Function to update only solverDicts and estimates back to cloud.

Author: GearsAD

src/CloudGraphsDFG/CloudGraphsDFG.jl

@@ -17,7 +17,7 @@ export getAddHistory, getDescription, getLabelDict
 export addVariable!, addFactor!
 export ls, lsf, getVariables, getFactors, getVariableIds, getFactorIds
 export getVariable, getFactor
-export updateVariable!, updateFactor!
+export updateVariable!, updateFactor!, updateVariableSolverData!
 export deleteVariable!, deleteFactor!
 export getAdjacencyMatrix
 export getNeighbors

src/CloudGraphsDFG/services/CloudGraphsDFG.jl

@@ -389,6 +389,20 @@ function updateVariable!(dfg::CloudGraphsDFG, variable::DFGVariable)::DFGVariabl
     return variable
 end
 
+"""
+    $(SIGNATURES)
+Update solver and estimate data for a variable (variable can be from another graph).
+"""
+function updateVariableSolverData!(dfg::CloudGraphsDFG, sourceVariable::DFGVariable)::DFGVariable
+    if !exists(dfg, sourceVariable)
+        error("Source variable '$(variable.label)' doesn't exist in the graph.")
+    end
+    nodeId = _tryGetNeoNodeIdFromNodeLabel(dfg.neo4jInstance, dfg.userId, dfg.robotId, dfg.sessionId, sourceVariable.label)
+    Neo4j.setnodeproperty(dfg.neo4jInstance.graph, nodeId, "estimateDict", JSON2.write(sourceVariable.estimateDict))
+    Neo4j.setnodeproperty(dfg.neo4jInstance.graph, nodeId, "solverDataDict", JSON2.write(Dict(keys(sourceVariable.solverDataDict) .=> map(vnd -> pack(dfg, vnd), values(sourceVariable.solverDataDict)))))
+    return sourceVariable
+end
+
 """
     $(SIGNATURES)
 Update a complete DFGFactor in the DFG.

test/HexagonalCloud.jl

@@ -2,7 +2,7 @@ using Revise
 using Neo4j # So that DFG initializes the database driver.
 using RoME
 using DistributedFactorGraphs
-using Test
+using Test, Dates
 
 # start with an empty factor graph object
 # fg = initfg()
@@ -24,17 +24,6 @@ IncrementalInference.compareVariable(x0, getVariable(cloudFg, :x0))
 
 # Add at a fixed location PriorPose2 to pin :x0 to a starting location (10,10, pi/4)
 prior = addFactor!(cloudFg, [:x0], PriorPose2( MvNormal([10; 10; 1.0/8.0], Matrix(Diagonal([0.1;0.1;0.05].^2))) ) )
-# retPrior = getFactor(cloudFg, :x0f1)
-# Do the check
-# IncrementalInference.compareFactor(prior, retPrior)
-# Testing
-
-# retPrior.data.fnc.cpt = prior.data.fnc.cpt
-# # This one
-# prior.data.fnc.cpt[1].factormetadata
-# deserialized: Any[Pose2(3, String[], (:Euclid, :Euclid, :Circular))]
-# vs.
-# original: Pose2[Pose2(3, String[], (:Euclid, :Euclid, :Circular))]
 
 # Drive around in a hexagon in the cloud
 for i in 0:5
@@ -58,60 +47,27 @@ toDotFile(localFg, "/tmp/localfg.dot")
 # Alrighty! At this point, we should be able to solve locally...
 # perform inference, and remember first runs are slower owing to Julia's just-in-time compiling
 # Can do with graph too!
-tree, smt, hist = solveTree!(localFg)
+# tree, smt, hist = solveTree!(localFg)
 
-wipeBuildNewTree!(localFg)
+# wipeBuildNewTree!(localFg)
 tree, smt, hist = solveTree!(localFg, tree) # Recycle
 # batchSolve!(localFg, drawpdf=true, show=true)
 # Erm, whut? Error = mcmcIterationIDs -- unaccounted variables
 
 # Trying new method.
-tree, smtasks = batchSolve!(localFg, treeinit=true, drawpdf=true, show=true,
-                            returntasks=true, limititers=50,
-                            upsolve=true, downsolve=true  )
-
-#### WIP and general debugging
-
-# Testing with GenericMarginal
-# This will not work because GenericMarginal *shouldn't* really be persisted.
-# That would mean we're decomposing the cloud graph...
-# genmarg = GenericMarginal()
-# Xi = [getVariable(fg, :x0)]
-# addFactor!(fg, Xi, genmarg, autoinit=false)
-
-# For Juno/Jupyter style use
-pl = drawPoses(localFg, meanmax=:mean)
-plotPose(fg, :x6)
-# For scripting use-cases you can export the image
-Gadfly.draw(Gadfly.PDF("/tmp/test1.pdf", 20cm, 10cm),pl)  # or PNG(...)
-
-
-# Add landmarks with Bearing range measurements
-addVariable!(fg, :l1, Point2, labels=["LANDMARK"])
-p2br = Pose2Point2BearingRange(Normal(0,0.1),Normal(20.0,1.0))
-addFactor!(fg, [:x0; :l1], p2br )
-
-
-# Initialize :l1 numerical values but do not rerun solver
-ensureAllInitialized!(fg)
-pl = drawPosesLandms(fg)
-Gadfly.draw(Gadfly.PDF("/tmp/test2.pdf", 20cm, 10cm),pl)  # or PNG(...)
-
-
-# Add landmarks with Bearing range measurements
-p2br2 = Pose2Point2BearingRange(Normal(0,0.1),Normal(20.0,1.0))
-addFactor!(fg, [:x6; :l1], p2br2 )
-
-
-# solve
-batchSolve!(fg, drawpdf=true)
-
-
-# redraw
-pl = drawPosesLandms(fg, meanmax=:mean)
-Gadfly.draw(Gadfly.PDF("/tmp/test3.pdf", 20cm, 10cm),pl)  # or PNG(...)
-
-
-
+# tree, smtasks = batchSolve!(localFg, treeinit=true, drawpdf=true, show=true,
+                            # returntasks=true, limititers=50,
+                            # upsolve=true, downsolve=true  )
+
+# Testing writing estimates
+for variable in getVariables(localFg)
+    means = mean(getData(variable).val, dims=2)[:]
+    variable.estimateDict[:default] = Dict{Symbol, VariableEstimate}(:Mean => VariableEstimate(:default, :Mean, means, now()))
+end
 
-#
+x0 = getVariable(localFg, :x0)
+data = getData(x0)
+# Update back to cloud.
+for variable in getVariables(localFg)
+    updateVariableSolverData!(cloudFg, variable)
+end