Fix tests

Author: mtfishman

README.md

@@ -36,7 +36,7 @@ and 3 edge(s):
 3 => 4
 
 with vertex data:
-4-element Dictionaries.Dictionary{Int64, Any}
+4-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=739|"1↔2"),)
  2 │ ((dim=2|id=739|"1↔2"), (dim=2|id=920|"2↔3"))
  3 │ ((dim=2|id=920|"2↔3"), (dim=2|id=761|"3↔4"))
@@ -88,7 +88,7 @@ and 4 edge(s):
 (1, 2) => (2, 2)
 
 with vertex data:
-4-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+4-element Dictionary{Tuple{Int64, Int64}, Any}
  (1, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=723|"1×1↔1×2"))
  (2, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=823|"2×1↔2×2"))
  (1, 2) │ ((dim=2|id=723|"1×1↔1×2"), (dim=2|id=712|"1×2↔2×2"))
@@ -118,7 +118,7 @@ and 1 edge(s):
 (1, 1) => (1, 2)
 
 with vertex data:
-2-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+2-element Dictionary{Tuple{Int64, Int64}, Any}
  (1, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=723|"1×1↔1×2"))
  (1, 2) │ ((dim=2|id=723|"1×1↔1×2"), (dim=2|id=712|"1×2↔2×2"))
 
@@ -132,7 +132,7 @@ and 1 edge(s):
 (2, 1) => (2, 2)
 
 with vertex data:
-2-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+2-element Dictionary{Tuple{Int64, Int64}, Any}
  (2, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=823|"2×1↔2×2"))
  (2, 2) │ ((dim=2|id=823|"2×1↔2×2"), (dim=2|id=712|"1×2↔2×2"))
 ```
@@ -155,13 +155,13 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Vector{Index}}
+3-element Dictionary{Int64, Vector{Index}}
  1 │ Index[(dim=2|id=598|"S=1/2,Site,n=1")]
  2 │ Index[(dim=2|id=457|"S=1/2,Site,n=2")]
  3 │ Index[(dim=2|id=683|"S=1/2,Site,n=3")]
 
 and edge data:
-0-element Dictionaries.Dictionary{NamedGraphs.NamedEdge{Int64}, Vector{Index}}
+0-element Dictionary{NamedEdge{Int64}, Vector{Index}}
 
 julia> tn1 = ITensorNetwork(s; link_space=2)
 ITensorNetwork{Int64} with 3 vertices:
@@ -175,7 +175,7 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Any}
+3-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=598|"S=1/2,Site,n=1"), (dim=2|id=123|"1↔2"))
  2 │ ((dim=2|id=457|"S=1/2,Site,n=2"), (dim=2|id=123|"1↔2"), (dim=2|id=656|"2↔3…
  3 │ ((dim=2|id=683|"S=1/2,Site,n=3"), (dim=2|id=656|"2↔3"))
@@ -192,7 +192,7 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Any}
+3-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=598|"S=1/2,Site,n=1"), (dim=2|id=382|"1↔2"))
  2 │ ((dim=2|id=457|"S=1/2,Site,n=2"), (dim=2|id=382|"1↔2"), (dim=2|id=190|"2↔3…
  3 │ ((dim=2|id=683|"S=1/2,Site,n=3"), (dim=2|id=190|"2↔3"))
@@ -312,5 +312,9 @@ This file was generated with [weave.jl](https://github.com/JunoLab/Weave.jl) wit
 
 ```julia
 using ITensorNetworks, Weave
-weave(joinpath(pkgdir(ITensorNetworks), "examples", "README.jl"); doctype="github", out_path=pkgdir(ITensorNetworks))
+weave(
+  joinpath(pkgdir(ITensorNetworks), "examples", "README.jl");
+  doctype="github",
+  out_path=pkgdir(ITensorNetworks),
+)
 ```

examples/belief_propagation/bpexample.jl

@@ -6,9 +6,9 @@ using ITensorNetworks
 using ITensorNetworks: construct_initial_mts, update_all_mts, get_single_site_expec
 
 n = 4
-dims = (n, n)
-g = named_grid(dims)
-# g = named_comb_tree(dims)
+system_dims = (n, n)
+g = named_grid(system_dims)
+# g = named_comb_tree(system_dims)
 s = siteinds("S=1/2", g)
 chi = 2
 
@@ -19,7 +19,7 @@ combiners = linkinds_combiners(ψψ)
 ψψ = combine_linkinds(ψψ, combiners)
 
 # Apply Sz to site v
-v = one.(dims)
+v = one.(system_dims)
 Oψ = copy(ψ)
 Oψ[v] = apply(op("Sz", s[v]), ψ[v])
 ψOψ = inner_network(ψ, Oψ; flatten=true, map_bra_linkinds=prime)

examples/contraction_sequence/contraction_sequence.jl

@@ -7,8 +7,8 @@ Random.seed!(1234)
 
 ITensors.disable_warn_order()
 
-dims = (2, 3)
-g = named_grid(dims)
+system_dims = (2, 3)
+g = named_grid(system_dims)
 s = siteinds("S=1/2", g)
 
 χ = 10
@@ -17,7 +17,7 @@ s = siteinds("S=1/2", g)
 tn = norm_sqr_network(ψ)
 
 # Contraction sequence for exactly computing expectation values
-# contract_edges = map(t -> (1, t...), collect(keys(cartesian_to_linear(dims))))
+# contract_edges = map(t -> (1, t...), collect(keys(cartesian_to_linear(system_dims))))
 # inner_sequence = reduce((x, y) -> [x, y], contract_edges)
 
 println("optimal")

examples/examples.jl

@@ -4,8 +4,8 @@ using ITensorUnicodePlots
 using NamedGraphs
 
 χ, d = 5, 2
-dims = (4, 4)
-g = named_grid(dims)
+system_dims = (4, 4)
+g = named_grid(system_dims)
 
 # Network of indices
 is = IndsNetwork(g; link_space=χ, site_space=d)

examples/partition/partitioning.jl

@@ -5,8 +5,8 @@ using ITensorUnicodePlots
 using KaHyPar
 using Suppressor
 
-dims = (4, 4)
-g = hypercubic_lattice_graph(dims)
+system_dims = (4, 4)
+g = hypercubic_lattice_graph(system_dims)
 
 s = siteinds("S=1/2", g)
 

examples/peps.jl

@@ -15,8 +15,8 @@ function heisenberg(g::AbstractGraph)
   return os
 end
 
-dims = (3, 3)
-g = named_grid(dims)
+system_dims = (3, 3)
+g = named_grid(system_dims)
 s = siteinds("S=1/2", g)
 
 ℋ = heisenberg(g)

examples/peps/ising_tebd.jl

@@ -8,22 +8,22 @@ Random.seed!(1234)
 
 ITensors.disable_warn_order()
 
-dims = (6, 6)
-n = prod(dims)
-g = named_grid(dims)
+system_dims = (6, 6)
+n = prod(system_dims)
+g = named_grid(system_dims)
 
 h = 2.0
 
 @show h
-@show dims
+@show system_dims
 
 s = siteinds("S=1/2", g)
 
 #
 # DMRG comparison
 #
 
-g_dmrg = rename_vertices(g, cartesian_to_linear(dims))
+g_dmrg = rename_vertices(g, cartesian_to_linear(system_dims))
 ℋ_dmrg = ising(g_dmrg; h)
 s_dmrg = [only(s[v]) for v in vertices(s)]
 H_dmrg = MPO(ℋ_dmrg, s_dmrg)
@@ -33,7 +33,7 @@ E_dmrg, ψ_dmrg = dmrg(
   H_dmrg, ψ_dmrg_init; nsweeps=20, maxdim=[fill(10, 10); 20], cutoff=1e-8
 )
 @show E_dmrg
-Z_dmrg = reshape(expect(ψ_dmrg, "Z"), dims)
+Z_dmrg = reshape(expect(ψ_dmrg, "Z"), system_dims)
 
 display(Z_dmrg)
 display(heatmap(Z_dmrg))
@@ -59,7 +59,7 @@ println("maxdim = $χ")
 @show ortho
 
 # Contraction sequence for exactly computing expectation values
-inner_sequence = reduce((x, y) -> [x, y], vec(Tuple.(CartesianIndices(dims))))
+inner_sequence = reduce((x, y) -> [x, y], vec(Tuple.(CartesianIndices(system_dims))))
 
 println("\nFirst run TEBD without orthogonalization")
 ψ = @time tebd(
@@ -81,6 +81,6 @@ E = @time expect(ℋ, ψ; sequence=inner_sequence)
 
 println("\nMeasure magnetization")
 Z_dict = @time expect("Z", ψ; sequence=inner_sequence)
-Z = [Z_dict[Tuple(I)] for I in CartesianIndices(dims)]
+Z = [Z_dict[Tuple(I)] for I in CartesianIndices(system_dims)]
 display(Z)
 display(heatmap(Z))

src/specialitensornetworks.jl

@@ -3,7 +3,7 @@ RETURN A TENSOR NETWORK WITH COPY TENSORS ON EACH VERTEX.
 Note that passing a link_space will mean the indices of the resulting network don't match those of the input indsnetwork
 """
 function delta_network(eltype::Type, s::IndsNetwork; link_space=nothing)
-  return ITensorNetwork((v, inds...) -> δ(eltype, inds...), s; link_space)
+  return ITensorNetwork((v, inds...) -> delta(eltype, inds...), s; link_space)
 end
 
 function delta_network(s::IndsNetwork; link_space=nothing)
@@ -50,7 +50,7 @@ function ising_network(eltype::Type, s::IndsNetwork, beta::Number; szverts=nothi
 end
 
 function ising_network(s::IndsNetwork, beta::Number; szverts=nothing)
-  return ising_network(typeof(beta), s; szverts)
+  return ising_network(typeof(beta), s, beta; szverts)
 end
 
 """

test/Project.toml

@@ -6,6 +6,7 @@ Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 GraphsFlows = "06909019-6f44-4949-96fc-b9d9aaa02889"
 ITensorUnicodePlots = "73163f41-4a9e-479f-8353-73bf94dbd758"
 ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5"
+KaHyPar = "2a6221f6-aa48-11e9-3542-2d9e0ef01880"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Metis = "2679e427-3c69-5b7f-982b-ece356f1e94b"
 NamedGraphs = "678767b0-92e7-4007-89e4-4527a8725b19"