Merge pull request #46 from JuliaRobotics/hotfix/v013

fix testing

Author: dehann

Project.toml

@@ -22,9 +22,9 @@ Requires = "≥ 0.5"
 julia = "0.7, 1"
 
 [extras]
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 IncrementalInference = "904591bb-b899-562f-9e6f-b8df64c7d480"
 RoME = "91fb55c2-4c03-5a59-ba21-f4ea956187b8"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
 test = ["Test"]

src/CloudGraphsDFG/CloudGraphsDFG.jl

@@ -1,5 +1,4 @@
 using Neo4j
-using JSON2
 
 # Entities
 include("entities/CloudGraphsDFG.jl")

src/DistributedFactorGraphs.jl

@@ -6,6 +6,7 @@ using Requires
 using Dates
 using Distributions
 using Reexport
+using JSON2
 
 # Entities
 include("entities/AbstractTypes.jl")
@@ -35,8 +36,39 @@ include("services/DFGVariable.jl")
 
 # Include the Graphs.jl API.
 include("GraphsDFG/GraphsDFG.jl")
-# Include the Cloudgraphs API
-include("CloudGraphsDFG/CloudGraphsDFG.jl")
+
+function __init__()
+    @require DataFrames="a93c6f00-e57d-5684-b7b6-d8193f3e46c0" begin
+        if isdefined(Main, :DataFrames)
+            """
+                $(SIGNATURES)
+            Get an adjacency matrix for the DFG as a DataFrame.
+            Rows are all factors, columns are all variables, and each cell contains either nothing or the symbol of the relating factor.
+            The first column is the factor headings.
+            """
+            function getAdjacencyMatrixDataFrame(dfg::GraphsDFG)::Main.DataFrames.DataFrame
+                varLabels = sort(map(v->v.label, getVariables(dfg)))
+                factLabels = sort(map(f->f.label, getFactors(dfg)))
+                adjDf = DataFrames.DataFrame(:Factor => Union{Missing, Symbol}[])
+                for varLabel in varLabels
+                    adjDf[varLabel] = Union{Missing, Symbol}[]
+                end
+                for (i, factLabel) in enumerate(factLabels)
+                    push!(adjDf, [factLabel, DataFrames.missings(length(varLabels))...])
+                    factVars = getNeighbors(dfg, getFactor(dfg, factLabel))
+                    map(vLabel -> adjDf[vLabel][i] = factLabel, factVars)
+                end
+                return adjDf
+            end
+        end
+    end
+
+  @require Neo4j="d2adbeaf-5838-5367-8a2f-e46d570981db" begin
+    # Include the Cloudgraphs API
+    include("CloudGraphsDFG/CloudGraphsDFG.jl")
+  end
+end
+
 # not sure where to put
 include("Common.jl")
 include("NeedsAHome.jl")

src/GraphsDFG/services/GraphsDFG.jl

@@ -502,29 +502,29 @@ function toDotFile(dfg::GraphsDFG, fileName::String="/tmp/dfg.dot")::Nothing
     return nothing
 end
 
-function __init__()
-    @require DataFrames="a93c6f00-e57d-5684-b7b6-d8193f3e46c0" begin
-        if isdefined(Main, :DataFrames)
-            """
-                $(SIGNATURES)
-            Get an adjacency matrix for the DFG as a DataFrame.
-            Rows are all factors, columns are all variables, and each cell contains either nothing or the symbol of the relating factor.
-            The first column is the factor headings.
-            """
-            function getAdjacencyMatrixDataFrame(dfg::GraphsDFG)::Main.DataFrames.DataFrame
-                varLabels = sort(map(v->v.label, getVariables(dfg)))
-                factLabels = sort(map(f->f.label, getFactors(dfg)))
-                adjDf = DataFrames.DataFrame(:Factor => Union{Missing, Symbol}[])
-                for varLabel in varLabels
-                    adjDf[varLabel] = Union{Missing, Symbol}[]
-                end
-                for (i, factLabel) in enumerate(factLabels)
-                    push!(adjDf, [factLabel, DataFrames.missings(length(varLabels))...])
-                    factVars = getNeighbors(dfg, getFactor(dfg, factLabel))
-                    map(vLabel -> adjDf[vLabel][i] = factLabel, factVars)
-                end
-                return adjDf
-            end
-        end
-    end
-end
+# function __init__()
+#     @require DataFrames="a93c6f00-e57d-5684-b7b6-d8193f3e46c0" begin
+#         if isdefined(Main, :DataFrames)
+#             """
+#                 $(SIGNATURES)
+#             Get an adjacency matrix for the DFG as a DataFrame.
+#             Rows are all factors, columns are all variables, and each cell contains either nothing or the symbol of the relating factor.
+#             The first column is the factor headings.
+#             """
+#             function getAdjacencyMatrixDataFrame(dfg::GraphsDFG)::Main.DataFrames.DataFrame
+#                 varLabels = sort(map(v->v.label, getVariables(dfg)))
+#                 factLabels = sort(map(f->f.label, getFactors(dfg)))
+#                 adjDf = DataFrames.DataFrame(:Factor => Union{Missing, Symbol}[])
+#                 for varLabel in varLabels
+#                     adjDf[varLabel] = Union{Missing, Symbol}[]
+#                 end
+#                 for (i, factLabel) in enumerate(factLabels)
+#                     push!(adjDf, [factLabel, DataFrames.missings(length(varLabels))...])
+#                     factVars = getNeighbors(dfg, getFactor(dfg, factLabel))
+#                     map(vLabel -> adjDf[vLabel][i] = factLabel, factVars)
+#                 end
+#                 return adjDf
+#             end
+#         end
+#     end
+# end