Formatter (#75)

Author: lkdvos

benchmark/benchtools.jl

@@ -1,82 +1,82 @@
-function distributeZ2(D; p = 0.5)
-    D0 = ceil(Int, p*D)
+function distributeZ2(D; p=0.5)
+    D0 = ceil(Int, p * D)
     D1 = D - D0
     return [(0, D0), (1, D1)]
 end
 
-distributeU1(D; p = 0.25) = distributeU1_poisson(D; p = p)
+distributeU1(D; p=0.25) = distributeU1_poisson(D; p=p)
 
-function distributeU1_exponential(D; p = 0.25)
-    λ = (1-p)/(1+p)
-    D0 = ceil(Int, p*D)
-    if isodd(D-D0)
-        D0 = D0 == 1 ? 2 : D0-1
+function distributeU1_exponential(D; p=0.25)
+    λ = (1 - p) / (1 + p)
+    D0 = ceil(Int, p * D)
+    if isodd(D - D0)
+        D0 = D0 == 1 ? 2 : D0 - 1
     end
     sectors = [(0, D0)]
     Drem = D - D0
     n = 1
     while Drem > 0
         pn = p * λ^n
-        Dn = ceil(Int, pn*D)
+        Dn = ceil(Int, pn * D)
         sectors = push!(sectors, (n, Dn), (-n, Dn))
-        Drem -= 2*Dn
+        Drem -= 2 * Dn
         n += 1
     end
-    return sort!(sectors, by = first)
+    return sort!(sectors; by=first)
 end
 
-function distributeU1_poisson(D; p = 0.25)
-    λ = log((1/p+1)/2)
-    D0 = ceil(Int, p*D)
-    if isodd(D-D0)
-        D0 = D0 == 1 ? 2 : D0-1
+function distributeU1_poisson(D; p=0.25)
+    λ = log((1 / p + 1) / 2)
+    D0 = ceil(Int, p * D)
+    if isodd(D - D0)
+        D0 = D0 == 1 ? 2 : D0 - 1
     end
     sectors = [(0, D0)]
     Drem = D - D0
     n = 1
     while Drem > 0
         pn = p * λ^n / factorial(n)
-        Dn = ceil(Int, pn*D)
+        Dn = ceil(Int, pn * D)
         sectors = push!(sectors, (n, Dn), (-n, Dn))
-        Drem -= 2*Dn
+        Drem -= 2 * Dn
         n += 1
     end
-    return sort!(sectors, by = first)
+    return sort!(sectors; by=first)
 end
 
 module Timers
-    struct Timer{F,D<:Tuple}
-        f::F
-        argsref::Base.RefValue{D}
-        Timer(f, args...) = new{typeof(f), typeof(args)}(f, Ref(args))
-    end
+struct Timer{F,D<:Tuple}
+    f::F
+    argsref::Base.RefValue{D}
+    Timer(f, args...) = new{typeof(f),typeof(args)}(f, Ref(args))
+end
 
-    @noinline donothing(arg) = arg
-    function (t::Timer)(; inner = 1, outer = 1)
-        args = t.argsref[]
-        f = t.f
-        f(args...) # run once to compile
-        times = zeros(Float64, (outer,))
-        gctimes = zeros(Float64, (outer,))
-        @inbounds for i = 1:outer
-            if inner == 1
-                gcstart = Base.gc_num()
-                start = Base.time_ns()
+@noinline donothing(arg) = arg
+function (t::Timer)(; inner=1, outer=1)
+    args = t.argsref[]
+    f = t.f
+    f(args...) # run once to compile
+    times = zeros(Float64, (outer,))
+    gctimes = zeros(Float64, (outer,))
+    @inbounds for i in 1:outer
+        if inner == 1
+            gcstart = Base.gc_num()
+            start = Base.time_ns()
+            donothing(f(args...))
+            stop = Base.time_ns()
+            gcstop = Base.gc_num()
+        else
+            gcstart = Base.gc_num()
+            start = Base.time_ns()
+            for _ in 1:inner
                 donothing(f(args...))
-                stop = Base.time_ns()
-                gcstop = Base.gc_num()
-            else
-                gcstart = Base.gc_num()
-                start = Base.time_ns()
-                for _ = 1:inner
-                    donothing(f(args...))
-                end
-                stop = Base.time_ns()
-                gcstop = Base.gc_num()
             end
-            times[i] = (stop - start)/(1e9)/inner
-            gctimes[i] = Base.GC_Diff(gcstop, gcstart).total_time/1e9/inner
+            stop = Base.time_ns()
+            gcstop = Base.gc_num()
         end
-        return times, gctimes
+        times[i] = (stop - start) / (1e9) / inner
+        gctimes[i] = Base.GC_Diff(gcstop, gcstart).total_time / 1e9 / inner
     end
+    return times, gctimes
+end
 end

benchmark/itensors_timers.jl

@@ -1,85 +1,86 @@
 include("benchtools.jl")
 module ITensorsTimers
-    using ITensors
-    import ..Timers
+using ITensors
+using ..Timers: Timers
 
-    function add_indexlabels_as_tags(tensor, labels)
-        new_inds = map(x -> settags(x[2], "l$(x[1])"), zip(labels, tensor.inds))
-        return ITensor(tensor.store, new_inds)
-    end
+function add_indexlabels_as_tags(tensor, labels)
+    new_inds = map(x -> settags(x[2], "l$(x[1])"), zip(labels, tensor.inds))
+    return ITensor(tensor.store, new_inds)
+end
 
-    function mpo_timer(T = Float64; Vmpo, Vmps, Vphys)
-        A = randomITensor(T, Vmps, Vphys, dag(Vmps))
-        M = randomITensor(T, Vmpo, Vphys, dag(Vphys), dag(Vmpo))
-        FL = randomITensor(T, Vmps, dag(Vmpo), dag(Vmps))
-        FR = randomITensor(T, Vmps, Vmpo, dag(Vmps))
+function mpo_timer(T=Float64; Vmpo, Vmps, Vphys)
+    A = randomITensor(T, Vmps, Vphys, dag(Vmps))
+    M = randomITensor(T, Vmpo, Vphys, dag(Vphys), dag(Vmpo))
+    FL = randomITensor(T, Vmps, dag(Vmpo), dag(Vmps))
+    FR = randomITensor(T, Vmps, Vmpo, dag(Vmps))
 
-        FL1 = add_indexlabels_as_tags(FL, (4, 2, 1))
-        A1 = add_indexlabels_as_tags(A, (1, 3, 6))
-        M1 = add_indexlabels_as_tags(M, (2, 5, 3, 7))
-        A2 = add_indexlabels_as_tags(A, (4, 5, 8))
-        FR1 = add_indexlabels_as_tags(FR, (6, 7, 8))
+    FL1 = add_indexlabels_as_tags(FL, (4, 2, 1))
+    A1 = add_indexlabels_as_tags(A, (1, 3, 6))
+    M1 = add_indexlabels_as_tags(M, (2, 5, 3, 7))
+    A2 = add_indexlabels_as_tags(A, (4, 5, 8))
+    FR1 = add_indexlabels_as_tags(FR, (6, 7, 8))
 
-        return Timers.Timer(A1, A2, M1, FL1, FR1) do A1, A2, M1, FL1, FR1
-            C = ((((FL1*A1)*M1)*dag(A2))*FR1)
-            return C[]
-        end
+    return Timers.Timer(A1, A2, M1, FL1, FR1) do A1, A2, M1, FL1, FR1
+        C = ((((FL1 * A1) * M1) * dag(A2)) * FR1)
+        return C[]
     end
+end
 
-    function pepo_timer(T = Float64; Vpepo, Vpeps, Venv, Vphys)
-        Vpeps′ = dag(Vpeps)
-        Vpepo′ = dag(Vpepo)
-        Venv′ = dag(Venv)
-        Vphys′ = dag(Vphys)
+function pepo_timer(T=Float64; Vpepo, Vpeps, Venv, Vphys)
+    Vpeps′ = dag(Vpeps)
+    Vpepo′ = dag(Vpepo)
+    Venv′ = dag(Venv)
+    Vphys′ = dag(Vphys)
 
-        A = randomITensor(T, Vpeps, Vpeps, Vphys, Vpeps′, Vpeps′)
-        P = randomITensor(T, Vpepo, Vpepo, Vphys, Vphys′, Vpepo′, Vpepo′)
-        FL = randomITensor(T, Venv, Vpeps, Vpepo′, Vpeps′, Venv′)
-        FD = randomITensor(T, Venv, Vpeps, Vpepo′, Vpeps′, Venv′)
-        FR = randomITensor(T, Venv, Vpeps, Vpepo, Vpeps′, Venv′)
-        FU = randomITensor(T, Venv, Vpeps, Vpepo, Vpeps′, Venv′)
+    A = randomITensor(T, Vpeps, Vpeps, Vphys, Vpeps′, Vpeps′)
+    P = randomITensor(T, Vpepo, Vpepo, Vphys, Vphys′, Vpepo′, Vpepo′)
+    FL = randomITensor(T, Venv, Vpeps, Vpepo′, Vpeps′, Venv′)
+    FD = randomITensor(T, Venv, Vpeps, Vpepo′, Vpeps′, Venv′)
+    FR = randomITensor(T, Venv, Vpeps, Vpepo, Vpeps′, Venv′)
+    FU = randomITensor(T, Venv, Vpeps, Vpepo, Vpeps′, Venv′)
 
-        FL1 = add_indexlabels_as_tags(FL, (18, 7, 4, 2, 1))
-        FU1 = add_indexlabels_as_tags(FU, (1, 3, 6, 9, 10))
-        A1 = add_indexlabels_as_tags(A, (2, 17, 5, 3, 11))
-        P1 = add_indexlabels_as_tags(P, (4, 16, 8, 5, 6, 12))
-        A2 = add_indexlabels_as_tags(A, (7, 15, 8, 9, 13))
-        FR1 = add_indexlabels_as_tags(FR, (10, 11, 12, 13, 14))
-        FD1 = add_indexlabels_as_tags(FD, (14, 15, 16, 17, 18))
+    FL1 = add_indexlabels_as_tags(FL, (18, 7, 4, 2, 1))
+    FU1 = add_indexlabels_as_tags(FU, (1, 3, 6, 9, 10))
+    A1 = add_indexlabels_as_tags(A, (2, 17, 5, 3, 11))
+    P1 = add_indexlabels_as_tags(P, (4, 16, 8, 5, 6, 12))
+    A2 = add_indexlabels_as_tags(A, (7, 15, 8, 9, 13))
+    FR1 = add_indexlabels_as_tags(FR, (10, 11, 12, 13, 14))
+    FD1 = add_indexlabels_as_tags(FD, (14, 15, 16, 17, 18))
 
-        return Timers.Timer(A1, A2, P1, FL1, FD1, FR1, FU1) do A1, A2, P1, FL1, FD1, FR1, FU1
-            C = (((((FL1*FU1)*A1)*P1)*dag(A2))*FR1)*FD1
-            return C[]
-        end
+    return Timers.Timer(A1, A2, P1, FL1, FD1, FR1, FU1) do A1, A2, P1, FL1, FD1, FR1, FU1
+        C = (((((FL1 * FU1) * A1) * P1) * dag(A2)) * FR1) * FD1
+        return C[]
     end
+end
 
-    function mera_timer(T = Float64; Vmera)
-        Vmera′ = dag(Vmera);
+function mera_timer(T=Float64; Vmera)
+    Vmera′ = dag(Vmera)
 
-        u = randomITensor(T, Vmera, Vmera, Vmera′, Vmera′)
-        w = randomITensor(T, Vmera, Vmera, Vmera′)
-        ρ = randomITensor(T, Vmera, Vmera, Vmera, Vmera′, Vmera′, Vmera′)
-        h = randomITensor(T, Vmera, Vmera, Vmera, Vmera′, Vmera′, Vmera′)
+    u = randomITensor(T, Vmera, Vmera, Vmera′, Vmera′)
+    w = randomITensor(T, Vmera, Vmera, Vmera′)
+    ρ = randomITensor(T, Vmera, Vmera, Vmera, Vmera′, Vmera′, Vmera′)
+    h = randomITensor(T, Vmera, Vmera, Vmera, Vmera′, Vmera′, Vmera′)
 
-        h1 = add_indexlabels_as_tags(h, (9, 3, 4, 5, 1, 2))
-        u1 = add_indexlabels_as_tags(u, (1, 2, 7, 12))
-        u2 = add_indexlabels_as_tags(u, (3, 4, 11, 13))
-        u3 = add_indexlabels_as_tags(u, (8, 5, 15, 6))
-        w1 = add_indexlabels_as_tags(w, (6, 7, 19))
-        u4 = add_indexlabels_as_tags(u, (8, 9, 17, 10))
-        w2 = add_indexlabels_as_tags(w, (10, 11, 22))
-        w3 = add_indexlabels_as_tags(w, (12, 14, 20))
-        w4 = add_indexlabels_as_tags(w, (13, 14, 23))
-        ρ1 = add_indexlabels_as_tags(ρ, (18, 19, 20, 21, 22, 23))
-        w5 = add_indexlabels_as_tags(w, (16, 15, 18))
-        w6 = add_indexlabels_as_tags(w, (16, 17, 21))
+    h1 = add_indexlabels_as_tags(h, (9, 3, 4, 5, 1, 2))
+    u1 = add_indexlabels_as_tags(u, (1, 2, 7, 12))
+    u2 = add_indexlabels_as_tags(u, (3, 4, 11, 13))
+    u3 = add_indexlabels_as_tags(u, (8, 5, 15, 6))
+    w1 = add_indexlabels_as_tags(w, (6, 7, 19))
+    u4 = add_indexlabels_as_tags(u, (8, 9, 17, 10))
+    w2 = add_indexlabels_as_tags(w, (10, 11, 22))
+    w3 = add_indexlabels_as_tags(w, (12, 14, 20))
+    w4 = add_indexlabels_as_tags(w, (13, 14, 23))
+    ρ1 = add_indexlabels_as_tags(ρ, (18, 19, 20, 21, 22, 23))
+    w5 = add_indexlabels_as_tags(w, (16, 15, 18))
+    w6 = add_indexlabels_as_tags(w, (16, 17, 21))
 
-        return Timers.Timer(u1, u2, u3, u4, w1, w2, w3, w4, w5, w6, ρ1, h1) do u1, u2, u3, u4, w1, w2, w3, w4, w5, w6, ρ1, h1
-            C = (((((( (h1*u1) * dag(u2) ) *
-                                (u3*w1) ) *
-                                (dag(u4)*dag(w2)) ) *
-                               ( (w3 * dag(w4)) * ρ1)) * w5) * dag(w6))
-            return C
-        end
+    return Timers.Timer(u1, u2, u3, u4, w1, w2, w3, w4, w5, w6, ρ1,
+                        h1) do u1, u2, u3, u4, w1, w2, w3, w4, w5, w6, ρ1, h1
+        C = (((((((h1 * u1) * dag(u2)) *
+                 (u3 * w1)) *
+                (dag(u4) * dag(w2))) *
+               ((w3 * dag(w4)) * ρ1)) * w5) * dag(w6))
+        return C
     end
 end
+end

benchmark/plotmera.jl

@@ -14,17 +14,18 @@ meraplots = Matrix{Any}(undef, (numplots, 3))
 data = ["_mera_triv.txt", "_mera_z2.txt", "_mera_u1.txt"]
 dims = [meradims_triv, meradims_z2, meradims_u1]
 name = ["Trivial", "Z2 symmetry", "U1 symmetry"]
-for j = 1:3
+for j in 1:3
     s = data[j]
     data_tensorkit = try
-        readdlm("results/tensorkit"*s)
+        readdlm("results/tensorkit" * s)
     catch
         nothing
     end
     ymin = minimum(data_tensorkit)
     ymax = maximum(data_tensorkit)
     data_itensors = try
-        readdlm("results/itensors"*s) - readdlm("results/itensors"*s[1:end-4]*"_gc.txt")
+        readdlm("results/itensors" * s) -
+        readdlm("results/itensors" * s[1:(end - 4)] * "_gc.txt")
     catch
         nothing
     end
@@ -33,7 +34,7 @@ for j = 1:3
         ymax = max(ymax, maximum(data_itensors))
     end
     data_tenpy = try
-        readdlm("results/tenpy"*s)
+        readdlm("results/tenpy" * s)
     catch
         nothing
     end
@@ -42,7 +43,7 @@ for j = 1:3
         ymax = max(ymax, maximum(data_tenpy))
     end
     data_tensornetwork = try
-        readdlm("results/tensornetwork"*s)
+        readdlm("results/tensornetwork" * s)
     catch
         nothing
     end
@@ -53,31 +54,34 @@ for j = 1:3
     ymin = 10^(floor(log10(ymin)))
     ymax = 10^(ceil(log10(ymax)))
 
-    for i = 1:numplots
+    for i in 1:numplots
         Dmera = dims[j][1][i]
 
-        p = boxplot(data_tensorkit[:,i], label="TensorKit.jl", markersize = M)
+        p = boxplot(data_tensorkit[:, i]; label="TensorKit.jl", markersize=M)
         !isnothing(data_itensors) &&
-            boxplot!(p, data_itensors[:,i], label="ITensors.jl", markersize = M)
+            boxplot!(p, data_itensors[:, i]; label="ITensors.jl", markersize=M)
         !isnothing(data_tenpy) &&
-            boxplot!(p, data_tenpy[:,i], label="Tenpy", markersize = M)
+            boxplot!(p, data_tenpy[:, i]; label="Tenpy", markersize=M)
         !isnothing(data_tensornetwork) &&
-            boxplot!(p, data_tensornetwork[:,i], label="TensorNetwork", markersize = M)
-        plot!(p, yscale = :log10, yminorticks = true, xticks = [], xgrid = false, xshowaxis = false, ylim = (ymin, ymax))
+            boxplot!(p, data_tensornetwork[:, i]; label="TensorNetwork", markersize=M)
+        plot!(p; yscale=:log10, yminorticks=true, xticks=[], xgrid=false, xshowaxis=false,
+              ylim=(ymin, ymax))
         if i == 1
-            plot!(p, yguide = name[j], yguide_position = :left, yguidefontsize = 10, ytickfontsize = 8, left_margin = 6mm)
-            plot!(p, title = "χ = $Dmera", titlefontsize = 10)
+            plot!(p; yguide=name[j], yguide_position=:left, yguidefontsize=10,
+                  ytickfontsize=8, left_margin=6mm)
+            plot!(p; title="χ = $Dmera", titlefontsize=10)
         else
-            plot!(p, title = "$Dmera", titlefontsize = 10)
-            plot!(p, left_margin = -5mm, yformatter = _->"", yshowaxis = false)
+            plot!(p; title="$Dmera", titlefontsize=10)
+            plot!(p; left_margin=-5mm, yformatter=_ -> "", yshowaxis=false)
         end
         if i == numplots && j == 3
-            plot!(p, legendfontsize = 10, legend = :bottomright)
+            plot!(p; legendfontsize=10, legend=:bottomright)
         else
-            plot!(p, legend = false)
+            plot!(p; legend=false)
         end
         meraplots[i, j] = p
     end
 end
-plotgrid = plot(meraplots..., layout=grid(3,numplots), link = :y, thickness_scaling = 1, size = (1500, 800))
+plotgrid = plot(meraplots...; layout=grid(3, numplots), link=:y, thickness_scaling=1,
+                size=(1500, 800))
 savefig(plotgrid, "meraresults.pdf")