WIP rmf

Project.toml

@@ -7,12 +7,13 @@ version = "0.2.3"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 LabelledGraphs = "605abd48-4d17-4660-b914-d4df33194460"
 LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MKL = "33e6dc65-8f57-5167-99aa-e5a354878fb2"
 MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
-NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 TensorCast = "02d47bb6-7ce6-556a-be16-bb1710789e2b"
 
@@ -23,7 +24,7 @@ LabelledGraphs = "0.3.2"
 LightGraphs = "1.3"
 MKL = "0.4.2"
 MetaGraphs = "0.6"
-julia = "1.5, 1.6, 1.7"
+julia = "1.5, 1.6, 1.7, 1.8"
 
 [extras]
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"

examples/temp.jl

@@ -0,0 +1,116 @@
+using HDF5
+using LightGraphs
+using LabelledGraphs
+using MetaGraphs
+using SpinGlassNetworks
+
+
+function load_openGM(fname::String, Nx::Integer, Ny::Integer)
+    file = h5open(fname, "r")
+
+    file_keys = collect(keys(read(file)))
+    data = read(file[file_keys[1]])
+    H = collect(Int64, data["header"])
+    F = Array{Int64}(data["factors"])
+    J = Array{Int64}(data["function-id-16000"]["indices"])
+    V = Array{Real}(data["function-id-16000"]["values"])
+    N = Array{Int64}(data["numbers-of-states"])
+
+    F = reverse(F)
+    factors = Dict()
+
+    while length(F) > 0
+        f1 = pop!(F)
+        z1 = pop!(F)
+        nn = pop!(F)
+        n = []
+
+        for _ in 1:nn
+            tt = pop!(F)
+            ny, nx = divrem(tt, Nx)
+            push!(n, ny, nx)
+        end
+
+        if length(n) == 4
+            if abs(n[1] - n[3]) + abs(n[2] - n[4]) != 1
+                throw(Exception("Not nearest neighbour"))
+            end
+        end
+
+        if length(n) == 2
+            if (n[1] >= Ny) || (n[2] >= Nx)
+                throw(Exception("Wrong size"))
+            end
+        end
+
+        factors[tuple(n...)] = f1
+
+        if z1 != 0
+            throw(Exception("Something wrong with the expected convention."))
+        end
+    end
+
+    J = reverse(J)
+    functions = Dict()
+    ii = -1
+    lower = 0
+
+    while length(J) > 0
+        ii += 1
+        nn = pop!(J)
+        n = []
+
+        for _ in 1:nn
+            push!(n, pop!(J))
+        end
+
+        upper = lower + prod(n)
+        functions[ii] = reshape(V[lower + 1:upper], reverse(n)...)'
+
+        lower = upper
+    end
+
+    result = Dict("fun" => functions, "fac" => factors, "N" => reshape(N, (Ny, Nx)), "Nx" => Nx, "Ny" => Ny)
+    result
+end
+
+function energy_rmf()
+
+end
+
+function clustered_hamiltonian(fname::String, Nx::Integer = 240, Ny::Integer = 320)
+    loaded_rmf = load_openGM(fname, Nx, Ny)
+    functions = loaded_rmf["fun"]
+    factors = loaded_rmf["fac"]
+    N = loaded_rmf["N"]
+    println(size(N))
+    node_factors = Dict()
+    edge_factors = Dict()
+
+    for index in keys(factors)
+        if length(index) == 4
+            push!(edge_factors, index=>factors[index])
+        else
+            push!(node_factors, index=>factors[index])
+        end
+    end
+    println(length(node_factors))
+    println((0,0) in collect(keys(node_factors)))
+    g = grid((Nx,Ny))
+    clusters = super_square_lattice((Nx, Ny, 1))
+    cl_h = LabelledGraph{MetaDiGraph}(sort(collect(values(clusters))))
+    for v ∈ cl_h.labels
+        x, y = v
+        sp = Spectrum([node_factors[(y-1, x-1)]], Array([collect(1:N[y, x])]), Vector{Int}([]))
+        set_props!(cl_h, v, Dict(:cluster => v, :spectrum => sp))
+    end
+
+    cl_h
+end
+
+
+x, y = 240, 320
+filename = "/home/tsmierzchalski/.julia/dev/SpinGlassNetworks/examples/penguin-small.h5"
+
+
+cf = clustered_hamiltonian(filename, x, y)

src/SpinGlassNetworks.jl

@@ -7,6 +7,7 @@ module SpinGlassNetworks
     using LinearAlgebra, MKL
     using Base.Cartesian
     using SparseArrays
+    using HDF5
     using CUDA, CUDA.CUSPARSE
     import Base.Prehashed