From 13aa933e9679b1c08399b010e475308538257211 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Wed, 20 Nov 2024 16:37:38 +0000 Subject: [PATCH] build based on 7846a75 --- previews/PR412/.documenter-siteinfo.json | 2 +- previews/PR412/array_types/index.html | 8 ++++---- previews/PR412/index.html | 2 +- previews/PR412/recursive_array_functions/index.html | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/previews/PR412/.documenter-siteinfo.json b/previews/PR412/.documenter-siteinfo.json index ff8fff75..f9180bb1 100644 --- a/previews/PR412/.documenter-siteinfo.json +++ b/previews/PR412/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-20T11:01:20","documenter_version":"1.8.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-20T16:37:30","documenter_version":"1.8.0"}} \ No newline at end of file diff --git a/previews/PR412/array_types/index.html b/previews/PR412/array_types/index.html index d0adb320..c049b73c 100644 --- a/previews/PR412/array_types/index.html +++ b/previews/PR412/array_types/index.html @@ -1,12 +1,12 @@ Recursive Array Types · RecursiveArrayTools.jl
GitHub

Recursive Array Types

The Recursive Array types are types which implement an AbstractArray interface so that recursive arrays can be handled with standard array functionality. For example, wrapped arrays will automatically do things like recurse broadcast, define optimized mapping and iteration functions, and more.

Abstract Types

Concrete Types

RecursiveArrayTools.VectorOfArray — Type
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 it to different forms. The indexing structure is:

A.u[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.

There is also support for VectorOfArray constructed from multi-dimensional arrays

VectorOfArray(u::AbstractArray{AT}) where {T, N, AT <: AbstractArray{T, N}}

where IndexStyle(typeof(u)) isa IndexLinear.

source
RecursiveArrayTools.DiffEqArray — Type
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
+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.

There is also support for VectorOfArray constructed from multi-dimensional arrays

VectorOfArray(u::AbstractArray{AT}) where {T, N, AT <: AbstractArray{T, N}}

where IndexStyle(typeof(u)) isa IndexLinear.

source
RecursiveArrayTools.DiffEqArray — Type
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
source
RecursiveArrayTools.ArrayPartition — Type
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.

source
RecursiveArrayTools.NamedArrayPartition — Type
NamedArrayPartition(; kwargs...)
-NamedArrayPartition(x::NamedTuple)

Similar to an ArrayPartition but the individual arrays can be accessed via the constructor-specified names. However, unlike ArrayPartition, each individual array must have the same element type.

source
+A.tsource
RecursiveArrayTools.ArrayPartition — Type
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.

source
RecursiveArrayTools.NamedArrayPartition — Type
NamedArrayPartition(; kwargs...)
+NamedArrayPartition(x::NamedTuple)

Similar to an ArrayPartition but the individual arrays can be accessed via the constructor-specified names. However, unlike ArrayPartition, each individual array must have the same element type.

source
diff --git a/previews/PR412/index.html b/previews/PR412/index.html index 55c9db12..8d163214 100644 --- a/previews/PR412/index.html +++ b/previews/PR412/index.html @@ -93,4 +93,4 @@ [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 [8e850ede] nghttp2_jll v1.59.0+0 - [3f19e933] p7zip_jll v17.4.0+2

You can also download the manifest file and the project file.

+ [3f19e933] p7zip_jll v17.4.0+2

You can also download the manifest file and the project file.

diff --git a/previews/PR412/recursive_array_functions/index.html b/previews/PR412/recursive_array_functions/index.html index 42b9f38d..a69ab356 100644 --- a/previews/PR412/recursive_array_functions/index.html +++ b/previews/PR412/recursive_array_functions/index.html @@ -1,2 +1,2 @@ -Recursive Array Functions · RecursiveArrayTools.jl
GitHub

Recursive Array Functions

These are functions designed for recursive arrays, like arrays of arrays, and do not require that the RecursiveArrayTools types are used.

Function List

RecursiveArrayTools.recursivecopy — Function
recursivecopy(a::Union{AbstractArray{T, N}, AbstractVectorOfArray{T, N}})

A recursive copy function. Acts like a deepcopy on arrays of arrays, but like copy on arrays of scalars.

source
RecursiveArrayTools.recursivecopy! — Function
recursivecopy!(b::AbstractArray{T, N}, a::AbstractArray{T, N})

A recursive copy! function. Acts like a deepcopy! on arrays of arrays, but like copy! on arrays of scalars.

source
RecursiveArrayTools.copyat_or_push! — Function
copyat_or_push!{T}(a::AbstractVector{T}, i::Int, x)

If i<length(x), it's simply a recursivecopy! to the ith element. Otherwise, it will push! a deepcopy.

source
+Recursive Array Functions · RecursiveArrayTools.jl
GitHub

Recursive Array Functions

These are functions designed for recursive arrays, like arrays of arrays, and do not require that the RecursiveArrayTools types are used.

Function List

RecursiveArrayTools.recursivecopy — Function
recursivecopy(a::Union{AbstractArray{T, N}, AbstractVectorOfArray{T, N}})

A recursive copy function. Acts like a deepcopy on arrays of arrays, but like copy on arrays of scalars.

source
RecursiveArrayTools.recursivecopy! — Function
recursivecopy!(b::AbstractArray{T, N}, a::AbstractArray{T, N})

A recursive copy! function. Acts like a deepcopy! on arrays of arrays, but like copy! on arrays of scalars.

source
RecursiveArrayTools.copyat_or_push! — Function
copyat_or_push!{T}(a::AbstractVector{T}, i::Int, x)

If i<length(x), it's simply a recursivecopy! to the ith element. Otherwise, it will push! a deepcopy.

source