Update for DFGv1 whereNoun filters

IncrementalInference/src/CliqueStateMachine/services/CliqueStateMachine.jl

@@ -287,7 +287,7 @@ function preUpSolve_StateMachine(csmc::CliqStateMachineContainer)
   logCSM(
     csmc,
     "CSM-2a messages for up";
-    upmsg = lsf(csmc.cliqSubFg; tagsFilter = ⊇([:__LIKELIHOODMESSAGE__])),
+    upmsg = lsf(csmc.cliqSubFg; whereTags = ⊇([:__LIKELIHOODMESSAGE__])),
   )
 
   # store the cliqSubFg for later debugging
@@ -762,7 +762,7 @@ function tryDownInit_StateMachine(csmc::CliqStateMachineContainer)
   # structure for all up message densities computed during this initialization procedure.
   # XXX
   dwnkeys_ =
-    lsf(csmc.cliqSubFg; tagsFilter = ⊇([:__DOWNWARD_COMMON__;])) .|> x -> ls(csmc.cliqSubFg, x)[1]
+    lsf(csmc.cliqSubFg; whereTags = ⊇([:__DOWNWARD_COMMON__;])) .|> x -> ls(csmc.cliqSubFg, x)[1]
   initorder = getCliqInitVarOrderDown(csmc.cliqSubFg, csmc.cliq, dwnkeys_)
   # initorder = getCliqVarInitOrderUp(csmc.tree, csmc.cliq)
 

IncrementalInference/src/ExportAPI.jl

@@ -164,7 +164,7 @@ export CSMHistory,
   TreeBelief,
   LikelihoodMessage,
   initfg,
-  buildSubgraph,
+  getSubgraph,
   buildCliqSubgraph!,
   transferUpdateSubGraph!,
   getEliminationOrder,

IncrementalInference/src/parametric/services/ParametricUtils.jl

@@ -682,7 +682,7 @@ function solveConditionalsParametric(
 )
   varIds = [frontals; separators]
 
-  sfg = issetequal(varIds, listVariables(fg)) ? fg : buildSubgraph(fg, varIds, 1)
+  sfg = issetequal(varIds, listVariables(fg)) ? fg : getSubgraph(fg, varIds, 1)
 
   flatvar = FlatVariables(fg, varIds)
 
@@ -941,7 +941,7 @@ function updateParametricSolution!(fg, M, labels::AbstractArray{Symbol}, vals, 
 
   for (i, (v, val)) in enumerate(zip(labels, vals))
     vnd = getState(getVariable(fg, v), solveKey)
-    covar = isnothing(Σ) ? vnd.bw : covars[i]
+    covar = isnothing(Σ) ? DFG.refCovariances(vnd)[1] : covars[i]
     # Update the variable node data value and covariance
     updateSolverDataParametric!(vnd, val, covar)#FIXME add cov
   end

IncrementalInference/src/services/AnalysisTools.jl

@@ -19,7 +19,7 @@ function shrinkFactorGraph(fg; upto::Int = 6)
   fgs = deepcopy(fg)
 
   delVars = filter(x -> isSolvable(getVariable(fgs, x)) == 0, ls(fgs))
-  todel = setdiff(lsf(fgs; solvableFilter = >=(0)), lsf(fgs; solvableFilter = >=(1)))
+  todel = setdiff(lsf(fgs; whereSolvable = >=(0)), lsf(fgs; whereSolvable = >=(1)))
   delFcts = intersect(lsf(fgs), todel)
   allMags = filter(x -> :MAGNETOMETER in listTags(getFactor(fgs, x)), lsfPriors(fgs))
   union!(delFcts, filter(x -> length(ls(fgs, x)) == 0, allMags))

IncrementalInference/src/services/BayesNet.jl

@@ -149,11 +149,11 @@ function buildBayesNet!(dfg::AbstractDFG, elimorder::Vector{Symbol}; solvable::I
     gm = FactorCompute[]
 
     vert = DFG.getVariable(dfg, v)
-    for fctId in listNeighbors(dfg, vert; solvableFilter = >=(solvable))
+    for fctId in listNeighbors(dfg, vert; whereSolvable = >=(solvable))
       fct = DFG.getFactor(dfg, fctId)
       if (fct.state.eliminated != true)
         push!(fi, fctId)
-        for sepNode in listNeighbors(dfg, fct; solvableFilter = >=(solvable))
+        for sepNode in listNeighbors(dfg, fct; whereSolvable = >=(solvable))
           # TODO -- validate !(sepNode.index in Si) vs. older !(sepNode in Si)
           if sepNode != v && !(sepNode in Si) # Symbol comparison!
             push!(Si, sepNode)

IncrementalInference/src/services/GraphInit.jl

@@ -21,7 +21,7 @@ See also: [`doautoinit!`](@ref), [`initAll!`](@ref)
 function makeSolverData!(
   dfg::AbstractDFG;
   solvable = 1,
-  varList::AbstractVector{Symbol} = ls(dfg; solvableFilter = >=(solvable)),
+  varList::AbstractVector{Symbol} = ls(dfg; whereSolvable = >=(solvable)),
   solveKey::Symbol=:default
 )
   count = 0
@@ -475,8 +475,8 @@ function ensureSolvable!(
   solvableFallback::Int = 0,
 )
   # workaround in case isolated variables occur
-  solvVars = ls(dfg; solvableFilter = >=(solvableTarget))
-  varHasFact = (x -> length(listNeighbors(dfg, x; solvableFilter = >=(solvableTarget))) == 0).(solvVars)
+  solvVars = ls(dfg; whereSolvable = >=(solvableTarget))
+  varHasFact = (x -> length(listNeighbors(dfg, x; whereSolvable = >=(solvableTarget))) == 0).(solvVars)
   blankVars = solvVars[findall(varHasFact)]
   if 0 < length(blankVars)
     @warn(
@@ -504,7 +504,7 @@ function initAll!(
 )
   #
   # allvarnodes = getVariables(dfg)
-  syms = intersect(DFG.getAddHistory(dfg), ls(dfg; solvableFilter = >=(solvable)))
+  syms = intersect(DFG.getAddHistory(dfg), ls(dfg; whereSolvable = >=(solvable)))
   # syms = ls(dfg, solvable=solvable) # |> sortDFG
 
   # May have to first add the solveKey VNDs if they are not yet available

IncrementalInference/src/services/JunctionTreeUtils.jl

@@ -999,7 +999,7 @@ function getCliqFactorsFromFrontals(
     for fctid in ls(fgl, frsym)
       fct = getFactor(fgl, fctid)
       if !unused || !fct.state.potentialused
-        loutn = listNeighbors(fgl, fctid; solvableFilter = >=(solvable))
+        loutn = listNeighbors(fgl, fctid; whereSolvable = >=(solvable))
         # deal with unary factors
         if length(loutn) == 1
           union!(usefcts, Symbol[Symbol(fct.label);])
@@ -1194,9 +1194,9 @@ function getCliqVarsWithFrontalNeighbors(
   union!(syms, Symbol.(cond))
 
   # TODO Can we trust factors are frontal connected?
-  ffcs = union(map(x -> listNeighbors(fgl, x; solvableFilter = >=(solvable)), frtl)...)
+  ffcs = union(map(x -> listNeighbors(fgl, x; whereSolvable = >=(solvable)), frtl)...)
   # @show ffcs = getCliqueData(cliq).potentials
-  neig = union(map(x -> listNeighbors(fgl, x; solvableFilter = >=(solvable)), ffcs)...)
+  neig = union(map(x -> listNeighbors(fgl, x; whereSolvable = >=(solvable)), ffcs)...)
   union!(syms, Symbol.(neig))
   return syms
 end

IncrementalInference/src/services/SolverAPI.jl

@@ -393,8 +393,8 @@ function solveTree!(
     allk = parse.(Int, ss_)
     nextk = length(allk) == 0 ? 0 : maximum(allk) + 1
     newKey = Symbol(:default_, nextk)
-    # DFG.cloneStates!(dfgl, newKey, :default; solvableFilter = >=(1))
-    for vlabel in ls(dfgl; solvableFilter = >=(1))
+    # DFG.cloneStates!(dfgl, newKey, :default; whereSolvable = >=(1))
+    for vlabel in ls(dfgl; whereSolvable = >=(1))
         DFG.copytoState!(dfgl, vlabel, newKey, getState(dfgl, vlabel, :default))
     end
     # foreach(x->updateVariableSolverData!(dfgl, x, getState(getVariable(dfgl,x), :default), newKey, true, Symbol[]), ls(dfgl, solvable=1))

IncrementalInference/src/services/SolverUtilities.jl

@@ -157,7 +157,7 @@ function _buildGraphByFactorAndTypes!(
   newFactor::Bool = true,
   destPattern::Regex = r"x\d+",
   destPrefix::Symbol = match(r"[a-zA-Z_]+", destPattern.pattern).match |> Symbol,
-  _allVars::AbstractVector{Symbol} = sortDFG(ls(dfg; labelFilter=contains(destPattern))),
+  _allVars::AbstractVector{Symbol} = sortDFG(ls(dfg; whereLabel=contains(destPattern))),
   currLabel::Symbol = 0 < length(_allVars) ? _allVars[end] : Symbol(destPrefix, 0),
   currNumber::Integer = reverse(match(r"\d+", reverse(string(currLabel))).match) |>
                         x -> parse(Int, x),

IncrementalInference/src/services/SubGraphFunctions.jl

@@ -3,7 +3,7 @@
     $(SIGNATURES)
 Specialized subgraph function for cliques to build a deep subgraph copy from the DFG given a list of frontals and separators.
 Dev notes:
-- TODO Since a clique should already have a list of frontals, seperators, and potentials (factors), this function should just be a light wrapper around copyGraph or buildSubgraph
+- TODO Since a clique should already have a list of frontals, seperators, and potentials (factors), this function should just be a light wrapper around copyGraph or getSubgraph
 - TODO Send in clique and then extract frontals, separators and factors
 - TODO ability to limit which solveKeys to copy.
 """

IncrementalInference/src/services/TreeMessageUtils.jl

@@ -136,8 +136,8 @@ function _findSubgraphsFactorType(
   # 1. count separtor connectivity in UPWARD_DIFFERENTIAL
   sepsCount = Dict{Symbol, Int}()
   map(x -> (sepsCount[x] = 0), separators)
-  # tagsFilter = [:__LIKELIHOODMESSAGE__;]
-  # tflsf = lsf(fg, tags=tagsFilter)
+  # whereTags = [:__LIKELIHOODMESSAGE__;]
+  # tflsf = lsf(fg, tags=whereTags)
   for likl in jointrelatives
     for vari in likl.variables
       sepsCount[vari] += 1
@@ -168,7 +168,7 @@ function _findSubgraphsFactorType(
         dfg_,
         key1,
         key2;
-        typeFilterFactors = x -> x <: defaultFct,
+        whereFactorType = x -> x <: defaultFct,
         initialized = true,
       )
       # check if connected to existing subClass
@@ -614,7 +614,7 @@ function deleteMsgFactors!(
   tags::Vector{Symbol} = [:__LIKELIHOODMESSAGE__],
 )
   # remove msg factors that were added to the subfg
-  facs = lsf(subfg; tagsFilter = !isdisjoint(tags))
+  facs = lsf(subfg; whereTags = !isdisjoint(tags))
   deleteFactor!.(subfg, facs)
   return facs
 end

IncrementalInference/test/testCompareVariablesFactors.jl

@@ -105,7 +105,7 @@ addFactor!(fg, [:x1;:x2], LinearRelative(Normal(4.0, 0.1)))
 addVariable!(fg, :l1, ContinuousScalar)
 addFactor!(fg, [:x1;:l1], LinearRelative(Rayleigh()))
 
-sfg = buildSubgraph(fg, [:x0;:x1], 1) # distance=1 to include factors 
+sfg = getSubgraph(fg, [:x0;:x1], 1) # distance=1 to include factors 
 
 #FIXME JT - this doesn't make sense to pass, it is a subgraph so should it not rather be ⊂ [subset]?
 # compareDFG(fg1, fg2, by=⊂, skip=...)

IncrementalInference/test/testDeadReckoningTether.jl

@@ -78,8 +78,8 @@ drec = MutableLinearRelative(Normal(0.0,0.1))
 addFactor!(fg, [:x0; :deadreckon_x0], drec, solvable=0)
 
 #
-@test length(map( x->x.label, getVariables(fg, solvableFilter = >=(1)))) == 8
-@test length(map( x->x.label, getVariables(fg, solvableFilter = >=(0)))) == 9
+@test length(map( x->x.label, getVariables(fg, whereSolvable = >=(1)))) == 8
+@test length(map( x->x.label, getVariables(fg, whereSolvable = >=(0)))) == 9
 #
 # # make sure
 @test length(getEliminationOrder(fg, solvable=1)) == 8

IncrementalInference/test/testDefaultDeconv.jl

@@ -108,7 +108,7 @@ addLikelihoodsDifferential!.(tfg, values(msg))
 @show ls(tfg)
 @test issetequal(ls(tfg), [:x2,:x4,:x6])
 @test lsf(tfg) |> length == 2
-@test lsf(tfg, tagsFilter = ⊇([:__UPWARD_DIFFERENTIAL__])) |> length == 2
+@test lsf(tfg, whereTags = ⊇([:__UPWARD_DIFFERENTIAL__])) |> length == 2
 
 ##
 

IncrementalInference/test/testJointEnforcement.jl

@@ -46,11 +46,11 @@ tp2_ = [ :x0;:x0x3f1;:x3;:x2x3f1;:x2]
 pth = findShortestPathDijkstra(fg, :x0, :x2)
 @test pth == tp1_ || pth == tp2_
 
-pth = findShortestPathDijkstra(fg, :x0, :x2, typeFilterFactors = x -> x <: LinearRelative)
+pth = findShortestPathDijkstra(fg, :x0, :x2, whereFactorType = x -> x <: LinearRelative)
 @test pth == tp1_
 
 # different path
-pth = findShortestPathDijkstra(fg, :x0, :x2, typeFilterFactors = x -> x <: EuclidDistance)
+pth = findShortestPathDijkstra(fg, :x0, :x2, whereFactorType = x -> x <: EuclidDistance)
 @test pth == tp2_
 
 
@@ -160,11 +160,11 @@ tp2_ = [ :x0;:x0x3f1;:x3;:x2x3f1;:x2]
 pth = findShortestPathDijkstra(fg, :x0, :x2)
 @test pth == tp1_ || pth == tp2_
 
-pth = findShortestPathDijkstra(fg, :x0, :x2, typeFilterFactors = x -> x <: LinearRelative)
+pth = findShortestPathDijkstra(fg, :x0, :x2, whereFactorType = x -> x <: LinearRelative)
 @test pth == tp1_ || pth == tp2_
 
 # different path
-pth = findShortestPathDijkstra(fg, :x0, :x2, typeFilterFactors = x -> x <: EuclidDistance)
+pth = findShortestPathDijkstra(fg, :x0, :x2, whereFactorType = x -> x <: EuclidDistance)
 @test pth == Symbol[]
 
 

attic/CSMCommon_Consolidate.jl

@@ -220,7 +220,7 @@ function buildCliqSubgraphForDown_StateMachine(csmc::CliqStateMachineContainer)
   # build a local subgraph for inference operations
   syms = getCliqAllVarIds(csmc.cliq)
   infocsm(csmc, "2r, build subgraph syms=$(syms)")
-  csmc.cliqSubFg = buildSubgraph(csmc.dfg, syms, 1; verbose=false)
+  csmc.cliqSubFg = getSubgraph(csmc.dfg, syms, 1; verbose=false)
 
   opts = getSolverParams(csmc.dfg)
   # store the cliqSubFg for later debugging