Merge pull request #1544 from JuliaRobotics/22Q2/maint/initVariable

initManual! -> initVariable! for deprecation

Author: Affie

src/DeconvUtils.jl

@@ -154,7 +154,7 @@ function approxDeconv(dfg::AbstractDFG,
   lbls = getVariableOrder(fct)
   for lb in lbls
     exists(tfg, lb) ? nothing : addVariable!(tfg, lb, getVariableType(dfg, lb))
-    initManual!(tfg, lb, getBelief(dfg, lb, solveKey))
+    initVariable!(tfg, lb, getBelief(dfg, lb, solveKey))
   end
 
   # add factor type requested by user
@@ -216,7 +216,7 @@ function deconvSolveKey(dfg::AbstractDFG,
   if !exists(tfg, refSym_)
     addVariable!(tfg, refSym_, refVarType)
   end
-  initManual!(tfg, refSym_, Xref)
+  initVariable!(tfg, refSym_, Xref)
 
   # add the second "test" variable
   tstVarType = getVariableType(dfg, tstSym)
@@ -225,7 +225,7 @@ function deconvSolveKey(dfg::AbstractDFG,
   if !exists(tfg, tstSym_)
     addVariable!(tfg, tstSym_, tstVarType)
   end
-  initManual!(tfg, tstSym_, Xtst)
+  initVariable!(tfg, tstSym_, Xtst)
 
   # add the new dummy factor with default manifold for computations
   fctType = selectFactorType(refVarType, tstVarType)

src/SolverUtilities.jl

@@ -150,7 +150,7 @@ function _buildGraphByFactorAndTypes!(fct::AbstractFactor,
     exists(dfg, vars[s_]) ? nothing : addVariable!(dfg, vars[s_],  vTyp)
     # set the numerical values if available
     # TODO allow pts to come in as full MKD beliefs, not just one point
-    ((0 < length(pts)) && (pts[s_] isa Nothing)) ? nothing : initManual!(dfg,  vars[s_], [pts[s_],], solveKey, bw=ones(getDimension(vTyp)))
+    ((0 < length(pts)) && (pts[s_] isa Nothing)) ? nothing : initVariable!(dfg,  vars[s_], [pts[s_],], solveKey, bw=ones(getDimension(vTyp)))
   end
   # if newFactor then add the factor on vars, else assume only one existing factor between vars
   _dfgfct = newFactor ? addFactor!(dfg, vars, fct, graphinit=graphinit, _blockRecursion=_blockRecursion) : getFactor(dfg, intersect((ls.(dfg, vars))...)[1] )

src/TreeMessageUtils.jl

@@ -226,7 +226,7 @@ function addLikelihoodsDifferential!( msgs::LikelihoodMessage,
   #     addVariable!(tfg, label, val.variableType)
   #     @debug "New variable added to subfg" _group=:check_addLHDiff #TODO JT remove debug. 
   #   end
-  #   initManual!(tfg, label, manikde!(val))
+  #   initVariable!(tfg, label, manikde!(val))
   # end
 
   # # list all variables in order of dimension size
@@ -275,7 +275,7 @@ function addLikelihoodsDifferentialCHILD!(cliqSubFG::AbstractDFG,
       addVariable!(tfg, label, getVariableType(cliqSubFG, label))
       @debug "New variable added to subfg" _group=:check_addLHDiff #TODO JT remove debug. 
     end
-    initManual!(tfg, label, getBelief(cliqSubFG, label, solveKey), solveKey)
+    initVariable!(tfg, label, getBelief(cliqSubFG, label, solveKey), solveKey)
   end
 
   # list all variables in order of dimension size

src/services/ApproxConv.jl

@@ -118,7 +118,7 @@ function approxConvBelief(dfg::AbstractDFG,
     getPoints(pts1Bel)
   end
   # didn't return early so shift focus to using `tfg` more intensely
-  initManual!(tfg, varLbls[1], pts)
+  initVariable!(tfg, varLbls[1], pts)
   # use in combination with setPPE and setPPEmethod keyword arguments
   ppemethod = setPPEmethod === nothing ? MeanMaxPPE : setPPEmethod
   !setPPE ? nothing : setPPE!(tfg, varLbls[1], solveKey, ppemethod)
@@ -130,7 +130,7 @@ function approxConvBelief(dfg::AbstractDFG,
       fct = getFactor(dfg, path[idx])
       addFactor!(tfg, fct)
       ptsBel = approxConvBelief(tfg, fct, path[idx+1]; solveKey, N, skipSolve)
-      initManual!(tfg, path[idx+1], ptsBel)
+      initVariable!(tfg, path[idx+1], ptsBel)
       !setPPE ? nothing : setPPE!(tfg, path[idx+1], solveKey, ppemethod)
     end
   end

src/services/GraphInit.jl

@@ -14,7 +14,7 @@ Development Notes
 
 Related
 
-doautoinit!, initManual!, isInitialized, isMultihypo
+doautoinit!, initVariable!, isInitialized, isMultihypo
 """
 function factorCanInitFromOtherVars(dfg::AbstractDFG,
                                     fct::Symbol,

test/testBasicForwardConvolve.jl

@@ -18,7 +18,7 @@ function forwardConvolve(X0::AbstractVector{P}, model) where P
   fg = initfg()
 
   addVariable!(fg, :x0, ContinuousScalar)
-  initManual!(fg, :x0, X0)
+  initVariable!(fg, :x0, X0)
   addVariable!(fg, :x1, ContinuousScalar)
 
   addFactor!(fg, [:x0;:x1], model)

test/testDERelative.jl

@@ -56,7 +56,7 @@ for i in 1:3
                         problemType=ODEProblem )
   #
   addFactor!( fg, [prev;nextSym], oder, graphinit=false )
-  initManual!(fg, nextSym, [zeros(1) for _ in 1:100])
+  initVariable!(fg, nextSym, [zeros(1) for _ in 1:100])
 
   prev = nextSym
 end
@@ -121,7 +121,7 @@ sl = DifferentialEquations.solve(oder_.forwardProblem)
 
 tfg = initfg()
 pts_ = approxConv(fg, :x0f1, :x3, setPPE=true, tfg=tfg)
-# initManual!(tfg, :x3, pts)
+# initVariable!(tfg, :x3, pts)
 
 
 ##
@@ -242,11 +242,11 @@ pts_ = approxConv(fg, :x0x1f1, :x0)
 
 tfg = initfg()
 for s in ls(fg)
-  initManual!(fg, s, [zeros(2) for _ in 1:100])
+  initVariable!(fg, s, [zeros(2) for _ in 1:100])
 end
 
 pts = approxConv(fg, :x0f1, :x7, setPPE=true, tfg=tfg)
-# initManual!(tfg, :x7, pts)
+# initVariable!(tfg, :x7, pts)
 
 
 
@@ -416,7 +416,7 @@ pts_ = approxConv(fg, :x0x1ωβf1, :x0)
 
 tfg = initfg()
 for s in ls(fg)
-  initManual!(fg, s, [zeros(2) for _ in 1:100])
+  initVariable!(fg, s, [zeros(2) for _ in 1:100])
 end
 
 # must initialize the parameters

test/testEuclidDistance.jl

@@ -112,7 +112,7 @@ N=100
 points = [[100.0;0.0],[0.0;100.0]]
 fg = IIF.generateGraph_EuclidDistance(points)
 
-# initManual!(fg, :l1, [1000.0.*randn(2) for _ in 1:100])
+# initVariable!(fg, :l1, [1000.0.*randn(2) for _ in 1:100])
 
 # check regular full solution produces two modes
 
@@ -275,6 +275,6 @@ end
 
 ##
 
-# initManual!(sfg, :l1, pts)
+# initVariable!(sfg, :l1, pts)
 # pts = approxConv(sfg, :x2l1f1, :l1)
 # plotKDE(manikde!(ContinuousEuclid{2}, pts))

test/testMultimodal1D.jl

@@ -149,19 +149,19 @@ end
 #
 #
 # stuff = localProduct(fg,:lm2)
-# initManual!(fg,:lm2, stuff[1]); p1 = plotKDE(stuff[1], title="lm2")
+# initVariable!(fg,:lm2, stuff[1]); p1 = plotKDE(stuff[1], title="lm2")
 #
 # stuff = localProduct(fg,:lm1)
-# initManual!(fg,:lm1, stuff[1]); p2 = plotKDE(stuff[1], title="lm1")
+# initVariable!(fg,:lm1, stuff[1]); p2 = plotKDE(stuff[1], title="lm1")
 #
 # stuff = localProduct(fg,:lp2)
-# initManual!(fg,:lp2, stuff[1]); p3 = plotKDE(stuff[1], title="lp2")
+# initVariable!(fg,:lp2, stuff[1]); p3 = plotKDE(stuff[1], title="lp2")
 #
 # stuff = localProduct(fg,:lp1)
-# initManual!(fg,:lp1, stuff[1]); p4 = plotKDE(stuff[1], title="lp1")
+# initVariable!(fg,:lp1, stuff[1]); p4 = plotKDE(stuff[1], title="lp1")
 #
 # stuff = localProduct(fg,:x1)
-# initManual!(fg,:x1, stuff[1]); p5 = plotKDE(stuff[1], title="x1")
+# initVariable!(fg,:x1, stuff[1]); p5 = plotKDE(stuff[1], title="x1")
 #
 # h1 = hstack(p1,p2,p3,p4,p5)
 #

test/testProductReproducable.jl

@@ -70,12 +70,12 @@ for i in 1:10
   pts = approxConv(fg, :abf1, :b)
   B_ = manikde!(ContinuousScalar, pts)
   # plotKDE([B_; B])
-  initManual!(fg, :b, B_)
+  initVariable!(fg, :b, B_)
 
   pts = approxConv(fg, :abf1, :a)
   A_ = manikde!(ContinuousScalar, pts)
   # plotKDE([A_; A])
-  initManual!(fg, :a, A_)
+  initVariable!(fg, :a, A_)
 end
 
 A_ = getBelief(fg, :a)