Merge pull request #120 from JuliaRobotics/feature/summaryfunction

Summary functions

Project.toml

@@ -13,7 +13,6 @@ JSON2 = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3"
 LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
-Neo4j = "d2adbeaf-5838-5367-8a2f-e46d570981db"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
@@ -29,6 +28,8 @@ julia = "0.7, 1"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+GraphPlot = "a2cc645c-3eea-5389-862e-a155d0052231"
+Neo4j = "d2adbeaf-5838-5367-8a2f-e46d570981db"
 
 [targets]
-test = ["Test"]
+test = ["Test", "GraphPlot", "Neo4j"]

docs/src/func_ref.md

@@ -58,6 +58,12 @@ getSubgraphAroundNode
 getSubgraph
 ```
 
+### Summaries
+```@docs
+getSummary
+getSummaryGraph
+```
+
 ### Visualization and Plotting
 ```@docs
 toDot

src/DistributedFactorGraphs.jl

@@ -11,23 +11,18 @@ using LinearAlgebra
 using SparseArrays
 
 # Entities
-include("entities/AbstractTypes.jl")
+include("entities/AbstractDFG.jl")
 include("entities/DFGFactor.jl")
 include("entities/DFGVariable.jl")
+include("entities/AbstractDFGSummary.jl")
 
 export AbstractDFG
 export AbstractParams, NoSolverParams
-export DFGNode
-
-export DFGFactor
+export DFGNode, DFGVariable, DFGFactor
 export InferenceType, PackedInferenceType, FunctorInferenceType, InferenceVariable, ConvolutionObject
-
 export FunctorSingleton, FunctorPairwise, FunctorPairwiseMinimize
-
-export DFGVariable
-export label, timestamp, tags, estimates, estimate, solverData, getData, solverDataDict, internalId, smallData, bigData
-export setSolverData
-export label, data, id
+export label, timestamp, tags, estimates, estimate, data, solverData, getData, solverDataDict, setSolverData, internalId, smallData, bigData
+export DFGVariableSummary, DFGFactorSummary, AbstractDFGSummary
 
 # Services/AbstractDFG Exports
 export hasFactor, hasVariable, isInitialized, getFactorFunction, isVariable, isFactor
@@ -45,9 +40,13 @@ export getAdjacencyMatrixSparse
 # File import and export
 export saveDFG, loadDFG
 
+# Summary functions
+export getSummary, getSummaryGraph
+
 # Common includes
 include("services/AbstractDFG.jl")
 include("services/DFGVariable.jl")
+include("services/DFGFactor.jl")
 
 # Include the Graphs.jl API.
 include("GraphsDFG/GraphsDFG.jl")

src/GraphsDFG/services/GraphsDFG.jl

@@ -366,68 +366,10 @@ function getSubgraphAroundNode(dfg::GraphsDFG{P}, node::T, distance::Int64=1, in
     return addToDFG
 end
 
-"""
-    $(SIGNATURES)
-Get an adjacency matrix for the DFG, returned as a Matrix{Union{Nothing, Symbol}}.
-Rows are all factors, columns are all variables, and each cell contains either nothing or the symbol of the relating factor.
-The first row and first column are factor and variable headings respectively.
-"""
-function getAdjacencyMatrix(dfg::GraphsDFG)::Matrix{Union{Nothing, Symbol}}
-    varLabels = sort(map(v->v.label, getVariables(dfg)))
-    factLabels = sort(map(f->f.label, getFactors(dfg)))
-    vDict = Dict(varLabels .=> [1:length(varLabels)...].+1)
-
-    adjMat = Matrix{Union{Nothing, Symbol}}(nothing, length(factLabels)+1, length(varLabels)+1)
-    # Set row/col headings
-    adjMat[2:end, 1] = factLabels
-    adjMat[1, 2:end] = varLabels
-    for (fIndex, factLabel) in enumerate(factLabels)
-        factVars = getNeighbors(dfg, getFactor(dfg, factLabel))
-        map(vLabel -> adjMat[fIndex+1,vDict[vLabel]] = factLabel, factVars)
-    end
-    return adjMat
-end
-
-function getAdjacencyMatrixSparse(dfg::GraphsDFG)::Tuple{LightGraphs.SparseMatrixCSC, Vector{Symbol}, Vector{Symbol}}
-	varLabels = map(v->v.label, getVariables(dfg))
-	factLabels = map(f->f.label, getFactors(dfg))
-
-	vDict = Dict(varLabels .=> [1:length(varLabels)...])
-
-	adjMat = spzeros(Int, length(factLabels), length(varLabels))
-
-	for (fIndex, factLabel) in enumerate(factLabels)
-		factVars = getNeighbors(dfg, getFactor(dfg, factLabel))
-	    map(vLabel -> adjMat[fIndex,vDict[vLabel]] = 1, factVars)
-	end
-	return adjMat, varLabels, factLabels
-end
-
 """
     $(SIGNATURES)
 Produces a dot-format of the graph for visualization.
 """
 function toDot(dfg::GraphsDFG)::String
-    m = PipeBuffer()
-    write(m,Graphs.to_dot(dfg.g))
-    data = take!(m)
-    close(m)
-    return String(data)
-end
-
-"""
-    $(SIGNATURES)
-Produces a dot file of the graph for visualization.
-Download XDot to see the data
-
-Note
-- Default location "/tmp/dfg.dot" -- MIGHT BE REMOVED
-- Can be viewed with the `xdot` system application.
-- Based on graphviz.org
-"""
-function toDotFile(dfg::GraphsDFG, fileName::String="/tmp/dfg.dot")::Nothing
-    open(fileName, "w") do fid
-        write(fid,Graphs.to_dot(dfg.g))
-    end
-    return nothing
+	return Graphs.to_dot(dfg.g)
 end

src/LightDFG/FactorGraphs/FactorGraphs.jl

@@ -31,19 +31,22 @@ export
     filter_vertices,
     reverse
 
-import DistributedFactorGraphs: DFGNode
-const AbstractNodeType = DFGNode
+# import DistributedFactorGraphs: DFGNode
+# const AbstractNodeType = DFGNode
+import DistributedFactorGraphs: AbstractDFGVariable, AbstractDFGFactor
+const AbstractVariableType = AbstractDFGVariable
+const AbstractFactorType = AbstractDFGFactor
 
 include("BiMaps.jl")
 
-struct FactorGraph{T <: Integer,V <: AbstractNodeType, F <: AbstractNodeType} <: AbstractGraph{T}
+struct FactorGraph{T <: Integer,V <: AbstractVariableType, F <: AbstractFactorType} <: AbstractGraph{T}
     graph::SimpleGraph{T}
     labels::BiDictMap{T}
     variables::Dict{Symbol,V}
     factors::Dict{Symbol,F}
 end
 
-function FactorGraph{T, V, F}(nv::Int=100, nf::Int=100) where {T <: Integer, V <: AbstractNodeType, F <: AbstractNodeType}
+function FactorGraph{T, V, F}(nv::Int=100, nf::Int=100) where {T <: Integer, V <: AbstractVariableType, F <: AbstractFactorType}
     fadjlist = Vector{Vector{T}}()
         sizehint!(fadjlist, nv + nf)
     g = SimpleGraph{T}(0, fadjlist)
@@ -55,10 +58,10 @@ function FactorGraph{T, V, F}(nv::Int=100, nf::Int=100) where {T <: Integer, V <
     return FactorGraph{T, V, F}(g, labels, variables, factors)
 end
 
-# fg = FactorGraph{Int, AbstractNodeType, AbstractNodeType}()
+# fg = FactorGraph{Int, AbstractVariableType, AbstractFactorType}()
 
-FactorGraph() = FactorGraph{Int, AbstractNodeType, AbstractNodeType}()
-FactorGraph{V,F}() where {V <: AbstractNodeType, F <: AbstractNodeType} = FactorGraph{Int, V, F}()
+FactorGraph() = FactorGraph{Int, AbstractVariableType, AbstractFactorType}()
+FactorGraph{V,F}() where {V <: AbstractVariableType, F <: AbstractFactorType} = FactorGraph{Int, V, F}()
 
 
 function show(io::IO, g::FactorGraph)

src/LightDFG/LightDFG.jl

@@ -3,9 +3,9 @@ module LightDFGs
 using LightGraphs
 using DocStringExtensions
 
-import ...DistributedFactorGraphs: AbstractDFG, DFGNode, AbstractParams, NoSolverParams, DFGVariable, DFGFactor
+import ...DistributedFactorGraphs: AbstractDFG, DFGNode, AbstractDFGVariable, AbstractDFGFactor, AbstractDFGSummary, AbstractParams, NoSolverParams, DFGVariable, DFGFactor
 
-# import DFG functions to exstend
+# import DFG functions to extend
 import ...DistributedFactorGraphs:  setSolverParams,
                                     getInnerGraph,
                                     getFactor,

src/LightDFG/entities/LightDFG.jl

@@ -1,31 +1,49 @@
 
-mutable struct LightDFG{T <: AbstractParams, V <: DFGNode, F <:DFGNode} <: AbstractDFG
+"""
+    $(SIGNATURES)
+
+An in-memory DistributedFactorGraph based on LightGraphs.jl with parameters:
+- T: Solver parameters (defaults to `NoSolverParams()`)
+- V: Variable type
+- F: Factor type
+"""
+mutable struct LightDFG{T <: AbstractParams, V <: AbstractDFGVariable, F <:AbstractDFGFactor} <: AbstractDFG
     g::FactorGraph{Int, V, F}
     description::String
     userId::String
     robotId::String
     sessionId::String
-    #NOTE does not exist
-    # nodeCounter::Int64
-    #NOTE does not exist
-    # labelDict::Dict{Symbol, Int64}
     addHistory::Vector{Symbol} #TODO: Discuss more - is this an audit trail?
     solverParams::T # Solver parameters
 end
 
-#TODO? do we not want props such as userId, robotId, sessionId, etc...
+"""
+    $(SIGNATURES)
+
+Create an in-memory LightDFG with the following parameters:
+- T: Solver parameters (defaults to `NoSolverParams()`)
+- V: Variable type
+- F: Factor type
+"""
 function LightDFG{T,V,F}(g::FactorGraph{Int,V,F}=FactorGraph{Int,V,F}();
                            description::String="LightGraphs.jl implementation",
                            userId::String="User ID",
                            robotId::String="Robot ID",
                            sessionId::String="Session ID",
-                           params::T=NoSolverParams()) where {T <: AbstractParams, V <:DFGNode, F<:DFGNode}
+                           params::T=NoSolverParams()) where {T <: AbstractParams, V <:AbstractDFGVariable, F<:AbstractDFGFactor}
 
     LightDFG{T,V,F}(g, description, userId, robotId, sessionId, Symbol[], params)
 end
 
 # LightDFG{T}(; kwargs...) where T <: AbstractParams = LightDFG{T,DFGVariable,DFGFactor}(;kwargs...)
+"""
+    $(SIGNATURES)
 
+Create an in-memory LightDFG with the following parameters:
+- T: Solver parameters (defaults to `NoSolverParams()`)
+- V: Variable type
+- F: Factor type
+"""
 LightDFG{T}(g::FactorGraph{Int,DFGVariable,DFGFactor}=FactorGraph{Int,DFGVariable,DFGFactor}(); kwargs...) where T <: AbstractParams = LightDFG{T,DFGVariable,DFGFactor}(g; kwargs...)
 
 Base.propertynames(x::LightDFG, private::Bool=false) =