Switch formatting to Runic (#72)

* format using runic

* update format workflow

* format

* remove juliaformatter toml file

* move code

* small type changes to ising constants

* add newline

* manual formatting changes for looptnr

* remove default `run` function

* format blocking.jl

* format cft.jl

* remove defaults.jl

* format projectors.jl

Author: VictorVanthilt

.JuliaFormatter.toml

@@ -5,43 +5,13 @@ on:
     branches:
       - 'main'
       - 'master'
-      - 'release-'
     tags: '*'
   pull_request:
+    branches:
+      - 'main'
+      - 'master'
 
 jobs:
-  build:
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        version:
-          - '1' # automatically expands to the latest stable 1.x release of Julia
-        os:
-          - ubuntu-latest
-        arch:
-          - x64
-    steps:
-      - uses: julia-actions/setup-julia@latest
-        with:
-          version: ${{ matrix.version }}
-          arch: ${{ matrix.arch }}
-
-      - uses: actions/checkout@v4
-      - name: Install JuliaFormatter and format
-        # This will use the latest version by default but you can set the version like so:
-        #
-        # julia  -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter", version="0.13.0"))'
-        run: |
-          julia  -e 'using Pkg; Pkg.add(PackageSpec(name="JuliaFormatter"))'
-          julia  -e 'using JuliaFormatter; format(".", verbose=true)'
-      - name: Format check
-        run: |
-          julia -e '
-          out = Cmd(`git diff --name-only`) |> read |> String
-          if out == ""
-              exit(0)
-          else
-              @error "Some files have not been formatted !!!"
-              write(stdout, out)
-              exit(1)
-          end'
+  formatcheck:
+    name: "Format Check"
+    uses: "QuantumKitHub/QuantumKitHubActions/.github/workflows/FormatCheck.yml@main"

.github/workflows/FormatCheck.yml

@@ -3,12 +3,16 @@ using Documenter
 using DocumenterCitations
 
 bibpath = joinpath(@__DIR__, "src", "assets", "tnrkit.bib")
-bib = CitationBibliography(bibpath; style=:authoryear)
+bib = CitationBibliography(bibpath; style = :authoryear)
 
-makedocs(; sitename="TNRKit.jl",
-         pages=["Home" => "index.md"
-                "Library" => "lib/lib.md"
-                "References" => "references.md"],
-         plugins=[bib])
+makedocs(;
+    sitename = "TNRKit.jl",
+    pages = [
+        "Home" => "index.md"
+        "Library" => "lib/lib.md"
+        "References" => "references.md"
+    ],
+    plugins = [bib]
+)
 
-deploydocs(; repo="github.com/VictorVanthilt/TNRKit.jl.git", push_preview=true)
+deploydocs(; repo = "github.com/VictorVanthilt/TNRKit.jl.git", push_preview = true)

docs/make.jl

@@ -4,16 +4,16 @@ using Revise, TensorKit, TNRKit
 trg_f(steps::Int, data) = abs(log(data[end]) * 2.0^(-steps))
 
 # stop when converged or after 50 steps, whichever comes first
-stopping_criterion = convcrit(1e-16, trg_f) & maxiter(20)
+stopping_criterion = convcrit(1.0e-16, trg_f) & maxiter(20)
 
 # choose a TensorKit truncation scheme
-trunc = truncdim(16) & truncbelow(1e-40)
+trunc = truncdim(16) & truncbelow(1.0e-40)
 
 # initialize the TRG scheme
 scheme = TRG(classical_ising(1.0))
 
 # run the TRG scheme (and normalize and store the norm in the beginning (finalize_beginning=true))
-data = run!(scheme, trunc, stopping_criterion; finalize_beginning=true)
+data = run!(scheme, trunc, stopping_criterion; finalize_beginning = true)
 # or: data = run!(scheme, truncdim(16)), this will default to maxiter(100)
 
 # initialize the BTRG scheme

examples/example.jl

@@ -52,7 +52,7 @@ export run!
 # models
 include("models/ising.jl")
 export classical_ising, classical_ising_symmetric, ising_βc, f_onsager,
-       ising_βc_3D, classical_ising_symmetric_3D, classical_ising_3D
+    ising_βc_3D, classical_ising_symmetric_3D, classical_ising_3D
 
 include("models/gross-neveu.jl")
 export gross_neveu_start

src/TNRKit.jl

@@ -1,50 +1,50 @@
-#! format: off
 function P_tensor()
     P = zeros(ComplexF64, 2, 2, 2, 2, 2, 2, 2, 2)
     for (pi1, pj1, pi2, pj2, i1, j1, i2, j2) in Iterators.product([0:1 for _ in 1:8]...)
-        P[pi1+1, pj1+1, pi2+1, pj2+1, i1+1, j1+1, i2+1, j2+1] =
+        P[pi1 + 1, pj1 + 1, pi2 + 1, pj2 + 1, i1 + 1, j1 + 1, i2 + 1, j2 + 1] =
             i1 * (j1 + j2 + pi1 + pi2) + i2 * (j2 + pi1 + pi2) + pj1 * (pi1 + pi2) + pj2 * pi2 + pi1 + pi2
     end
     return P
 end
 
-# Manually defining the A and A_bar tensors
-A = zeros(ComplexF64, 2, 2, 2, 2)
-A[2, 2, 1, 1] = 1 + 1im
-A[1, 1, 2, 2] = -1 - 1im
-A[2, 1, 1, 2] = 1 - 1im
-A[2, 1, 2, 1] = 2
-A[1, 2, 1, 2] = -2im
-A[1, 2, 2, 1] = 1 - 1im
-
-A_bar = zeros(ComplexF64, 2, 2, 2, 2)
-A_bar[2, 2, 1, 1] = -1 + 1im
-A_bar[1, 1, 2, 2] = 1 - 1im
-A_bar[2, 1, 1, 2] = -1 - 1im
-A_bar[2, 1, 2, 1] = -2
-A_bar[1, 2, 1, 2] = -2im
-A_bar[1, 2, 2, 1] = -1 - 1im
-
 function gross_neveu_8_leg_tensor(μ::Number, m::Number, g::Number)
+    # Manually defining the A and A_bar tensors
+    A = zeros(ComplexF64, 2, 2, 2, 2)
+    A[2, 2, 1, 1] = 1 + 1im
+    A[1, 1, 2, 2] = -1 - 1im
+    A[2, 1, 1, 2] = 1 - 1im
+    A[2, 1, 2, 1] = 2
+    A[1, 2, 1, 2] = -2im
+    A[1, 2, 2, 1] = 1 - 1im
+
+    A_bar = zeros(ComplexF64, 2, 2, 2, 2)
+    A_bar[2, 2, 1, 1] = -1 + 1im
+    A_bar[1, 1, 2, 2] = 1 - 1im
+    A_bar[2, 1, 1, 2] = -1 - 1im
+    A_bar[2, 1, 2, 1] = -2
+    A_bar[1, 2, 1, 2] = -2im
+    A_bar[1, 2, 2, 1] = -1 - 1im
+
     # Utility Kronecker delta function
     δ(x, y) = ==(x, y)
 
     T = zeros(ComplexF64, 2, 2, 2, 2, 2, 2, 2, 2)
     P = P_tensor()
     V = Vect[FermionParity](0 => 1, 1 => 1)
     for (pi1, pj1, pi2, pj2, i1, j1, i2, j2) in Iterators.product([0:1 for _ in 1:8]...)
-        p = P[pi1+1, pj1+1, pi2+1, pj2+1, i1+1, j1+1, i2+1, j2+1]
-        T[pi1+1, pj1+1, pi2+1, pj2+1, i2+1, j2+1, i1+1, j1+1] =
+        p = P[pi1 + 1, pj1 + 1, pi2 + 1, pj2 + 1, i1 + 1, j1 + 1, i2 + 1, j2 + 1]
+        T[pi1 + 1, pj1 + 1, pi2 + 1, pj2 + 1, i2 + 1, j2 + 1, i1 + 1, j1 + 1] =
             ((-1)^p) * exp(0.5 * μ * (i2 - j2 + pi2 - pj2)) * ((1 / sqrt(2))^(i1 + i2 + j1 + j2 + pi1 + pi2 + pj1 + pj2)) *
-            (((m + 2)^2 + 2 * g^2) * δ(i1 + i2 + pj1 + pj2, 0) * δ(j1 + j2 + pi1 + pi2, 0) -
-             (m + 2) * δ(i1 + i2 + pj1 + pj2, 1) * δ(j1 + j2 + pi1 + pi2, 1) -
-             ((-1)^(i1 + i2 + j2 + pi1)) * (1im^(i2 + j2 + pi2 + pj2)) * (m + 2) * δ(i1 + i2 + pj1 + pj2, 1) * δ(j1 + j2 + pi1 + pi2, 1) -
-             A_bar[i1+1, i2+1, pj1+1, pj2+1] * A[j1+1, j2+1, pi1+1, pi2+1])
+            (
+            ((m + 2)^2 + 2 * g^2) * δ(i1 + i2 + pj1 + pj2, 0) * δ(j1 + j2 + pi1 + pi2, 0) -
+                (m + 2) * δ(i1 + i2 + pj1 + pj2, 1) * δ(j1 + j2 + pi1 + pi2, 1) -
+                ((-1)^(i1 + i2 + j2 + pi1)) * (1im^(i2 + j2 + pi2 + pj2)) * (m + 2) * δ(i1 + i2 + pj1 + pj2, 1) * δ(j1 + j2 + pi1 + pi2, 1) -
+                A_bar[i1 + 1, i2 + 1, pj1 + 1, pj2 + 1] * A[j1 + 1, j2 + 1, pi1 + 1, pi2 + 1]
+        )
 
     end
     return TensorMap(T, V ⊗ V ⊗ V ⊗ V ← V ⊗ V ⊗ V ⊗ V)
 end
-#! format: on
 
 function gross_neveu_start(μ::Number, m::Number, g::Number)
     T_unfused = gross_neveu_8_leg_tensor(μ, m, g)
@@ -53,7 +53,7 @@ function gross_neveu_start(μ::Number, m::Number, g::Number)
     Udg = adjoint(U)
 
     @tensor T_fused[-1 -2; -3 -4] := T_unfused[1 2 3 4; 5 6 7 8] * U[-1; 1 2] * U[-2; 3 4] *
-                                     Udg[5 6; -3] * Udg[7 8; -4]
+        Udg[5 6; -3] * Udg[7 8; -4]
 
     # restore the TNRKit.jl convention
     return T_fused

src/models/gross-neveu.jl

@@ -1,15 +1,21 @@
-const ising_βc = BigFloat(log(1.0 + sqrt(2)) / 2.0)
-const ising_cft_exact = [1 / 8, 1, 9 / 8, 9 / 8, 2, 2, 2, 2, 17 / 8, 17 / 8, 17 / 8, 3, 3,
-                         3, 3, 3,
-                         25 / 8, 25 / 8, 25 / 8, 25 / 8, 25 / 8, 25 / 8]
-function classical_ising(β::Number; h=0)
+const ising_βc = BigFloat(log(BigFloat(1.0) + sqrt(BigFloat(2.0))) / BigFloat(2.0))
+const ising_cft_exact = [
+    1 / 8, 1, 9 / 8, 9 / 8, 2, 2, 2, 2, 17 / 8, 17 / 8, 17 / 8, 3, 3,
+    3, 3, 3,
+    25 / 8, 25 / 8, 25 / 8, 25 / 8, 25 / 8, 25 / 8,
+]
+function classical_ising(β::Number; h = 0)
     function σ(i::Int64)
         return 2i - 3
     end
 
-    T_array = Float64[exp(β * (σ(i)σ(j) + σ(j)σ(l) + σ(l)σ(k) + σ(k)σ(i)) +
-                          h / 2 * β * (σ(i) + σ(j) + σ(k) + σ(l)))
-                      for i in 1:2, j in 1:2, k in 1:2, l in 1:2]
+    T_array = Float64[
+        exp(
+                β * (σ(i)σ(j) + σ(j)σ(l) + σ(l)σ(k) + σ(k)σ(i)) +
+                h / 2 * β * (σ(i) + σ(j) + σ(k) + σ(l))
+            )
+            for i in 1:2, j in 1:2, k in 1:2, l in 1:2
+    ]
 
     T = TensorMap(T_array, ℝ^2 ⊗ ℝ^2 ← ℝ^2 ⊗ ℝ^2)
 
@@ -23,8 +29,8 @@ function classical_ising_symmetric(β)
 
     S = ℤ₂Space(0 => 1, 1 => 1)
     T = zeros(Float64, S ⊗ S ← S ⊗ S)
-    block(T, Irrep[ℤ₂](0)) .= [2x^2 2x*y; 2x*y 2y^2]
-    block(T, Irrep[ℤ₂](1)) .= [2x*y 2x*y; 2x*y 2x*y]
+    block(T, Irrep[ℤ₂](0)) .= [2x^2 2x * y; 2x * y 2y^2]
+    block(T, Irrep[ℤ₂](1)) .= [2x * y 2x * y; 2x * y 2x * y]
 
     return T
 end
@@ -41,7 +47,7 @@ function classical_ising_symmetric_3D(β)
         for a in 1:2
             # Outer product of W[a, :] with itself 6 times
             T_array[i, j, k, l, m, n] += W[a, i] * W[a, j] * W[a, k] * W[a, l] * W[a, m] *
-                                         W[a, n]
+                W[a, n]
         end
     end
     S = ℤ₂Space(0 => 1, 1 => 1)
@@ -50,7 +56,7 @@ function classical_ising_symmetric_3D(β)
     return permute(T, ((1, 4), (5, 6, 2, 3)))
 end
 
-function classical_ising_3D(β; J=1.0)
+function classical_ising_3D(β; J = 1.0)
     K = β * J
 
     # Boltzmann weights
@@ -63,13 +69,13 @@ function classical_ising_3D(β; J=1.0)
     O[1, 1, 1, 1, 1, 1] = 1
     O[2, 2, 2, 2, 2, 2] = 1
     @tensor o[-1 -2; -3 -4 -5 -6] := O[1 2; 3 4 5 6] * q[-1; 1] * q[-2; 2] * q[-3; 3] *
-                                     q[-4; 4] * q[-5; 5] * q[-6; 6]
+        q[-4; 4] * q[-5; 5] * q[-6; 6]
 
     TMS = ℂ^2 ⊗ (ℂ^2)' ← ℂ^2 ⊗ ℂ^2 ⊗ (ℂ^2)' ⊗ (ℂ^2)'
 
     return TensorMap(o, TMS)
 end
-ising_βc_3D = 1 / 4.51152469
+const ising_βc_3D = 1.0 / 4.51152469
 
 classical_ising_symmetric_3D() = classical_ising_symmetric_3D(ising_βc_3D)
 classical_ising_3D() = classical_ising_3D(ising_βc_3D)

src/models/ising.jl

@@ -1,4 +1,5 @@
 potts_βc(q) = log(1.0 + sqrt(q))
+
 function classical_potts(q::Int, β::Float64)
     V = ℂ^q
     A_potts = TensorMap(zeros, V ⊗ V ← V ⊗ V)
@@ -17,7 +18,7 @@ function classical_potts(q::Int, β::Float64)
 end
 classical_potts(q::Int) = classical_potts(q, potts_βc(q))
 
-function weyl_heisenberg_matrices(Q::Int, elt=ComplexF64)
+function weyl_heisenberg_matrices(Q::Int, elt = ComplexF64)
     U = zeros(elt, Q, Q) # clock matrix
     V = zeros(elt, Q, Q) # shift matrix
     W = zeros(elt, Q, Q) # DFT
@@ -49,8 +50,10 @@ function classical_potts_symmetric(q::Int64, β::Float64)
     Vp = Vect[ZNIrrep{q}](sector => 1 for sector in 0:(q - 1))
     _, _, W = weyl_heisenberg_matrices(q)
     P = TensorMap(W, ℂ^q ← ℂ^q)
-    A_potts = TensorMap(reshape(((P' ⊗ P') * A_potts * (P ⊗ P)).data, (q, q, q, q)),
-                        Vp ⊗ Vp ← Vp ⊗ Vp)
+    A_potts = TensorMap(
+        reshape(((P' ⊗ P') * A_potts * (P ⊗ P)).data, (q, q, q, q)),
+        Vp ⊗ Vp ← Vp ⊗ Vp
+    )
     return A_potts
 end
 classical_potts_symmetric(q::Int) = classical_potts_symmetric(q, potts_βc(q))

src/models/potts.jl

@@ -1,22 +1,26 @@
 # Taken from MPSKitModels.jl
 sixvertex(symmetry::Type{<:Sector}; kwargs...) = sixvertex(ComplexF64, symmetry; kwargs...)
-function sixvertex(elt::Type{<:Number}=ComplexF64, (::Type{Trivial})=Trivial; a=1.0, b=1.0,
-                   c=1.0)
-    d = elt[a 0 0 0
-            0 c b 0
-            0 b c 0
-            0 0 0 a]
+function sixvertex(
+        elt::Type{<:Number} = ComplexF64, (::Type{Trivial}) = Trivial; a = 1.0, b = 1.0,
+        c = 1.0
+    )
+    d = elt[
+        a 0 0 0
+        0 c b 0
+        0 b c 0
+        0 0 0 a
+    ]
     return TensorMap(d, ℂ^2 ⊗ ℂ^2, ℂ^2 ⊗ ℂ^2)
 end
-function sixvertex(elt::Type{<:Number}, ::Type{U1Irrep}; a=1.0, b=1.0, c=1.0)
+function sixvertex(elt::Type{<:Number}, ::Type{U1Irrep}; a = 1.0, b = 1.0, c = 1.0)
     pspace = U1Space(-1 // 2 => 1, 1 // 2 => 1)
     mpo = zeros(elt, pspace ⊗ pspace, pspace ⊗ pspace)
     block(mpo, Irrep[U₁](0)) .= [b c; c b]
     block(mpo, Irrep[U₁](1)) .= reshape([a], (1, 1))
     block(mpo, Irrep[U₁](-1)) .= reshape([a], (1, 1))
     return mpo
 end
-function sixvertex(elt::Type{<:Number}, ::Type{CU1Irrep}; a=1.0, b=1.0, c=1.0)
+function sixvertex(elt::Type{<:Number}, ::Type{CU1Irrep}; a = 1.0, b = 1.0, c = 1.0)
     pspace = CU1Space(1 // 2 => 1)
     mpo = zeros(elt, pspace ⊗ pspace, pspace ⊗ pspace)
     block(mpo, Irrep[CU₁](0, 0)) .= reshape([b + c], (1, 1))

src/models/sixvertex.jl

@@ -27,7 +27,7 @@ mutable struct ATRG <: TNRScheme
     T::TensorMap
 
     finalize!::Function
-    function ATRG(T::TensorMap{E,S,2,2}; finalize=(finalize!)) where {E,S}
+    function ATRG(T::TensorMap{E, S, 2, 2}; finalize = (finalize!)) where {E, S}
         return new(T, finalize)
     end
 end
@@ -43,7 +43,7 @@ function step!(scheme::ATRG, trunc::TensorKit.TruncationScheme)
 end
 
 function _step!(scheme::ATRG, trunc::TensorKit.TruncationScheme)
-    A, S, B, _ = tsvd(scheme.T, ((1, 3), (2, 4)); trunc=trunc)
+    A, S, B, _ = tsvd(scheme.T, ((1, 3), (2, 4)); trunc = trunc)
     C, D = deepcopy.([A, B])
 
     @tensor begin
@@ -53,7 +53,7 @@ function _step!(scheme::ATRG, trunc::TensorKit.TruncationScheme)
 
     @tensor M[-1 -2; -3 -4] := B[-3; 1 -4] * C[-1 1; -2]
 
-    X, S, Y, _ = tsvd(M, ((1, 3), (2, 4)); trunc=trunc)
+    X, S, Y, _ = tsvd(M, ((1, 3), (2, 4)); trunc = trunc)
     sqrtS = sqrt(S)
 
     @tensor begin
@@ -62,17 +62,17 @@ function _step!(scheme::ATRG, trunc::TensorKit.TruncationScheme)
     end
 
     @tensor Q[-1 -2; -3 -4] := A[3 -3; 2] * D[1; -2 4] * X[4 2; -4] *
-                               Y[-1 1; 3]
+        Y[-1 1; 3]
 
-    H, S, G, _ = tsvd(Q; trunc=trunc)
+    H, S, G, _ = tsvd(Q; trunc = trunc)
     sqrtS = sqrt(S)
 
     @tensor begin
         H[-1 -2; -3] = H[-1 -2; 1] * sqrtS[1; -3]
         G[-1; -2 -3] = sqrtS[-1; 1] * G[1; -2 -3]
     end
 
-    @tensor scheme.T[-1 -2; -3 -4] := G[-1; -3 1] * H[1 -2; -4]
+    return @tensor scheme.T[-1 -2; -3 -4] := G[-1; -3 1] * H[1 -2; -4]
 end
 
 function Base.show(io::IO, scheme::ATRG)