Runic formatter

.github/workflows/FormatCheck.yml

@@ -1,4 +1,4 @@
-name: "Format Check"
+name: FormatCheck
 
 on:
   push:
@@ -7,10 +7,12 @@ on:
       - 'master'
     tags: '*'
   pull_request:
+    branches:
+      - 'main'
+      - 'master'
 
 jobs:
-  format-check:
+  formatcheck:
     name: "Format Check"
-    uses: "QuantumKitHub/.github/.github/workflows/formatcheck.yml@main"
-    with: 
-      juliaformatter-version: "2"
+    uses: "QuantumKitHub/QuantumKitHubActions/.github/workflows/FormatCheck.yml@main"
+

docs/make.jl

@@ -2,7 +2,7 @@
 if Base.active_project() != joinpath(@__DIR__, "Project.toml")
     using Pkg
     Pkg.activate(@__DIR__)
-    Pkg.develop(PackageSpec(; path=joinpath(@__DIR__, "..")))
+    Pkg.develop(PackageSpec(; path = joinpath(@__DIR__, "..")))
     Pkg.resolve()
     Pkg.instantiate()
 end
@@ -15,7 +15,7 @@ using MPSKitModels: MPSKitModels # used for docstrings
 
 # bibliography
 bibpath = joinpath(@__DIR__, "src", "assets", "pepskit.bib")
-bib = CitationBibliography(bibpath; style=:authoryear)
+bib = CitationBibliography(bibpath; style = :authoryear)
 
 # interlinks
 # Zygote didn't update to documenter v1 yet...
@@ -53,12 +53,12 @@ examples_partition_functions = joinpath.(
 examples_boundary_mps = joinpath.(["boundary_mps"], Ref("index.md"))
 
 makedocs(;
-    modules=[PEPSKit, MPSKitModels],
-    sitename="PEPSKit.jl",
-    format=Documenter.HTML(;
-        prettyurls=true, mathengine, assets=["assets/custom.css"], size_threshold=1024000
+    modules = [PEPSKit, MPSKitModels],
+    sitename = "PEPSKit.jl",
+    format = Documenter.HTML(;
+        prettyurls = true, mathengine, assets = ["assets/custom.css"], size_threshold = 1024000
     ),
-    pages=[
+    pages = [
         "Home" => "index.md",
         "Manual" => ["man/models.md", "man/multithreading.md", "man/precompilation.md"],
         "Examples" => [
@@ -72,9 +72,9 @@ makedocs(;
         "Library" => "lib/lib.md",
         "References" => "references.md",
     ],
-    checkdocs=:none,
+    checkdocs = :none,
     # checkdocs_ignored_modules=[MPSKitModels], # doesn't seem to work...
-    plugins=[bib, links],
+    plugins = [bib, links],
 )
 
-deploydocs(; repo="github.com/QuantumKitHub/PEPSKit.jl.git", push_preview=true)
+deploydocs(; repo = "github.com/QuantumKitHub/PEPSKit.jl.git", push_preview = true)

examples/2d_ising_partition_function/main.jl

@@ -39,7 +39,7 @@ Since we later want to compute the magnetization and energy to check our results
 the appropriate rank-4 tensors here as well while we're at it.
 """
 
-function classical_ising(; beta=log(1 + sqrt(2)) / 2, J=1.0)
+function classical_ising(; beta = log(1 + sqrt(2)) / 2, J = 1.0)
     K = beta * J
 
     ## Boltzmann weights
@@ -92,7 +92,7 @@ settings:
 
 Venv = ℂ^20
 env₀ = CTMRGEnv(Z, Venv)
-env, = leading_boundary(env₀, Z; tol=1e-8, maxiter=500);
+env, = leading_boundary(env₀, Z; tol = 1.0e-8, maxiter = 500);
 
 md"""
 Note that CTMRG environments for partition functions differ from the PEPS environments only
@@ -125,7 +125,7 @@ magnetization and energy per site (where we use `quadgk` to perform integrals of
 auxiliary variable from $0$ to $\pi/2$):
 """
 
-function classical_ising_exact(; beta=log(1 + sqrt(2)) / 2, J=1.0)
+function classical_ising_exact(; beta = log(1 + sqrt(2)) / 2, J = 1.0)
     K = beta * J
 
     k = 1 / sinh(2 * K)^2

examples/3d_ising_partition_function/main.jl

@@ -45,7 +45,7 @@ lattice. To verify our example we will check the magnetization and energy, so we
 the corresponding rank-6 tensors `M` and `E` while we're at it.
 """
 
-function three_dimensional_classical_ising(; beta, J=1.0)
+function three_dimensional_classical_ising(; beta, J = 1.0)
     K = beta * J
 
     ## Boltzmann weights
@@ -150,9 +150,9 @@ we'll specify the specific reverse rule algorithm that will be used to compute t
 of this cost function.
 """
 
-boundary_alg = SimultaneousCTMRG(; maxiter=150, tol=1e-8, verbosity=1)
+boundary_alg = SimultaneousCTMRG(; maxiter = 150, tol = 1.0e-8, verbosity = 1)
 rrule_alg = EigSolver(;
-    solver_alg=KrylovKit.Arnoldi(; maxiter=30, tol=1e-6, eager=true), iterscheme=:diffgauge
+    solver_alg = KrylovKit.Arnoldi(; maxiter = 30, tol = 1.0e-6, eager = true), iterscheme = :diffgauge
 )
 T = InfinitePEPO(O)
 
@@ -167,7 +167,7 @@ function pepo_costfun((peps, env_double_layer, env_triple_layer))
             env_double_layer,
             n_double_layer,
             boundary_alg;
-            alg_rrule=rrule_alg,
+            alg_rrule = rrule_alg,
         )
         ## construct the PEPS-PEPO-PEPS overlap network
         n_triple_layer = InfiniteSquareNetwork(ψ, T)
@@ -177,7 +177,7 @@ function pepo_costfun((peps, env_double_layer, env_triple_layer))
             env_triple_layer,
             n_triple_layer,
             boundary_alg;
-            alg_rrule=rrule_alg,
+            alg_rrule = rrule_alg,
         )
         ## update the environments for reuse
         PEPSKit.ignore_derivatives() do
@@ -242,12 +242,12 @@ function pepo_retract((peps, env_double_layer, env_triple_layer), η, α)
     return (peps´, env_double_layer´, env_triple_layer´), ξ
 end
 function pepo_transport!(
-    ξ,
-    (peps, env_double_layer, env_triple_layer),
-    η,
-    α,
-    (peps´, env_double_layer´, env_triple_layer´),
-)
+        ξ,
+        (peps, env_double_layer, env_triple_layer),
+        η,
+        α,
+        (peps´, env_double_layer´, env_triple_layer´),
+    )
     return PEPSKit.peps_transport!(
         ξ, (peps, env_double_layer), η, α, (peps´, env_double_layer´)
     )
@@ -268,15 +268,15 @@ psi0 = initializePEPS(T, Vpeps)
 env2_0 = CTMRGEnv(InfiniteSquareNetwork(psi0), Venv)
 env3_0 = CTMRGEnv(InfiniteSquareNetwork(psi0, T), Venv)
 
-optimizer_alg = LBFGS(32; maxiter=100, gradtol=1e-5, verbosity=3)
+optimizer_alg = LBFGS(32; maxiter = 100, gradtol = 1.0e-5, verbosity = 3)
 
 (psi_final, env2_final, env3_final), f, = optimize(
     pepo_costfun,
     (psi0, env2_0, env3_0),
     optimizer_alg;
-    inner=PEPSKit.real_inner,
-    retract=pepo_retract,
-    (transport!)=(pepo_transport!),
+    inner = PEPSKit.real_inner,
+    retract = pepo_retract,
+    (transport!) = (pepo_transport!),
 );
 
 md"""

examples/bose_hubbard/main.jl

@@ -87,9 +87,9 @@ optimization framework in the usual way to find the ground state. So, we first s
 algorithms and their tolerances:
 """
 
-boundary_alg = (; tol=1e-8, alg=:simultaneous, trscheme=(; alg=:fixedspace))
-gradient_alg = (; tol=1e-6, maxiter=10, alg=:eigsolver, iterscheme=:diffgauge)
-optimizer_alg = (; tol=1e-4, alg=:lbfgs, maxiter=150, ls_maxiter=2, ls_maxfg=2);
+boundary_alg = (; tol = 1.0e-8, alg = :simultaneous, trscheme = (; alg = :fixedspace))
+gradient_alg = (; tol = 1.0e-6, maxiter = 10, alg = :eigsolver, iterscheme = :diffgauge)
+optimizer_alg = (; tol = 1.0e-4, alg = :lbfgs, maxiter = 150, ls_maxiter = 2, ls_maxfg = 2);
 
 md"""
 !!! note
@@ -118,7 +118,7 @@ And at last, we optimize (which might take a bit):
 """
 
 peps, env, E, info = fixedpoint(
-    H, peps₀, env₀; boundary_alg, gradient_alg, optimizer_alg, verbosity=3
+    H, peps₀, env₀; boundary_alg, gradient_alg, optimizer_alg, verbosity = 3
 )
 @show E;
 

examples/boundary_mps/main.jl

@@ -128,7 +128,7 @@ boundary MPS fixed point, we call [`leading_boundary`](@ref) using the
 [`MPSKit.VUMPS`](@extref) algorithm:
 """
 
-mps, env, ϵ = leading_boundary(mps₀, T, VUMPS(; tol=1e-6, verbosity=2));
+mps, env, ϵ = leading_boundary(mps₀, T, VUMPS(; tol = 1.0e-6, verbosity = 2));
 
 md"""
 The norm of the state per unit cell is then given by the expectation value
@@ -142,7 +142,7 @@ This can be compared to the result obtained using CTMRG, where we see that the r
 match:
 """
 
-env_ctmrg, = leading_boundary(CTMRGEnv(ψ, ComplexSpace(20)), ψ; tol=1e-6, verbosity=2)
+env_ctmrg, = leading_boundary(CTMRGEnv(ψ, ComplexSpace(20)), ψ; tol = 1.0e-6, verbosity = 2)
 norm_ctmrg = abs(norm(ψ, env_ctmrg))
 @show abs(norm_vumps - norm_ctmrg) / norm_vumps;
 
@@ -162,19 +162,19 @@ argument and then define the corresponding transfer operator, where we again spe
 direction which will be facing north:
 """
 
-ψ_2x2 = InfinitePEPS(rand, ComplexF64, ComplexSpace(2), ComplexSpace(2); unitcell=(2, 2))
+ψ_2x2 = InfinitePEPS(rand, ComplexF64, ComplexSpace(2), ComplexSpace(2); unitcell = (2, 2))
 T_2x2 = PEPSKit.MultilineTransferPEPS(ψ_2x2, dir);
 
 md"""
 Now, the procedure is the same as before: We compute the norm once using VUMPS, once using CTMRG and then compare.
 """
 
 mps₀_2x2 = initialize_mps(T_2x2, fill(ComplexSpace(20), 2, 2))
-mps_2x2, = leading_boundary(mps₀_2x2, T_2x2, VUMPS(; tol=1e-6, verbosity=2))
+mps_2x2, = leading_boundary(mps₀_2x2, T_2x2, VUMPS(; tol = 1.0e-6, verbosity = 2))
 norm_2x2_vumps = abs(prod(expectation_value(mps_2x2, T_2x2)))
 
 env_ctmrg_2x2, = leading_boundary(
-    CTMRGEnv(ψ_2x2, ComplexSpace(20)), ψ_2x2; tol=1e-6, verbosity=2
+    CTMRGEnv(ψ_2x2, ComplexSpace(20)), ψ_2x2; tol = 1.0e-6, verbosity = 2
 )
 norm_2x2_ctmrg = abs(norm(ψ_2x2, env_ctmrg_2x2))
 
@@ -196,7 +196,7 @@ T | \psi \rangle$.
 The classical Ising PEPO is defined as follows:
 """
 
-function ising_pepo(β; unitcell=(1, 1, 1))
+function ising_pepo(β; unitcell = (1, 1, 1))
     t = ComplexF64[exp(β) exp(-β); exp(-β) exp(β)]
     q = sqrt(t)
 
@@ -223,7 +223,7 @@ As before, we converge the boundary MPS using VUMPS and then compute the expecta
 """
 
 mps₀_pepo = initialize_mps(transfer_pepo, [ComplexSpace(20)])
-mps_pepo, = leading_boundary(mps₀_pepo, transfer_pepo, VUMPS(; tol=1e-6, verbosity=2))
+mps_pepo, = leading_boundary(mps₀_pepo, transfer_pepo, VUMPS(; tol = 1.0e-6, verbosity = 2))
 norm_pepo = abs(prod(expectation_value(mps_pepo, transfer_pepo)));
 @show norm_pepo;
 

examples/fermi_hubbard/main.jl

@@ -70,9 +70,9 @@ Again, the procedure of ground state optimization is very similar to before. Fir
 define all algorithmic parameters:
 """
 
-boundary_alg = (; tol=1e-8, alg=:simultaneous, trscheme=(; alg=:fixedspace))
-gradient_alg = (; tol=1e-6, alg=:eigsolver, maxiter=10, iterscheme=:diffgauge)
-optimizer_alg = (; tol=1e-4, alg=:lbfgs, maxiter=80, ls_maxiter=3, ls_maxfg=3)
+boundary_alg = (; tol = 1.0e-8, alg = :simultaneous, trscheme = (; alg = :fixedspace))
+gradient_alg = (; tol = 1.0e-6, alg = :eigsolver, maxiter = 10, iterscheme = :diffgauge)
+optimizer_alg = (; tol = 1.0e-4, alg = :lbfgs, maxiter = 80, ls_maxiter = 3, ls_maxfg = 3)
 
 md"""
 Second, we initialize a PEPS state and environment (which we converge) constructed from
@@ -89,7 +89,7 @@ And third, we start the ground state search (this does take quite long):
 """
 
 peps, env, E, info = fixedpoint(
-    H, peps₀, env₀; boundary_alg, gradient_alg, optimizer_alg, verbosity=3
+    H, peps₀, env₀; boundary_alg, gradient_alg, optimizer_alg, verbosity = 3
 )
 @show E;
 

examples/heisenberg/main.jl

@@ -37,7 +37,7 @@ the `heisenberg_XYZ` method from [MPSKitModels](https://quantumkithub.github.io/
 which is redefined for the `InfiniteSquare` and reexported in PEPSKit:
 """
 
-H = heisenberg_XYZ(InfiniteSquare(); Jx=-1, Jy=1, Jz=-1)
+H = heisenberg_XYZ(InfiniteSquare(); Jx = -1, Jy = 1, Jz = -1)
 
 md"""
 ## Setting up the algorithms and initial guesses
@@ -63,7 +63,7 @@ arguments. To see a description of all arguments, see the docstring of
 specific tolerance and during the CTMRG run keep all index dimensions fixed:
 """
 
-boundary_alg = (; tol=1e-10, trscheme=(; alg=:fixedspace));
+boundary_alg = (; tol = 1.0e-10, trscheme = (; alg = :fixedspace));
 
 md"""
 Let us also configure the optimizer algorithm. We are going to optimize the PEPS using the
@@ -72,7 +72,7 @@ the convergence tolerance (for the gradient norm) as well as the maximal number
 and the BFGS memory size (which is used to approximate the Hessian):
 """
 
-optimizer_alg = (; alg=:lbfgs, tol=1e-4, maxiter=100, lbfgs_memory=16);
+optimizer_alg = (; alg = :lbfgs, tol = 1.0e-4, maxiter = 100, lbfgs_memory = 16);
 
 md"""
 Additionally, during optimization, we want to reuse the previous CTMRG environment to

examples/heisenberg_su/main.jl

@@ -34,7 +34,7 @@ no internal symmetries (`symm = Trivial`) or use the global $U(1)$ symmetry
 
 symm = Trivial ## ∈ {Trivial, U1Irrep}
 Nr, Nc = 2, 2
-H = real(heisenberg_XYZ(ComplexF64, symm, InfiniteSquare(Nr, Nc); Jx=1, Jy=1, Jz=1));
+H = real(heisenberg_XYZ(ComplexF64, symm, InfiniteSquare(Nr, Nc); Jx = 1, Jy = 1, Jz = 1));
 
 md"""
 ## Simple updating
@@ -50,14 +50,14 @@ if symm == Trivial
     bond_space = ℂ^Dbond
     env_space = ℂ^χenv
 elseif symm == U1Irrep
-    physical_space = ℂ[U1Irrep](1//2 => 1, -1//2 => 1)
-    bond_space = ℂ[U1Irrep](0 => Dbond ÷ 2, 1//2 => Dbond ÷ 4, -1//2 => Dbond ÷ 4)
-    env_space = ℂ[U1Irrep](0 => χenv ÷ 2, 1//2 => χenv ÷ 4, -1//2 => χenv ÷ 4)
+    physical_space = ℂ[U1Irrep](1 // 2 => 1, -1 // 2 => 1)
+    bond_space = ℂ[U1Irrep](0 => Dbond ÷ 2, 1 // 2 => Dbond ÷ 4, -1 // 2 => Dbond ÷ 4)
+    env_space = ℂ[U1Irrep](0 => χenv ÷ 2, 1 // 2 => χenv ÷ 4, -1 // 2 => χenv ÷ 4)
 else
     error("not implemented")
 end
 
-wpeps = InfiniteWeightPEPS(rand, Float64, physical_space, bond_space; unitcell=(Nr, Nc));
+wpeps = InfiniteWeightPEPS(rand, Float64, physical_space, bond_space; unitcell = (Nr, Nc));
 
 md"""
 Next, we can start the `SimpleUpdate` routine, successively decreasing the time intervals
@@ -66,14 +66,14 @@ truncation schemes, which we use here to set a maximal bond dimension and at the
 fix a truncation error (if that can be reached by remaining below `Dbond`):
 """
 
-dts = [1e-2, 1e-3, 4e-4]
-tols = [1e-6, 1e-8, 1e-8]
+dts = [1.0e-2, 1.0e-3, 4.0e-4]
+tols = [1.0e-6, 1.0e-8, 1.0e-8]
 maxiter = 10000
-trscheme_peps = truncerr(1e-10) & truncdim(Dbond)
+trscheme_peps = truncerr(1.0e-10) & truncdim(Dbond)
 
 for (dt, tol) in zip(dts, tols)
     alg = SimpleUpdate(dt, tol, maxiter, trscheme_peps)
-    result = simpleupdate(wpeps, H, alg; bipartite=true)
+    result = simpleupdate(wpeps, H, alg; bipartite = true)
     global wpeps = result[1]
 end
 
@@ -86,14 +86,14 @@ on the evolved PEPS. Let's do so:
 
 peps = InfinitePEPS(wpeps) ## absorb the weights
 env₀ = CTMRGEnv(rand, Float64, peps, env_space)
-trscheme_env = truncerr(1e-10) & truncdim(χenv)
+trscheme_env = truncerr(1.0e-10) & truncdim(χenv)
 env, = leading_boundary(
     env₀,
     peps;
-    alg=:sequential,
-    projector_alg=:fullinfinite,
-    tol=1e-10,
-    trscheme=trscheme_env,
+    alg = :sequential,
+    projector_alg = :fullinfinite,
+    tol = 1.0e-10,
+    trscheme = trscheme_env,
 );
 
 md"""