Use random_itensor, random_mps, etc. (#183)

Author: mtfishman

Project.toml

@@ -1,7 +1,7 @@
 name = "ITensorNetworks"
 uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
 authors = ["Matthew Fishman <[email protected]>, Joseph Tindall <[email protected]> and contributors"]
-version = "0.11.8"
+version = "0.11.9"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -58,8 +58,8 @@ DocStringExtensions = "0.9"
 EinExprs = "0.6.4"
 Graphs = "1.8"
 GraphsFlows = "0.1.1"
-ITensorMPS = "0.1"
-ITensors = "0.4, 0.5, 0.6"
+ITensorMPS = "0.2.2"
+ITensors = "0.6.8"
 IsApprox = "0.1"
 IterTools = "1.4.0"
 KrylovKit = "0.6, 0.7"

ext/ITensorNetworksOMEinsumContractionOrdersExt/ITensorNetworksOMEinsumContractionOrdersExt.jl

@@ -47,7 +47,7 @@ Returns a [`NestedEinsum`](@ref) instance.
 ```jldoctest
 julia> using ITensors, ITensorContractionOrders
 julia> i, j, k, l = Index(4), Index(5), Index(6), Index(7);
-julia> x, y, z = randomITensor(i, j), randomITensor(j, k), randomITensor(k, l);
+julia> x, y, z = random_itensor(i, j), random_itensor(j, k), random_itensor(k, l);
 julia> net = optimize_contraction([x, y, z]; optimizer=TreeSA());
 ```
 """

src/lib/ModelNetworks/src/ModelNetworks.jl

@@ -1,7 +1,7 @@
 module ModelNetworks
 using Graphs: degree, dst, edges, src
 using ..ITensorNetworks: IndsNetwork, delta_network, insert_linkinds, itensor
-using ITensors: commoninds, diagITensor, inds, noprime
+using ITensors: commoninds, diag_itensor, inds, noprime
 using LinearAlgebra: Diagonal, eigen
 using NamedGraphs: NamedGraph
 
@@ -21,7 +21,7 @@ function ising_network(
   tn = delta_network(eltype, s)
   if (szverts != nothing)
     for v in szverts
-      tn[v] = diagITensor(eltype[1, -1], inds(tn[v]))
+      tn[v] = diag_itensor(eltype[1, -1], inds(tn[v]))
     end
   end
   for edge in edges(tn)

src/mpo_mps_compatibility.jl

@@ -9,9 +9,9 @@ function ITensorMPS.MPO(opsum_sum::Sum{<:OpSum}, s::IndsNetwork)
   return ITensorMPS.MPO(sum(Ops.terms(opsum_sum)), s)
 end
 
-function ITensorMPS.randomMPS(s::IndsNetwork, args...; kwargs...)
+function ITensorMPS.random_mps(s::IndsNetwork, args...; kwargs...)
   s_linear = [only(s[v]) for v in 1:nv(s)]
-  return ITensorMPS.randomMPS(s_linear, args...; kwargs...)
+  return ITensorMPS.random_mps(s_linear, args...; kwargs...)
 end
 
 function ITensorMPS.MPS(s::IndsNetwork, args...; kwargs...)

test/test_belief_propagation.jl

@@ -24,7 +24,7 @@ using ITensorNetworks:
   update,
   update_factor,
   update_message
-using ITensors: ITensors, ITensor, combiner, dag, inds, inner, op, prime, randomITensor
+using ITensors: ITensors, ITensor, combiner, dag, inds, inner, op, prime, random_itensor
 using ITensorNetworks.ModelNetworks: ModelNetworks
 using ITensors.NDTensors: array
 using LinearAlgebra: eigvals, tr
@@ -55,7 +55,7 @@ using Test: @test, @testset
 
   #Test updating the underlying tensornetwork in the cache
   v = first(vertices(ψψ))
-  new_tensor = randomITensor(inds(ψψ[v]))
+  new_tensor = random_itensor(inds(ψψ[v]))
   bpc_updated = update_factor(bpc, v, new_tensor)
   ψψ_updated = tensornetwork(bpc_updated)
   @test ψψ_updated[v] == new_tensor

test/test_binary_tree_partition.jl

@@ -3,7 +3,7 @@ using DataGraphs: DataGraph, underlying_graph, vertex_data
 using Graphs: add_vertex!, vertices
 # Trigger package extension.
 using GraphsFlows: GraphsFlows
-using ITensors: Index, ITensor, contract, noncommoninds, randomITensor
+using ITensors: Index, ITensor, contract, noncommoninds, random_itensor
 using ITensorMPS: MPS
 using ITensorNetworks:
   _DensityMartrixAlgGraph,
@@ -35,7 +35,7 @@ using Test: @test, @testset
   o = Index(2, "o")
   p = Index(2, "p")
 
-  T = randomITensor(i, j, k, l, m, n, o, p)
+  T = random_itensor(i, j, k, l, m, n, o, p)
   M = MPS(T, (i, j, k, l, m, n, o, p); cutoff=1e-5, maxdim=500)
   tn = ITensorNetwork(M[:])
   for out in [binary_tree_structure(tn), path_graph_structure(tn)]
@@ -75,7 +75,7 @@ end
 
 @testset "test partition with mincut_recursive_bisection alg of disconnected tn" begin
   inds = [Index(2, "$i") for i in 1:5]
-  tn = ITensorNetwork([randomITensor(i) for i in inds])
+  tn = ITensorNetwork([random_itensor(i) for i in inds])
   par = _partition(tn, binary_tree_structure(tn); alg="mincut_recursive_bisection")
   network = mapreduce(v -> collect(eachtensor(par[v])), vcat, vertices(par))
   @test isapprox(contract(tn), contract(network))
@@ -88,7 +88,7 @@ end
   l = Index(2, "l")
   m = Index(2, "m")
   for dtype in (Float64, Complex{Float64})
-    T = randomITensor(dtype, i, j, k, l, m)
+    T = random_itensor(dtype, i, j, k, l, m)
     M = MPS(T, (i, j, k, l, m); cutoff=1e-5, maxdim=5)
     network = M[:]
     out1 = contract(network...)
@@ -126,7 +126,7 @@ end
   k = Index(2, "k")
   l = Index(2, "l")
   m = Index(2, "m")
-  T = randomITensor(i, j, k, l, m)
+  T = random_itensor(i, j, k, l, m)
   M = MPS(T, (i, j, k, l, m); cutoff=1e-5, maxdim=5)
   tn = ITensorNetwork(M[:])
   out_tree = path_graph_structure(tn)

test/test_contract_deltas.jl

@@ -2,7 +2,7 @@
 using Graphs: dfs_tree, nv, vertices
 # Trigger package extension.
 using GraphsFlows: GraphsFlows
-using ITensors: Index, ITensor, delta, noncommoninds, randomITensor
+using ITensors: Index, ITensor, delta, noncommoninds, random_itensor
 using ITensorNetworks:
   IndsNetwork,
   ITensorNetwork,
@@ -21,7 +21,7 @@ using Test: @test, @testset
 
 @testset "test _contract_deltas with no deltas" begin
   i = Index(2, "i")
-  t = randomITensor(i)
+  t = random_itensor(i)
   tn = _contract_deltas(ITensorNetwork([t]))
   @test tn[1] == t
 end

test/test_forms.jl

@@ -20,7 +20,7 @@ using ITensorNetworks:
   tensornetwork,
   union_all_inds,
   update
-using ITensors: contract, dag, inds, prime, randomITensor
+using ITensors: contract, dag, inds, prime, random_itensor
 using LinearAlgebra: norm
 using Test: @test, @testset
 using Random: Random
@@ -52,7 +52,7 @@ using Random: Random
   @test isempty(flatten_siteinds(qf))
 
   v = (1, 1)
-  new_tensor = randomITensor(inds(ψket[v]))
+  new_tensor = random_itensor(inds(ψket[v]))
   qf_updated = update(qf, v, copy(new_tensor))
 
   @test tensornetwork(qf_updated)[bra_vertex(qf_updated, v)] ≈

test/test_gauging.jl

@@ -9,7 +9,7 @@ using ITensorNetworks:
   random_tensornetwork,
   siteinds,
   update
-using ITensors: diagITensor, inds, inner
+using ITensors: diag_itensor, inds, inner
 using ITensors.NDTensors: vector
 using LinearAlgebra: diag
 using NamedGraphs.NamedGraphGenerators: named_grid
@@ -43,7 +43,7 @@ using Test: @test, @testset
   #Test all message tensors are approximately diagonal even when we keep running BP
   bp_cache = update(bp_cache; maxiter=10)
   for m_e in values(messages(bp_cache))
-    @test diagITensor(vector(diag(only(m_e))), inds(only(m_e))) ≈ only(m_e) atol = 1e-8
+    @test diag_itensor(vector(diag(only(m_e))), inds(only(m_e))) ≈ only(m_e) atol = 1e-8
   end
 end
 end

test/test_itensornetwork.jl

@@ -30,7 +30,7 @@ using ITensors:
   itensor,
   onehot,
   order,
-  randomITensor,
+  random_itensor,
   scalartype,
   sim,
   uniqueinds