Compat JSON v1.0 and DataStructure v0.19

Project.toml

@@ -24,10 +24,10 @@ LuxorTensorPlot = ["LuxorGraphPlot"]
 AbstractTrees = "0.3, 0.4"
 Aqua = "0.8"
 CliqueTrees = "1.12.1"
-DataStructures = "0.18"
+DataStructures = "0.19"
 Documenter = "1.10.1"
 Graphs = "1"
-JSON = "0.21"
+JSON = "0.21, 1"
 KaHyPar = "0.3"
 LuxorGraphPlot = "0.5"
 OMEinsum = "0.9"

src/OMEinsumContractionOrders.jl

@@ -8,7 +8,7 @@ using Base.Threads
 using AbstractTrees
 using TreeWidthSolver
 using TreeWidthSolver.Graphs
-using DataStructures: PriorityQueue, enqueue!, dequeue!, peek, dequeue_pair!
+using DataStructures: PriorityQueue
 import CliqueTrees
 using CliqueTrees: cliquetree, cliquetree!, separator, residual, CliqueTree, EliminationAlgorithm, MMW, BFS, MCS, LexBFS, RCMMD, RCMGL, MCSM, LexM, AMF, MF, MMD, MF, BT, SafeRules, KaHyParND, METISND, ND, BestWidth, ConnectedComponents
 

src/greedy.jl

@@ -82,7 +82,7 @@ function evaluate_costs(α::TA, incidence_list::IncidenceList{Int,ET}, log2_edge
     for vi in vertices(incidence_list)
         for vj in neighbors(incidence_list, vi)
             if vj > vi
-                enqueue!(cost_values, (vi,vj), greedy_loss(α, incidence_list, log2_edge_sizes, vi, vj))
+                push!(cost_values, (vi,vj) => greedy_loss(α, incidence_list, log2_edge_sizes, vi, vj))
                 add_edge!(cost_graph, vi, vj)
             end
         end
@@ -95,9 +95,9 @@ function update_costs!(cost_values, cost_graph, va, vb, α::TA, incidence_list::
         vx, vy = minmax(vj, va)
         if has_edge(cost_graph, vx, vy)
             delete!(cost_values, (vx,vy))
-            enqueue!(cost_values, (vx,vy), greedy_loss(α, incidence_list, log2_edge_sizes, vx, vy))
+            push!(cost_values, (vx,vy) => greedy_loss(α, incidence_list, log2_edge_sizes, vx, vy))
         else
-            enqueue!(cost_values, (vx,vy), greedy_loss(α, incidence_list, log2_edge_sizes, vx, vy))
+            push!(cost_values, (vx,vy) => greedy_loss(α, incidence_list, log2_edge_sizes, vx, vy))
             add_edge!(cost_graph, vx, vy)
         end
     end
@@ -110,18 +110,18 @@ end
 
 function find_best_cost!(temperature::TT, cost_values::PriorityQueue{PT}, cost_graph) where {PT,TT}
     length(cost_values) < 1 && error("cost value information missing")
-    pair, cost = dequeue_pair!(cost_values)
+    pair, cost = popfirst!(cost_values)
     if iszero(temperature) || isempty(cost_values)
         rem_edge!(cost_graph, pair...)
         return pair
     else
-        pair2, cost2 = dequeue_pair!(cost_values)
+        pair2, cost2 = popfirst!(cost_values)
         if rand() < exp(-(cost2 - cost) / temperature)   # pick 2
-            enqueue!(cost_values, pair, cost)
+            push!(cost_values, pair => cost)
             rem_edge!(cost_graph, pair2...)
             return pair2
         else
-            enqueue!(cost_values, pair2, cost2)
+            push!(cost_values, pair2 => cost2)
             rem_edge!(cost_graph, pair...)
             return pair
         end

src/json.jl

@@ -73,15 +73,15 @@ function einstodict(eins::EinCode)
     return Dict("ixs"=>ixs, "iy"=>iy)
 end
 
-function fromdict(::Type{LT}, dict::Dict) where LT
+function fromdict(::Type{LT}, dict::AbstractDict) where LT
     if dict["isleaf"]
         return NestedEinsum{LT}(dict["tensorindex"])
     end
     eins = einsfromdict(LT, dict["eins"])
     return NestedEinsum(fromdict.(LT, dict["args"]), eins)
 end
 
-function einsfromdict(::Type{LT}, dict::Dict) where LT
+function einsfromdict(::Type{LT}, dict::AbstractDict) where LT
     return EinCode([collect(LT, _convert.(LT, ix)) for ix in dict["ixs"]], collect(LT, _convert.(LT, dict["iy"])))
 end
 

src/simplify.jl

@@ -94,7 +94,7 @@ function merge_greedy(code::EinCode{LT}, size_dict; threshhold=-1e-12) where LT
         if isempty(cost_values)
             return _buildsimplifier(tree, incidence_list)
         end
-        pair, v = peek(cost_values)
+        pair, v = first(cost_values)
         if v <= threshhold
             _, _, c = contract_pair!(incidence_list, pair..., log2_edge_sizes)
             tree[pair[1]] = NestedEinsum([tree[pair[1]], tree[pair[2]]], EinCode([c.first...], c.second))