Make recom and flip chains an iterable a la GerryChain Python

Although this is a breaking change, this brings GerryChain Julia
more in line with GerryChain Python and allows the user to save
and export state more easily. This API is different from the one
proposed in #94 in that it returns a Partition object and the
scores.

Project.toml

@@ -17,6 +17,7 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ResumableFunctions = "c5292f4c-5179-55e1-98c5-05642aab7184"
 Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

src/GerryChain.jl

@@ -13,6 +13,7 @@ import Shapefile
 import LibGEOS
 import LibSpatialIndex
 using Logging
+using ResumableFunctions
 
 export AbstractGraph,
     BaseGraph,

src/flip.jl

@@ -150,7 +150,8 @@ every score at each step of the chain.
                       infinite loops if the acceptance function is never
                       satisfied!
 """
-function flip_chain(
+function flip_chain end  # this is a workaround
+@resumable function flip_chain(
     graph::BaseGraph,
     partition::Partition,
     pop_constraint::PopulationConstraint,
@@ -186,6 +187,7 @@ function flip_chain(
                 end
             end
             score_vals = score_partition_from_proposal(graph, partition, proposal, scores)
+            @yield partition, score_vals
             push!(chain_scores.step_values, score_vals)
             step_completed = true
         end

src/recom.jl

@@ -285,7 +285,8 @@ step of the chain.
                     infinite loops if the acceptance function is never
                     satisfied!
 """
-function recom_chain(
+function recom_chain end  # this is a workaround
+@resumable function recom_chain(
     graph::BaseGraph,
     partition::Partition,
     pop_constraint::PopulationConstraint,
@@ -322,6 +323,7 @@ function recom_chain(
                 end
             end
             score_vals = score_partition_from_proposal(graph, partition, proposal, scores)
+            @yield partition, score_vals
             push!(chain_scores.step_values, score_vals)
             step_completed = true
         end