Merge pull request #631 from JuliaRobotics/master

Bringing in latest.

Author: GearsAD

Project.toml

@@ -20,8 +20,8 @@ Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 TimeZones = "f269a46b-ccf7-5d73-abea-4c690281aa53"
-Unmarshal = "cbff2730-442d-58d7-89d1-8e530c41eb02"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+Unmarshal = "cbff2730-442d-58d7-89d1-8e530c41eb02"
 
 [compat]
 Colors = "0.8, 0.9, 0.10, 0.11, 0.12"

docs/src/BuildingGraphs.md

@@ -63,9 +63,9 @@ In addition, the following optional parameters are provided:
 Three variables are added:
 
 ```@example buildingGraphs; continued = true
-v1 = addVariable!(dfg, :x0, ContinuousScalar, labels = [:POSE], solvable=1)
-v2 = addVariable!(dfg, :x1, ContinuousScalar, labels = [:POSE], solvable=1)
-v3 = addVariable!(dfg, :l0, ContinuousScalar, labels = [:LANDMARK], solvable=1)
+v1 = addVariable!(dfg, :x0, ContinuousScalar, tags = [:POSE], solvable=1)
+v2 = addVariable!(dfg, :x1, ContinuousScalar, tags = [:POSE], solvable=1)
+v3 = addVariable!(dfg, :l0, ContinuousScalar, tags = [:LANDMARK], solvable=1)
 ```
 
 ### Creating Factors with IIF

docs/src/DrawingGraphs.md

@@ -26,9 +26,9 @@ using Cairo # hide
 using IncrementalInference
 # Create graph
 dfg = LightDFG{SolverParams}(solverParams=SolverParams())
-v1 = addVariable!(dfg, :x0, ContinuousScalar, labels = [:POSE], solvable=1)
-v2 = addVariable!(dfg, :x1, ContinuousScalar, labels = [:POSE], solvable=1)
-v3 = addVariable!(dfg, :l0, ContinuousScalar, labels = [:LANDMARK], solvable=1)
+v1 = addVariable!(dfg, :x0, ContinuousScalar, tags = [:POSE], solvable=1)
+v2 = addVariable!(dfg, :x1, ContinuousScalar, tags = [:POSE], solvable=1)
+v3 = addVariable!(dfg, :l0, ContinuousScalar, tags = [:LANDMARK], solvable=1)
 prior = addFactor!(dfg, [:x0], Prior(Normal(0,1)))
 f1 = addFactor!(dfg, [:x0; :x1], LinearConditional(Normal(50.0,2.0)), solvable=1)
 f1 = addFactor!(dfg, [:l0; :x0], LinearConditional(Normal(40.0,5.0)), solvable=1)
@@ -73,9 +73,9 @@ using DistributedFactorGraphs
 using IncrementalInference
 # Create graph
 dfg = LightDFG{SolverParams}(solverParams=SolverParams())
-v1 = addVariable!(dfg, :x0, ContinuousScalar, labels = [:POSE], solvable=1)
-v2 = addVariable!(dfg, :x1, ContinuousScalar, labels = [:POSE], solvable=1)
-v3 = addVariable!(dfg, :l0, ContinuousScalar, labels = [:LANDMARK], solvable=1)
+v1 = addVariable!(dfg, :x0, ContinuousScalar, tags = [:POSE], solvable=1)
+v2 = addVariable!(dfg, :x1, ContinuousScalar, tags = [:POSE], solvable=1)
+v3 = addVariable!(dfg, :l0, ContinuousScalar, tags = [:LANDMARK], solvable=1)
 prior = addFactor!(dfg, [:x0], Prior(Normal(0,1)))
 f1 = addFactor!(dfg, [:x0; :x1], LinearConditional(Normal(50.0,2.0)), solvable=1)
 f1 = addFactor!(dfg, [:l0; :x0], LinearConditional(Normal(40.0,5.0)), solvable=1)

docs/src/GraphData.md

@@ -26,8 +26,8 @@ using IncrementalInference
 # Create a DFG with default solver parameters using the LightGraphs.jl driver.
 dfg = LightDFG{SolverParams}(params=SolverParams())
 
-x0 = addVariable!(dfg, :x0, ContinuousScalar, labels = [:POSE], solvable=1)
-x1 = addVariable!(dfg, :x1, ContinuousScalar, labels = [:POSE], solvable=1)
+x0 = addVariable!(dfg, :x0, ContinuousScalar, tags = [:POSE], solvable=1)
+x1 = addVariable!(dfg, :x1, ContinuousScalar, tags = [:POSE], solvable=1)
 f1 = addFactor!(dfg, [:x0; :x1], LinearConditional(Normal(50.0,2.0)), solvable=1)
 ```
 

src/FileDFG/services/FileDFG.jl

@@ -12,7 +12,7 @@ using DistributedFactorGraphs, IncrementalInference
 # Create a DFG - can make one directly, e.g. LightDFG{NoSolverParams}() or use IIF:
 dfg = initfg()
 # ... Add stuff to graph using either IIF or DFG:
-v1 = addVariable!(dfg, :a, ContinuousScalar, labels = [:POSE], solvable=0)
+v1 = addVariable!(dfg, :a, ContinuousScalar, tags = [:POSE], solvable=0)
 # Now save it:
 saveDFG(dfg, "/tmp/saveDFG.tar.gz")
 ```

test/CGStructureTests.jl

@@ -55,9 +55,9 @@ setSessionData!(dfg, session.data)
 @test getSessionData(dfg) == session.data
 
 # Add some nodes.
-v1 = addVariable!(dfg, :a, ContinuousScalar, labels = [:POSE])
-v2 = addVariable!(dfg, :b, ContinuousScalar, labels = [:POSE])
-v3 = addVariable!(dfg, :c, ContinuousScalar, labels = [:LANDMARK])
+v1 = addVariable!(dfg, :a, ContinuousScalar, tags = [:POSE])
+v2 = addVariable!(dfg, :b, ContinuousScalar, tags = [:POSE])
+v3 = addVariable!(dfg, :c, ContinuousScalar, tags = [:LANDMARK])
 addFactor!(dfg, [:a], Prior(Normal(0,1)))
 f1 = addFactor!(dfg, [:a; :b], LinearConditional(Normal(50.0,2.0)) )
 f2 = addFactor!(dfg, [:b; :c], LinearConditional(Normal(50.0,2.0)) )

test/fileDFGTests.jl

@@ -18,7 +18,7 @@ using Test
         numNodes = 5
 
         # Arranging some random data
-        verts = map(n -> addVariable!(dfg, Symbol("x$n"), ContinuousScalar, labels = [:POSE]), 1:numNodes)
+        verts = map(n -> addVariable!(dfg, Symbol("x$n"), ContinuousScalar, tags = [:POSE]), 1:numNodes)
         map(v -> setSolvable!(v, Int(round(rand()))), verts)
         map(v -> getSolverData(verts[4]).solveInProgress=Int(round(rand())), verts)
         map(v -> setSolvedCount!(v, Int(round(10*rand()))), verts)

test/iifInterfaceTests.jl

@@ -10,8 +10,8 @@ end
 @testset "Building a simple Graph" begin
     global dfg,v1,v2,f1
     # Use IIF to add the variables and factors
-    v1 = addVariable!(dfg, :a, ContinuousScalar, labels = [:POSE], solvable=0)
-    v2 = addVariable!(dfg, :b, ContinuousScalar, labels = [:LANDMARK], solvable=1)
+    v1 = addVariable!(dfg, :a, ContinuousScalar, tags = [:POSE], solvable=0)
+    v2 = addVariable!(dfg, :b, ContinuousScalar, tags = [:LANDMARK], solvable=1)
     f1 = addFactor!(dfg, [:a; :b], LinearConditional(Normal(50.0,2.0)), solvable=0)
 
     @show dfg
@@ -323,10 +323,10 @@ end
         @test isConnected(dfg) == true
         # @test @test_deprecated isFullyConnected(dfg) == true
         # @test @test_deprecated hasOrphans(dfg) == false
-        addVariable!(dfg, :orphan, ContinuousScalar, labels = [:POSE], solvable=0)
+        addVariable!(dfg, :orphan, ContinuousScalar, tags = [:POSE], solvable=0)
         @test isConnected(dfg) == false
     else
-        addVariable!(dfg, :orphan, ContinuousScalar, labels = [:POSE], solvable=0)
+        addVariable!(dfg, :orphan, ContinuousScalar, tags = [:POSE], solvable=0)
         @warn "CloudGraphsDFG is currently failing with the connectivity test."
     end
 end
@@ -387,7 +387,7 @@ numNodes = 10
 # end
 
 #change solvable and solveInProgress for x7,x8 for improved tests on x7x8f1
-verts = map(n -> addVariable!(dfg, Symbol("x$n"), ContinuousScalar, labels = [:POSE]), 1:numNodes)
+verts = map(n -> addVariable!(dfg, Symbol("x$n"), ContinuousScalar, tags = [:POSE]), 1:numNodes)
 #TODO fix this to use accessors
 setSolvable!(verts[7], 1)
 setSolvable!(verts[8], 0)

test/solveTest.jl

@@ -1,9 +1,9 @@
 #TODO Test with standard generated graphs from IIF
 # Add some nodes.
-v1 = addVariable!(dfg, :a, ContinuousScalar, labels = [:POSE])
+v1 = addVariable!(dfg, :a, ContinuousScalar, tags = [:POSE])
 addFactor!(dfg, [:a], Prior(Normal(0,1)))
-v2 = addVariable!(dfg, :b, ContinuousScalar, labels = [:POSE])
-v3 = addVariable!(dfg, :c, ContinuousScalar, labels = [:LANDMARK])
+v2 = addVariable!(dfg, :b, ContinuousScalar, tags = [:POSE])
+v3 = addVariable!(dfg, :c, ContinuousScalar, tags = [:LANDMARK])
 f1 = addFactor!(dfg, [:a; :b], LinearConditional(Normal(50.0,2.0)) )
 f2 = addFactor!(dfg, [:b; :c], LinearConditional(Normal(50.0,2.0)) )
 

test/testFurther.jl

@@ -10,9 +10,9 @@ dfg = CloudGraphsDFG{SolverParams}("localhost", 7474, "neo4j", "test",
 clearUser!!(dfg)
 
 # Add some nodes.
-v1 = addVariable!(dfg, :a, ContinuousScalar, labels = [:POSE])
-v2 = addVariable!(dfg, :b, ContinuousScalar, labels = [:POSE])
-v3 = addVariable!(dfg, :c, ContinuousScalar, labels = [:LANDMARK])
+v1 = addVariable!(dfg, :a, ContinuousScalar, tags = [:POSE])
+v2 = addVariable!(dfg, :b, ContinuousScalar, tags = [:POSE])
+v3 = addVariable!(dfg, :c, ContinuousScalar, tags = [:LANDMARK])
 addFactor!(dfg, [:a], Prior(Normal(0,1)))
 f1 = addFactor!(dfg, [:a; :b], LinearConditional(Normal(50.0,2.0)) )
 f2 = addFactor!(dfg, [:b; :c], LinearConditional(Normal(50.0,2.0)) )