Example using a blend of hard type metadata and dicts for adding new …

src/DistributedFactorGraphs.jl

@@ -23,13 +23,14 @@ export InferenceType, PackedInferenceType, FunctorInferenceType, InferenceVariab
 export FunctorSingleton, FunctorPairwise, FunctorPairwiseMinimize
 export label, timestamp, tags, estimates, estimate, data, softtype, solverData, getData, solverDataDict, setSolverData, internalId, smallData, bigData
 export DFGVariableSummary, DFGFactorSummary, AbstractDFGSummary
+export PointParametricEstimates
 
 # Services/AbstractDFG Exports
 export hasFactor, hasVariable, isInitialized, getFactorFunction, isVariable, isFactor
 export updateGraphSolverData!
 
 # Solver (IIF) Exports
-export VariableNodeData, PackedVariableNodeData, VariableEstimate
+export VariableNodeData, PackedVariableNodeData
 export GenericFunctionNodeData#, FunctionNodeData
 export getSerializationModule, setSerializationModule!
 export pack, unpack

src/entities/DFGVariable.jl

@@ -78,24 +78,16 @@ mutable struct PackedVariableNodeData
 end
 
 
-abstract type AbstractVariableEstimate end
 """
     $TYPEDEF
 
 Data container to store Parameteric Point Estimate (PPE) from a variety of types.
-
-Notes
-- `ppeType` is something like `:max/:mean/:modefit` etc.
-- `solveKey` is from super-solve concept, starting with `:default`,
-- `estimate` is the actual numerical estimate value,
-- Additional information such as how the data is represented (ie softtype) is stored alongside this data container in the `DFGVariableSummary` container.
-"""
-struct VariableEstimate <: AbstractVariableEstimate
-  solverKey::Symbol
-  ppeType::Symbol
-  estimate::Vector{Float64}
+"""
+struct PointParametricEstimates
+  # Put other fields here.
+  estimates::Dict{Symbol, Vector{Float64}}
   lastUpdatedTimestamp::DateTime
-  VariableEstimate(solverKey::Symbol, type::Symbol, estimate::Vector{Float64}, lastUpdatedTimestamp::DateTime=now()) = new(solverKey, type, estimate, lastUpdatedTimestamp)
+  PointParametricEstimates(lastUpdatedTimestamp::DateTime=now()) = new(Dict{Symbol, Vector{Float64}}(), lastUpdatedTimestamp)
 end
 
 """
@@ -106,15 +98,15 @@ mutable struct DFGVariable <: AbstractDFGVariable
     label::Symbol
     timestamp::DateTime
     tags::Vector{Symbol}
-    estimateDict::Dict{Symbol, Dict{Symbol, <: AbstractVariableEstimate}}
+    estimateDict::Dict{Symbol, PointParametricEstimates}
     solverDataDict::Dict{Symbol, VariableNodeData}
     smallData::Dict{String, String}
     bigData::Any
     ready::Int
     backendset::Int
     _internalId::Int64
-    DFGVariable(label::Symbol, _internalId::Int64) = new(label, now(), Symbol[], Dict{Symbol, Dict{Symbol, VariableEstimate}}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), nothing, 0, 0, _internalId)
-    DFGVariable(label::Symbol) = new(label, now(), Symbol[], Dict{Symbol, VariableEstimate}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), nothing, 0, 0, 0)
+    DFGVariable(label::Symbol, _internalId::Int64) = new(label, now(), Symbol[], Dict{Symbol, PointParametricEstimates}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), nothing, 0, 0, _internalId)
+    DFGVariable(label::Symbol) = new(label, now(), Symbol[], Dict{Symbol, PointParametricEstimates}(), Dict{Symbol, VariableNodeData}(:default => VariableNodeData()), Dict{String, String}(), nothing, 0, 0, 0)
 end
 
 # Accessors
@@ -166,7 +158,7 @@ mutable struct DFGVariableSummary <: AbstractDFGVariable
     label::Symbol
     timestamp::DateTime
     tags::Vector{Symbol}
-    estimateDict::Dict{Symbol, Dict{Symbol, <:AbstractVariableEstimate}}
+    estimateDict::Dict{Symbol, PointParametricEstimates}
     softtypename::Symbol
     _internalId::Int64
 end

src/services/DFGVariable.jl

@@ -102,6 +102,12 @@ function ==(a::VariableNodeData,b::VariableNodeData, nt::Symbol=:var)
   return DistributedFactorGraphs.compare(a,b)
 end
 
+function ==(a::PointParametricEstimates, b::PointParametricEstimates)::Bool
+  a.estimates != b.estimates && return false
+  a.lastUpdatedTimestamp != b.lastUpdatedTimestamp && return false
+  return true
+end
+
 function convert(::Type{DFGVariableSummary}, v::DFGVariable)
     return DFGVariableSummary(v.label, v.timestamp, deepcopy(v.tags), deepcopy(v.estimateDict), Symbol(typeof(getSofttype(v))), v._internalId)
 end

test/LightDFGSummaryTypes.jl

@@ -1,6 +1,6 @@
 dfg = LightDFG{NoSolverParams, DFGVariableSummary, DFGFactorSummary}()
 
-DistributedFactorGraphs.DFGVariableSummary(label::Symbol) = DFGVariableSummary(label, DistributedFactorGraphs.now(), Symbol[], Dict{Symbol, VariableEstimate}(), :NA, 0)
+DistributedFactorGraphs.DFGVariableSummary(label::Symbol) = DFGVariableSummary(label, DistributedFactorGraphs.now(), Symbol[], Dict{Symbol, PointParametricEstimates}(), :NA, 0)
 
 DistributedFactorGraphs.DFGFactorSummary(label::Symbol) = DFGFactorSummary(label, Symbol[], 0, Symbol[])
 
@@ -124,30 +124,24 @@ end
     var = getVariable(dfg, :a)
     #make a copy and simulate external changes
     newvar = deepcopy(var)
-    estimates(newvar)[:default] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [100.0]),
-        :mean => VariableEstimate(:default, :mean, [50.0]),
-        :modefit => VariableEstimate(:default, :modefit, [75.0]))
+    ppe = PointParametricEstimates()
+    ppe.estimates[:max] = [100.0]
+    ppe.estimates[:mean] = [50.0]
+    ppe.estimates[:modefit] = [75.0]
+    estimates(newvar)[:default] = ppe
     #update
     updateVariableSolverData!(dfg, newvar)
-    #TODO maybe implement ==; @test newvar==var
-    Base.:(==)(varest1::VariableEstimate, varest2::VariableEstimate) = begin
-        varest1.lastUpdatedTimestamp == varest2.lastUpdatedTimestamp || return false
-        varest1.ppeType == varest2.ppeType || return false
-        varest1.solverKey == varest2.solverKey || return false
-        varest1.estimate == varest2.estimate || return false
-        return true
-    end
     #For now spot check
     # @test solverDataDict(newvar) == solverDataDict(var)
     @test estimates(newvar) == estimates(var)
 
     # Delete :default and replace to see if new ones can be added
     delete!(estimates(newvar), :default)
-    estimates(newvar)[:second] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [10.0]),
-        :mean => VariableEstimate(:default, :mean, [5.0]),
-        :ppe => VariableEstimate(:default, :ppe, [7.0]))
+    ppe = PointParametricEstimates()
+    ppe.estimates[:max] = [10.0]
+    ppe.estimates[:mean] = [5.0]
+    ppe.estimates[:modefit] = [7.0]
+    estimates(newvar)[:second] = ppe
 
     # Persist to the original variable.
     updateVariableSolverData!(dfg, newvar)

test/interfaceTests.jl

@@ -146,30 +146,24 @@ end
     var = getVariable(dfg, :a)
     #make a copy and simulate external changes
     newvar = deepcopy(var)
-    estimates(newvar)[:default] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [100.0]),
-        :mean => VariableEstimate(:default, :mean, [50.0]),
-        :modefit => VariableEstimate(:default, :modefit, [75.0]))
+    ppe = PointParametricEstimates()
+    ppe.estimates[:max] = [100.0]
+    ppe.estimates[:mean] = [50.0]
+    ppe.estimates[:modefit] = [75.0]
+    estimates(newvar)[:default] = ppe
     #update
     updateVariableSolverData!(dfg, newvar)
-    #TODO maybe implement ==; @test newvar==var
-    Base.:(==)(varest1::VariableEstimate, varest2::VariableEstimate) = begin
-        varest1.lastUpdatedTimestamp == varest2.lastUpdatedTimestamp || return false
-        varest1.ppeType == varest2.ppeType || return false
-        varest1.solverKey == varest2.solverKey || return false
-        varest1.estimate == varest2.estimate || return false
-        return true
-    end
     #For now spot check
     @test solverDataDict(newvar) == solverDataDict(var)
     @test estimates(newvar) == estimates(var)
 
     # Delete :default and replace to see if new ones can be added
     delete!(estimates(newvar), :default)
-    estimates(newvar)[:second] = Dict{Symbol, VariableEstimate}(
-        :max => VariableEstimate(:default, :max, [10.0]),
-        :mean => VariableEstimate(:default, :mean, [5.0]),
-        :modefit => VariableEstimate(:default, :modefit, [7.0]))
+    ppe = PointParametricEstimates()
+    ppe.estimates[:max] = [10.0]
+    ppe.estimates[:mean] = [5.0]
+    ppe.estimates[:modefit] = [7.0]
+    estimates(newvar)[:second] = ppe
 
     # Persist to the original variable.
     updateVariableSolverData!(dfg, newvar)