Merge remote-tracking branch 'modelB/main'

corr.jl

@@ -0,0 +1,34 @@
+cd(@__DIR__)
+
+using Plots
+using DelimitedFiles
+using LaTeXStrings
+
+
+function autocor_loc_2(x, beg, max, n=2)
+	C = zeros(Complex{Float64},max+1)
+	N = zeros(Int64,max+1)
+	Threads.@threads for tau in 0:max
+		for i in beg:length(x)-max
+			j = i + tau
+			@inbounds @fastmath  C[tau+1] = C[tau+1] +  (x[i]*conj(x[j]))^n
+			@inbounds @fastmath  N[tau+1] = N[tau+1] + 1
+		end
+	end
+	(collect(1:max+1),  C ./ N)
+end
+
+
+df_16=readdlm("output_16.dat",' ')
+df_8=readdlm("output_8.dat",' ')
+
+
+(t_8,c_8) = autocor_loc_2(df_8[:,7].+df_8[:,8].*1.0im, 1, 8, 1)
+
+
+plot(t_8/8^2,real(c_8)/real(c_8[1]),label=L"L=8",xlabel = L"t/L^2")
+
+
+
+
+savefig("c.pdf")

modelB.jl

@@ -14,9 +14,11 @@ const T = 1.0f0
 
 const Δt = 0.04f0/Γ
 const Rate = Float32(sqrt(2.0*Δt*Γ))
+ξ = Normal(0.0f0, 1.0f0)
+
 
 function hotstart(n)
-    rand(Normal(), n, n, n)
+    rand(ξ, n, n, n)
 end
 
 function ΔH(x, ϕ, q, m²)
@@ -32,15 +34,16 @@ function ΔH(x, ϕ, q, m²)
 end
 
 function step(m², ϕ, x1, x2)
-    q = Rate*rand(Normal())
+    q = Rate*rand(ξ)
 
     @inbounds ϕ1 = ϕ[x1[1], x1[2], x1[3]]
     @inbounds ϕ2 = ϕ[x2[1], x2[2], x2[3]]
 
     δH = ΔH(x1, ϕ, q, m²) + ΔH(x2, ϕ, -q, m²) + q^2
     P = min(1.0f0, exp(-δH))
     r = rand(Float32)
-    if (r < P)
+
+	if (r < P)
         @inbounds ϕ[x1[1], x1[2], x1[3]] += q
         @inbounds ϕ[x2[1], x2[2], x2[3]] -= q
     end
@@ -56,7 +59,7 @@ function sweep(m², ϕ)
                 x1 = transition[idx]
                 x1[idx[1]] += m%2
                 x1[idx[2]] += m<3
-                x2 = copy(x1)
+				x2 = copy(x1)
                 x2[n+1] += 1
 
                 step(m², ϕ, x1.%L.+1, x2.%L.+1)
@@ -81,18 +84,26 @@ m² = -2.285
 
 ϕ = hotstart(L)
 
-thermalize(m², ϕ, 100*L^2)
+thermalize(m², ϕ, 100*L^4)
+
+maxt = L^4*25
 
-maxt = L^2
+skip=20 
+
+ϕk = fft(ϕ)
 
 open("output_$L.dat","w") do io 
 	for i in 0:maxt
-        (M, ϕk) = op(ϕ, L)
-		Printf.@printf(io, "%i %f", 20i, M)
-        for kx in 1:L
-            Printf.@printf(io, " %f %f", real(ϕk[kx]), imag(ϕk[kx]))
-        end
-        Printf.@printf(io, "\n")
-		thermalize(m², ϕ, 20)
+		Mt = M(ϕ)
+
+		ϕk = fft(ϕ)
+
+		Printf.@printf(io, "%i %f", skip*i, Mt)
+		for kx in 1:L
+			Printf.@printf(io, " %f %f", real(ϕk[1,kx,1]), imag(ϕk[1,kx,1]))
+		end 
+
+		Printf.@printf(io,  "\n")
+		thermalize(m², ϕ, skip)
 	end
-end
+end