[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,6 +1,9 @@
+ci:
+  skip: [julia-formatter]
+
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.5.0
+  rev: v5.0.0
   hooks:
   - id: check-merge-conflict
   - id: check-toml
@@ -10,7 +13,8 @@ repos:
     exclude_types: [markdown]  # incompatible with Literate.jl
   - id: trailing-whitespace
     exclude: '.*references/.*\.txt$'  # do not check reference TN images
-- repo: https://github.com/qiaojunfeng/pre-commit-julia-format
-  rev: v0.2.0
+
+- repo: "https://github.com/domluna/JuliaFormatter.jl"
+  rev: v2.1.1
   hooks:
-  - id: julia-format
+  - id: "julia-formatter"

NDTensors/src/blocksparse/block.jl

@@ -68,7 +68,7 @@ convert(::Type{Block{N}}, t::Tuple) where {N} = Block{N}(t)
 
 gethash(b::Block) = b.hash[]
 
-sethash!(b::Block, h::UInt) = (b.hash[] = h; return b)
+sethash!(b::Block, h::UInt) = (b.hash[]=h; return b)
 
 #
 # Basic functions

NDTensors/src/dense/densetensor.jl

@@ -134,7 +134,7 @@ end
 @propagate_inbounds @inline getindex(T::DenseTensor, i::Integer) = storage(T)[i]
 
 @propagate_inbounds @inline function setindex!(T::DenseTensor, v, i::Integer)
-  return (storage(T)[i] = v; T)
+  return (storage(T)[i]=v; T)
 end
 
 #

NDTensors/src/diag/tensoralgebra/contract.jl

@@ -93,7 +93,7 @@ function contract!(
     # elements of A and B.
     # `expose` allows dispatching on the data type
     # in order to allow scalar indexing on GPU.
-    expose(R)[] = mapreduce(*, +, diagview(T1), diagview(T2))
+    expose(R)[] = mapreduce(*,+,diagview(T1),diagview(T2))
   else
     diagview(R) .= diagview(T1) .* diagview(T2)
   end

NDTensors/src/lib/BackendSelection/src/backend_types.jl

@@ -28,8 +28,9 @@ for type in (:Algorithm, :Backend)
     function Base.show(io::IO, backend::$type)
       return print(io, "$($type) type ", backend_string(backend), ", ", parameters(backend))
     end
-    Base.print(io::IO, backend::$type) =
-      print(io, backend_string(backend), ", ", parameters(backend))
+    Base.print(io::IO, backend::$type) = print(
+      io, backend_string(backend), ", ", parameters(backend)
+    )
   end
 end
 

NDTensors/src/tupletools.jl

@@ -60,7 +60,7 @@ permute(s::AbstractVector, perm) = _permute(s, perm)
 sim(s::NTuple) = s
 
 # type stable findfirst
-@inline _findfirst(args...) = (i = findfirst(args...); i === nothing ? 0 : i)
+@inline _findfirst(args...) = (i=findfirst(args...); i === nothing ? 0 : i)
 
 """
     getperm(col1,col2)

Project.toml

@@ -1,7 +1,7 @@
 name = "ITensors"
 uuid = "9136182c-28ba-11e9-034c-db9fb085ebd5"
 authors = ["Matthew Fishman <mfishman@flatironinstitute.org>", "Miles Stoudenmire <mstoudenmire@flatironinstitute.org>"]
-version = "0.9.2"
+version = "0.9.3"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

jenkins/Dockerfile

@@ -13,4 +13,4 @@ RUN apt-get update && \
 
 ARG JULIA=1.6
 RUN curl -s -L https://julialang-s3.julialang.org/bin/linux/x64/${JULIA}/julia-${JULIA}-latest-linux-x86_64.tar.gz | \
-    tar -C /usr/local -x -z --strip-components=1 -f -
+    tar -C /usr/local -x -z --strip-components=1 -f -

src/itensor.jl

@@ -119,8 +119,9 @@
 and a set of indices.
 The ITensor stores a view of the TensorStorage.
 """
-ITensor(as::AliasStyle, st::TensorStorage, is)::ITensor =
-  ITensor(as, Tensor(as, st, Tuple(is)))
+ITensor(as::AliasStyle, st::TensorStorage, is)::ITensor = ITensor(
+  as, Tensor(as, st, Tuple(is))
+)
 ITensor(as::AliasStyle, is, st::TensorStorage)::ITensor = ITensor(as, st, is)
 
 ITensor(st::TensorStorage, is)::ITensor = itensor(Tensor(NeverAlias(), st, Tuple(is)))
@@ -137,8 +138,9 @@
 """
 itensor(args...; kwargs...)::ITensor = ITensor(AllowAlias(), args...; kwargs...)
 
-ITensor(::AliasStyle, args...; kwargs...)::ITensor =
-  error("ITensor constructor with input arguments of types `$(typeof.(args))` not defined.")
+ITensor(::AliasStyle, args...; kwargs...)::ITensor = error(
+  "ITensor constructor with input arguments of types `$(typeof.(args))` not defined."
+)
 
 """
     Tensor(::ITensor)
@@ -300,7 +302,7 @@
 
 emptyITensor(is...) = emptyITensor(EmptyNumber, indices(is...))
 
-function emptyITensor(::Type{ElT}=EmptyNumber) where {ElT<:Number}
+function emptyITensor((::Type{ElT})=EmptyNumber) where {ElT<:Number}
   return itensor(EmptyTensor(ElT, ()))
 end
 
@@ -1092,8 +1094,9 @@
 end
 
 # Special case that handles indexing with `end` like `A[i => end, j => 3]`
-@propagate_inbounds getindex(T::ITensor, I::Union{Integer,LastVal}...)::Any =
-  _getindex(tensor(T), I...)
+@propagate_inbounds getindex(T::ITensor, I::Union{Integer,LastVal}...)::Any = _getindex(
+  tensor(T), I...
+)
 
 # Simple version with just integer indexing, bounds checking gets done by NDTensors
 
@@ -1119,8 +1122,9 @@
 A[i => 1, i' => 2] # 2.0, same as: A[i' => 2, i => 1]
 ```
 """
-@propagate_inbounds (getindex(T::ITensor, ivs::Vararg{Any,N})::Any) where {N} =
-  _getindex(tensor(T), ivs...)
+@propagate_inbounds (getindex(T::ITensor, ivs::Vararg{Any,N})::Any) where {N} = _getindex(
+  tensor(T), ivs...
+)
 
 ## Allowing one to get the first ITensor element if its an order 0 tensor or an order 1 tensor with a dimension of 1. Also convert GPU back to CPU
 @propagate_inbounds function getindex(T::ITensor)::Any

src/symmetrystyle.jl

@@ -7,9 +7,8 @@ function symmetrystyle(T)
   return error("No SymmetryStyle defined for the specified object $T of type $(typeof(T))")
 end
 
-symmetrystyle(T, S, U, V...)::SymmetryStyle = (
-  Base.@_inline_meta; symmetrystyle(symmetrystyle(T), symmetrystyle(S, U, V...))
-)
+symmetrystyle(T, S, U, V...)::SymmetryStyle =
+  (Base.@_inline_meta; symmetrystyle(symmetrystyle(T), symmetrystyle(S, U, V...)))
 
 symmetrystyle(T, S)::SymmetryStyle = symmetrystyle(symmetrystyle(T), symmetrystyle(S))
 

src/tensor_operations/tensor_algebra.jl

@@ -139,7 +139,7 @@ generally optimal and is found via the optimaltree function in TensorOperations.
 function optimal_contraction_sequence(As)
   return throw(
     ArgumentError(
-      "Optimal contraction sequence isn't defined. Try loading a backend package like 
+      "Optimal contraction sequence isn't defined. Try loading a backend package like
         TensorOperations.jl"
     ),
   )
@@ -581,23 +581,23 @@ function product(A::ITensor, B::ITensor; apply_dag::Bool=false)
   danglings_inds = unioninds(danglings_indsA, danglings_indsB)
   if hassameinds(common_paired_indsA, common_paired_indsB)
     # matrix-matrix product
-    A′ = prime(A; inds=!danglings_inds)
-    AB = mapprime(A′ * B, 2 => 1; inds=!danglings_inds)
+    A′ = prime(A; inds=(!danglings_inds))
+    AB = mapprime(A′ * B, 2 => 1; inds=(!danglings_inds))
     if apply_dag
-      AB′ = prime(AB; inds=!danglings_inds)
-      Adag = swapprime(dag(A), 0 => 1; inds=!danglings_inds)
-      return mapprime(AB′ * Adag, 2 => 1; inds=!danglings_inds)
+      AB′ = prime(AB; inds=(!danglings_inds))
+      Adag = swapprime(dag(A), 0 => 1; inds=(!danglings_inds))
+      return mapprime(AB′ * Adag, 2 => 1; inds=(!danglings_inds))
     end
     return AB
   elseif isempty(common_paired_indsA) && !isempty(common_paired_indsB)
     # vector-matrix product
     apply_dag && error("apply_dag not supported for matrix-vector product")
-    A′ = prime(A; inds=!danglings_inds)
+    A′ = prime(A; inds=(!danglings_inds))
     return A′ * B
   elseif !isempty(common_paired_indsA) && isempty(common_paired_indsB)
     # matrix-vector product
     apply_dag && error("apply_dag not supported for vector-matrix product")
-    return replaceprime(A * B, 1 => 0; inds=!danglings_inds)
+    return replaceprime(A * B, 1 => 0; inds=(!danglings_inds))
   end
 end
 

test/base/test_itensor.jl

@@ -246,7 +246,7 @@ end
       @test A[b => end - 1, a => 2] == A[a => 2, b => 2]
       @test A[b => end, a => 1] == A[a => 1, b => 3]
       @test A[b => end - 2, a => 1] == A[a => 1, b => 1]
-      @test A[b => end^2 - 7, a => 1] == A[a => 1, b => 2]
+      @test A[b => end ^ 2 - 7, a => 1] == A[a => 1, b => 2]
 
       i, j, k, l = Index.(2, ("i", "j", "k", "l"))
       B = random_itensor(i)

test/base/test_not.jl

@@ -11,7 +11,7 @@ using ITensors, Test
 
   @test hassameinds(Ap, (i', j, k''))
 
-  Ap = prime(A; tags=!ts"j")
+  Ap = prime(A; tags=(!ts"j"))
 
   @test hassameinds(Ap, (i', j, k''))
 
@@ -23,7 +23,7 @@ using ITensors, Test
 
   @test hassameinds(Ap2, (i, j'', k'''))
 
-  Ap2 = prime(A, 2; inds=!i)
+  Ap2 = prime(A, 2; inds=(!i))
 
   @test hassameinds(Ap2, (i, j'', k'''))
 
@@ -39,7 +39,7 @@ using ITensors, Test
 
   @test hassameinds(At2, (settags(i, "y"), j, k'))
 
-  At2 = settags(A, "y"; inds=!IndexSet(j, k'))
+  At2 = settags(A, "y"; inds=(!IndexSet(j, k')))
 
   @test hassameinds(At2, (settags(i, "y"), j, k'))
 

test/base/test_sitetype.jl

@@ -226,7 +226,7 @@ end
 
       o = op("$(ot)_op_1", s, 1, 2)
       @test o ≈ itensor(
-        [i * j for i in 1:(d^2), j in 1:(d^2)], s[2]', s[1]', dag(s[2]), dag(s[1])
+        [i * j for i in 1:(d ^ 2), j in 1:(d ^ 2)], s[2]', s[1]', dag(s[2]), dag(s[1])
       )
 
       d = 4

test/ext/ITensorsChainRulesCoreExt/test_chainrules.jl

@@ -240,11 +240,11 @@ Random.seed!(1234)
     args = (A,)
 
     test_rrule(ZygoteRuleConfig(), f, args...; rrule_f=rrule_via_ad, check_inferred=false)
-    f = x -> (x^2 * δ((i', i)))[1, 1]
+    f = x -> (x ^ 2 * δ((i', i)))[1, 1]
     args = (6.2,)
 
     test_rrule(ZygoteRuleConfig(), f, args...; rrule_f=rrule_via_ad, check_inferred=false)
-    f = x -> (x^2 * δ(i', i))[1, 1]
+    f = x -> (x ^ 2 * δ(i', i))[1, 1]
     args = (5.2,)
     test_rrule(ZygoteRuleConfig(), f, args...; rrule_f=rrule_via_ad, check_inferred=false)
   end
@@ -284,7 +284,7 @@ Random.seed!(1234)
     #args = (2.8 + 3.1im,)
     #test_rrule(ZygoteRuleConfig(), f, args...; rrule_f=rrule_via_ad, check_inferred=false)
     f = function (x)
-      return real((x^3 * ITensor([sin(x) exp(-2x); 3x^3 x+x^2], j', dag(j)))[1, 1])
+      return real((x ^ 3 * ITensor([sin(x) exp(-2x); 3x ^ 3 x + x ^ 2], j', dag(j)))[1, 1])
     end
     args = (3.4 + 2.3im,)
     test_rrule(ZygoteRuleConfig(), f, args...; rrule_f=rrule_via_ad, check_inferred=false)