayPartition(y,z)
```

we would have `A.x[1]==y`

and `A.x[2]==z`

. Broadcasting like `f.(A)`

is efficient.

```
recursivecopy!(b::Array{T,N},a::Array{T,N})
```

A recursive `copy!`

function. Acts like a `deepcopy!`

on arrays of arrays, but
like `copy!`

on arrays of scalars.

```
convert(Array,vecvec)
```

Technically, just a Base fallback that works well. Takes in a vector of arrays,
returns an array of dimension one greater than the original elements.
Works on `AbstractVectorOfArray`

. If the `vecvec`

is ragged, i.e., not all of the
elements are the same, then it uses the size of the first element to determine
the conversion.

```
vecvecapply(f::Base.Callable,v)
```

Calls `f`

on each element of a vecvec `v`

.

```
copyat_or_push!{T}(a::AbstractVector{T},i::Int,x)
```

If `i<length(x)`

, it's simply a `recursivecopy!`

to the `i`

th element. Otherwise, it will
`push!`

a `deepcopy`

.

```
recursive_one(a)
```

Calls `one`

on the bottom container to get the "true element one type".

```
mean{T<:AbstractArray}(vecvec::Vector{T})
mean{T<:AbstractArray}(matarr::Matrix{T},region=0)
```

Generalized mean functions for vectors of arrays and a matrix of arrays.

10/30/2016

9 days ago

360 commits