Merge pull request #1821 from JuliaRobotics/24Q1/enh/minicleanwr

Some ParametricManopt.jl cleanup while reading

Author: dehann

src/parametric/services/ParametricManopt.jl

@@ -7,14 +7,20 @@ using SparseArrays
 # using Zygote
 
 ##
-function getVarIntLabelMap(vartypeslist::OrderedDict{DataType, Vector{Symbol}})
+function getVarIntLabelMap(
+  vartypeslist::OrderedDict{DataType, Vector{Symbol}}
+)
   varlist_tuple = (values(vartypeslist)...,)
   varlabelsAP = ArrayPartition{Symbol, typeof(varlist_tuple)}(varlist_tuple)
   varIntLabel = OrderedDict(zip(varlabelsAP, collect(1:length(varlabelsAP))))
   return varIntLabel, varlabelsAP
 end
 
-function CalcFactorResidual(fg, fct::DFGFactor, varIntLabel)
+function CalcFactorResidual(
+  fg, 
+  fct::DFGFactor, 
+  varIntLabel
+)
   fac_func = getFactorType(fct)
   varOrder = getVariableOrder(fct)
 
@@ -45,7 +51,11 @@ end
   CalcFactorResidualAP
 Create an `ArrayPartition` of `CalcFactorResidual`s.
 """
-function CalcFactorResidualAP(fg::GraphsDFG, factorLabels::Vector{Symbol}, varIntLabel::OrderedDict{Symbol, Int64})
+function CalcFactorResidualAP(
+  fg::GraphsDFG, 
+  factorLabels::Vector{Symbol}, 
+  varIntLabel::OrderedDict{Symbol, Int64}
+)
   factypes, typedict, alltypes = getFactorTypesCount(getFactor.(fg, factorLabels))
 
   # skip non-numeric prior (MetaPrior)
@@ -300,6 +310,7 @@ function solve_RLM(
   faclabels = lsf(fg);
   is_sparse = true,
   finiteDiffCovariance = false,
+  solveKey::Symbol = :parametric,
   kwargs...
 )
 
@@ -312,7 +323,7 @@ function solve_RLM(
 
   #Can use varIntLabel (because its an OrderedDict), but varLabelsAP makes the ArrayPartition.
   p0 = map(varlabelsAP) do label
-    getVal(fg, label, solveKey = :parametric)[1]
+    getVal(fg, label; solveKey)[1]
   end
 
   # create an ArrayPartition{CalcFactorResidual} for faclabels
@@ -389,6 +400,7 @@ function solve_RLM_conditional(
   separators::Vector{Symbol} = setdiff(ls(fg), frontals);
   is_sparse=false,
   finiteDiffCovariance=true,
+  solveKey::Symbol = :parametric,
   kwargs...
 )
   is_sparse && error("Sparse solve_RLM_conditional not supported yet")
@@ -420,7 +432,7 @@ function solve_RLM_conditional(
   all_varlabelsAP = ArrayPartition((frontal_varlabelsAP.x..., separator_varlabelsAP.x...))
 
   all_points = map(all_varlabelsAP) do label
-    getVal(fg, label, solveKey = :parametric)[1]
+    getVal(fg, label; solveKey)[1]
   end
 
   p0 = ArrayPartition(all_points.x[1:length(frontal_varlabelsAP.x)])
@@ -537,7 +549,7 @@ function autoinitParametric!(
         )
       )
     end
-    M, vartypeslist, lm_r, Σ = solve_RLM_conditional(dfg, [initme], initfrom; kwargs...)
+    M, vartypeslist, lm_r, Σ = solve_RLM_conditional(dfg, [initme], initfrom; solveKey, kwargs...)
 
     val = lm_r[1]
     vnd.val[1] = val
@@ -549,8 +561,8 @@ function autoinitParametric!(
     vnd.initialized = true
     #fill in ppe as mean
     Xc::Vector{Float64} = collect(getCoordinates(getVariableType(xi), val))
-    ppe = MeanMaxPPE(:parametric, Xc, Xc, Xc)
-    getPPEDict(xi)[:parametric] = ppe
+    ppe = MeanMaxPPE(solveKey, Xc, Xc, Xc)
+    getPPEDict(xi)[solveKey] = ppe
 
     result = vartypeslist, lm_r