Merge 1b3c643 into f4c7dcf

Author: mtfishman

Project.toml

@@ -1,39 +1,32 @@
 name = "SparseArraysBase"
 uuid = "0d5efcca-f356-4864-8770-e1ed8d78f208"
 authors = ["ITensor developers <support@itensor.org> and contributors"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
-Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
-Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 ArrayLayouts = "4c555306-a7a7-4459-81d9-ec55ddd5c99a"
 BroadcastMapConversion = "4a4adec5-520f-4750-bb37-d5e66b4ddeb2"
-Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
+Derive = "a07dfc7f-7d04-4eb5-84cc-a97f051f655a"
 Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
-NestedPermutedDimsArrays = "2c2a8ec4-3cfc-4276-aa3e-1307b4294e58"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-TypeParameterAccessors = "7e5a90cf-f82e-492e-a09b-e3e26432c138"
-VectorInterface = "409d34a3-91d5-4945-b6ec-7529ddf182d8"
 
 [compat]
-Accessors = "0.1.38"
-Adapt = "4.1.1"
 Aqua = "0.8.9"
-ArrayLayouts = "1.10.4"
-Compat = "4.16.0"
+ArrayLayouts = "1.11.0"
+BroadcastMapConversion = "0.1.0"
+Derive = "0.3.0"
 Dictionaries = "0.4.3"
 LinearAlgebra = "1.10"
-MacroTools = "0.5.13"
-SparseArrays = "1.10"
+SafeTestsets = "0.1"
+Suppressor = "0.2"
 Test = "1.10"
-VectorInterface = "0.5.0"
 julia = "1.10"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "Test"]
+test = ["Aqua", "Test", "Suppressor", "SafeTestsets"]

README.md

@@ -32,10 +32,127 @@ julia> Pkg.add("SparseArraysBase")
 ## Examples
 
 ````julia
-using SparseArraysBase: SparseArraysBase
+using SparseArraysBase:
+  SparseArrayDOK,
+  SparseMatrixDOK,
+  SparseVectorDOK,
+  eachstoredindex,
+  getstoredindex,
+  getunstoredindex,
+  isstored,
+  setstoredindex!,
+  setunstoredindex!,
+  storedlength,
+  storedpairs,
+  storedvalues,
+  zero!
+using Test: @test, @test_throws
+
+a = SparseArrayDOK{Float64}(2, 2)
+````
+
+AbstractArray interface:
+
+````julia
+@test iszero(a)
+@test iszero(sum(a))
+@test iszero(storedlength(a))
+
+a[1, 2] = 12
+@test a == [0 12; 0 0]
+@test a[1, 1] == 0
+@test a[2, 1] == 0
+@test a[1, 2] == 12
+@test a[2, 2] == 0
+````
+
+SparseArraysBase interface:
+
+````julia
+using Dictionaries: IndexError
+@test issetequal(eachstoredindex(a), [CartesianIndex(1, 2)])
+@test getstoredindex(a, 1, 2) == 12
+@test_throws IndexError getstoredindex(a, 1, 1)
+@test getunstoredindex(a, 1, 1) == 0
+@test getunstoredindex(a, 1, 2) == 0
+@test !isstored(a, 1, 1)
+@test isstored(a, 1, 2)
+@test setstoredindex!(copy(a), 21, 1, 2) == [0 21; 0 0]
+@test_throws IndexError setstoredindex!(copy(a), 21, 2, 1)
+@test setunstoredindex!(copy(a), 21, 1, 2) == [0 21; 0 0]
+@test storedlength(a) == 1
+@test issetequal(storedpairs(a), [CartesianIndex(1, 2) => 12])
+@test issetequal(storedvalues(a), [12])
+@test sum(a) == 12
+@test isreal(a)
+@test !iszero(a)
+@test mapreduce(x -> 2x, +, a) == 24
+````
+
+AbstractArray functionality:
+
+````julia
+b = a .+ 2 .* a'
+@test b isa SparseMatrixDOK{Float64}
+@test b == [0 12; 24 0]
+@test storedlength(b) == 2
+@test sum(b) == 36
+@test isreal(b)
+@test !iszero(b)
+@test mapreduce(x -> 2x, +, b) == 72
+
+b = permutedims(a, (2, 1))
+@test b isa SparseMatrixDOK{Float64}
+@test b[1, 1] == a[1, 1]
+@test b[2, 1] == a[1, 2]
+@test b[1, 2] == a[2, 1]
+@test b[2, 2] == a[2, 2]
+
+b = a * a'
+@test b isa SparseMatrixDOK{Float64}
+@test b == [144 0; 0 0]
+@test storedlength(b) == 1
 ````
 
-Examples go here.
+Second column.
+
+````julia
+b = a[1:2, 2]
+@test b isa SparseVectorDOK{Float64}
+@test b == [12, 0]
+@test storedlength(b) == 1
+
+a = SparseArrayDOK{Float64}(2, 2)
+a .= 2
+for I in eachindex(a)
+  @test a[I] == 2
+end
+@test storedlength(a) == length(a)
+
+a = SparseArrayDOK{Float64}(2, 2)
+fill!(a, 2)
+for I in eachindex(a)
+  @test a[I] == 2
+end
+@test storedlength(a) == length(a)
+
+a = SparseArrayDOK{Float64}(2, 2)
+fill!(a, 0)
+@test iszero(a)
+@test iszero(storedlength(a))
+
+a = SparseArrayDOK{Float64}(2, 2)
+a[1, 2] = 12
+zero!(a)
+@test iszero(a)
+@test iszero(storedlength(a))
+
+a = SparseArrayDOK{Float64}(2, 2)
+a[1, 2] = 12
+b = zero(a)
+@test iszero(b)
+@test iszero(storedlength(b))
+````
 
 ---
 

TODO.md

@@ -1,124 +1 @@
-# To-do
-
-- Remove Compat.jl from dependencies and test dependencies.
-- Unregistered dependencies: BroadcastMapConversion, NestedPermutedDimsArrays, TypeParameterAccessors.
-- Create SparseArraysBaseLinearAlgebraExt, SparseArraysBaseNestedPermutedDimsArraysExt, SparseArraysBaseVectorInterfaceExt.
-- Change [sources] entries from paths to urls.
-
-# SparseArraysBase
-
-SparseArraysBase is a package that aims to expand on the sparse array functionality that is currently in Julia Base.
-While SparseArrays.jl is centered mostly around `SparseMatrixCSC` and the SuiteSparse library, here we wish to broaden the scope a bit, and consider generic sparse arrays.
-Abstractly, the mental model can be considered as a storage object that holds the stored values, and a bijection between the array indices and the indices of the storage.
-For now, we focus on providing efficient implementations of Dictionary of Key (DOK) type sparse storage formats, but may expand upon this in the future.
-As a result, for typical linear algebra routines, we still expect `SparseMatrixCSC` to be the object of choice.
-
-The design consists of roughly three components:
-- `AbstractSparseArray` interface functions
-- Overloaded Julia base methods
-- `SparseArrayDOK` struct that implements this
-
-## AbstractSparseArray
-
-The first part consists of typical functions that are useful in the context of sparse arrays.
-The minimal interface, which enables the usage of the rest of this package, consists of the following functions:
-
-| Signature | Description | Default |
-|-----------|-------------|---------|
-| `sparse_storage(a::AbstractArray)` | Returns the storage object of the sparse array | `a` |
-| `storage_index_to_index(a::AbstractArray, I)` | Converts a storage index to an array index | `I` |
-| `index_to_storage_index(a::AbstractArray, I)` | Converts an array index to a storage index | `I` |
-
-Using these primitives, several convenience functions are defined to facilitate the writing of sparse array algorithms.
-
-| Signature | Description | Default |
-|-----------|-------------|---------|
-| `storage_indices(a)` | Returns the indices of the storage | `eachindex(sparse_storage(a))` |
-| `stored_indices(a)` | Returns the indices of the stored values | `Iterators.map(Base.Fix1(storage_index_to_index, a), storage_indices(a))` |
-| `stored_length(a)` | Returns the number of stored values | `length(storage_indices(a))` |
-
-<!-- TODO: `getindex!`, `increaseindex!`, `sparse_map`, expose "zero" functionality?  -->
-
-Interesting to note here is that the design is such that we can define sparse arrays without having to subtype `AbstractSparseArray`.
-To achieve this, each function `f` is defined in terms of `sparse_f`, rather than directly overloading `f`.
-<!--
-TODO:
-In order to opt-in to the sparse array functionality, one needs to dispatch the functions through `sparse_f` instead of `f`.
-For convenience, you can automatically dispatch all functions through `sparse_f` by using the following macro:
-
-```julia
-@abstractsparsearray MySparseArrayType
-```
--->
-
-## Overloaded Julia base methods
-
-The second part consists of overloading Julia base methods to work with sparse arrays.
-In particular, specialised implementations exist for the following functions:
-
-- `sparse_similar`
-- `sparse_reduce`
-- `sparse_map`
-- `sparse_map!`
-- `sparse_all`
-- `sparse_any`
-- `sparse_isequal`
-- `sparse_fill!`
-- `sparse_zero`, `sparse_zero!`, `sparse_iszero`
-- `sparse_one`, `sparse_one!`, `sparse_isone`
-- `sparse_reshape`, `sparse_reshape!`
-- `sparse_cat`, `sparse_cat!`
-- `sparse_copy!`, `sparse_copyto!`
-- `sparse_permutedims`, `sparse_permutedims!`
-- `sparse_mul!`, `sparse_dot`
-
-## SparseArrayDOK
-
-Finally, the `SparseArrayDOK` struct is provided as a concrete implementation of the `AbstractSparseArray` interface.
-It is a dictionary of keys (DOK) type sparse array, which stores the values in a `Dictionaries.jl` dictionary, and maps the indices to the keys of the dictionary.
-This model is particularly useful for sparse arrays with a small number of non-zero elements, or for arrays that are constructed incrementally, as it boasts fast random accesses and insertions.
-The drawback is that sequential iteration is slower than for other sparse array types, leading to slower linear algebra operations.
-For the purposes of `SparseArraysBase`, this struct will serve as the canonical example of a sparse array, and will be returned by default when new sparse arrays are created.
-
-One particular feature of `SparseArrayDOK` is that it can be used in cases where the non-stored entries have to be constructed in a non-trivial way.
-Typically, sparse arrays use `zero(eltype(a))` to construct the non-stored entries, but this is not always sufficient.
-A concrete example is found in `BlockSparseArrays.jl`, where initialization of the non-stored entries requires the construction of a block of zeros of appropriate size.
-
-<!-- TODO: update TODOs -->
-
-## TODO
-Still need to implement `Base` functions:
-```julia
-[x] sparse_zero(a::AbstractArray) = similar(a)
-[x] sparse_iszero(a::AbstractArray) = iszero(nonzero_length(a)) # Uses `all`, make `sparse_all`?
-[x] sparse_one(a::AbstractArray) = ...
-[x] sparse_isreal(a::AbstractArray) = ... # Uses `all`, make `sparse_all`?
-[x] sparse_isequal(a1::AbstractArray, a2::AbstractArray) = ...
-[x] sparse_conj!(a::AbstractArray) = conj!(nonzeros(a))
-[x] sparse_reshape(a::AbstractArray, dims) = ...
-[ ] sparse_all(f, a::AbstractArray) = ...
-[ ] sparse_getindex(a::AbstractArray, 1:2, 2:3) = ... # Slicing
-```
-`LinearAlgebra` functions:
-```julia
-[ ] sparse_mul!
-[ ] sparse_lmul!
-[ ] sparse_ldiv!
-[ ] sparse_rdiv!
-[ ] sparse_axpby!
-[ ] sparse_axpy!
-[ ] sparse_norm
-[ ] sparse_dot/sparse_inner
-[ ] sparse_adoint!
-[ ] sparse_transpose!
-
-# Using conversion to `SparseMatrixCSC`:
-[ ] sparse_qr
-[ ] sparse_eigen
-[ ] sparse_svd
-```
-`TensorAlgebra` functions:
-```julia
-[ ] add!
-[ ] contract!
-```
+- Updates for latest Derive.

docs/Project.toml

@@ -1,7 +1,5 @@
 [deps]
-BroadcastMapConversion = "4a4adec5-520f-4750-bb37-d5e66b4ddeb2"
+Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
-NestedPermutedDimsArrays = "2c2a8ec4-3cfc-4276-aa3e-1307b4294e58"
 SparseArraysBase = "0d5efcca-f356-4864-8770-e1ed8d78f208"
-TypeParameterAccessors = "7e5a90cf-f82e-492e-a09b-e3e26432c138"

examples/Project.toml

@@ -1,5 +1,4 @@
 [deps]
-BroadcastMapConversion = "4a4adec5-520f-4750-bb37-d5e66b4ddeb2"
-NestedPermutedDimsArrays = "2c2a8ec4-3cfc-4276-aa3e-1307b4294e58"
+Dictionaries = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4"
 SparseArraysBase = "0d5efcca-f356-4864-8770-e1ed8d78f208"
-TypeParameterAccessors = "7e5a90cf-f82e-492e-a09b-e3e26432c138"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"