Merge pull request #168 from JuliaRobotics/maintenance/removewarns

fix and remove more warnings

examples/squarefixedpoint.jl

@@ -182,7 +182,7 @@ end
 # frequencies of interest
 
 FR = range(0.5/(2pi),stop=3.0/(2pi), length=8)
-# FR = linspace(0.5/(2pi),3.0/(2pi), 8)
+# FR = range(0.5/(2pi),stop=3.0/(2pi), length=8)
 
 
 mc = 3

src/DispatchPackedConversions.jl

@@ -177,7 +177,7 @@ end
 
 function convert(::Type{PackedFunctionNodeData{P}}, d::FunctionNodeData{T}) where {P <: PackedInferenceType, T <: FunctorInferenceType}
   # println("convert(::Type{PackedFunctionNodeData{$P}}, d::FunctionNodeData{$T})")
-  warn("convert GenericWrapParam is deprecated, use CommonConvWrapper instead.")
+  @warn "convert GenericWrapParam is deprecated, use CommonConvWrapper instead."
   mhstr = packmultihypo(d.fnc)
   return PackedFunctionNodeData(d.fncargvID, d.eliminated, d.potentialused, d.edgeIDs,
           string(d.frommodule), convert(P, d.fnc.usrfnc!), mhstr)
@@ -198,7 +198,7 @@ function convert(
             ::Type{IncrementalInference.GenericFunctionNodeData{IncrementalInference.CommonConvWrapper{F},Symbol}},
             d::IncrementalInference.GenericFunctionNodeData{P,String} ) where {F <: FunctorInferenceType, P <: PackedInferenceType}
   #
-  # warn("Unpacking Option 2, F=$(F), P=$(P)")
+  # @warn "Unpacking Option 2, F=$(F), P=$(P)"
   usrfnc = convert(F, d.fnc)
   # @show d.multihypo
   mhcat = parsemultihypostr(d.multihypo)
@@ -254,7 +254,7 @@ end
 function encodePackedType(topackdata::GenericFunctionNodeData{T, <:AbstractString}) where {T <: PackedInferenceType}
   error("IncrementalInference.encodePackedType(::FunctionNodeData{T, <:AbstractString}): Unknown packed type encoding T=$(T) of $(topackdata)")
   # @show T, typeof(topackdata)
-  # warn("Yes, its packed!")
+  # @warn "Yes, its packed!"
   # fnctype = getfnctype(topackdata)
   # @show fnc = getfield(getmodule(fnctype), Symbol("Packed$(getname(fnctype))"))
   # convert(PackedFunctionNodeData{T}, topackdata)

src/FactorGraph01.jl

@@ -119,7 +119,7 @@ function updateFullVert!(fgl::FactorGraph, exvert::ExVertex;
             api::DataLayerAPI=IncrementalInference.dlapi,
             updateMAPest::Bool=false  )
   #
-  warn("use of updateFullVert! should be clarified for local or remote operations.")
+  @warn "use of updateFullVert! should be clarified for local or remote operations."
   api.updatevertex!(fgl, exvert, updateMAPest=updateMAPest)
 end
 
@@ -139,7 +139,7 @@ function setDefaultNodeData!(v::Graphs.ExVertex,
   data = nothing
   if initialized
     if size(initval,2) < N && size(initval, 1) == dims
-      warn("setDefaultNodeData! -- deprecated use of stdev.")
+      @warn "setDefaultNodeData! -- deprecated use of stdev."
       p = kde!(initval,diag(stdev));
       pN = resample(p,N)
     elseif size(initval,2) < N && size(initval, 1) != dims
@@ -149,7 +149,7 @@ function setDefaultNodeData!(v::Graphs.ExVertex,
       pN = kde!(initval)
     end
     # dims = size(initval,1) # rows indicate dimensions
-    sp = Int[0;] #round.(Int,linspace(dodims,dodims+dims-1,dims))
+    sp = Int[0;] #round.(Int,range(dodims,stop=dodims+dims-1,length=dims))
     gbw = getBW(pN)[:,1]
     gbw2 = Array{Float64}(length(gbw),1)
     gbw2[:,1] = gbw[:]
@@ -283,7 +283,7 @@ end
 
 
 function getVal(vA::Array{Graphs.ExVertex,1})
-  warn("getVal(::Vector{ExVertex}) is obsolete, use getVal.(ExVertex) instead.")
+  @warn "getVal(::Vector{ExVertex}) is obsolete, use getVal.(ExVertex) instead."
   len = length(vA)
   vals = Array{Array{Float64,2},1}()
   cols = Array{Int,1}()
@@ -697,7 +697,7 @@ function addBayesNetVerts!(fg::FactorGraph, elimOrder::Array{Int,1})
       vert.attributes["data"].BayesNetVertID = p
       localapi.updatevertex!(fg, vert)
     else
-      warn("addBayesNetVerts -- something is very wrong, should not have a Bayes net vertex")
+      @warn "addBayesNetVerts -- something is very wrong, should not have a Bayes net vertex"
     end
   end
 end
@@ -742,7 +742,7 @@ function rmVarFromMarg(fgl::FactorGraph, fromvert::Graphs.ExVertex, gm::Array{Gr
           edge = localapi.getedge(fgl, id)
           if edge != nothing # hack to avoid dictionary use case
             if edge.SourceVertex.exVertexId == m.index || edge.DestVertex.exVertexId == m.index
-              warn("removing edge $(edge.neo4jEdgeId), between $(m.index) and $(n.index)")
+              @warn "removing edge $(edge.neo4jEdgeId), between $(m.index) and $(n.index)"
               localapi.deleteedge!(fgl, edge)
               m.attributes["data"].edgeIDs = alleids[[collect(1:(i-1));collect((i+1):length(alleids))]]
               localapi.updatevertex!(fgl, m)
@@ -753,7 +753,7 @@ function rmVarFromMarg(fgl::FactorGraph, fromvert::Graphs.ExVertex, gm::Array{Gr
     end
     # if 0 edges, delete the marginal
     if length(localapi.outneighbors(fgl, m)) <= 1
-      warn("removing vertex id=$(m.index)")
+      @warn "removing vertex id=$(m.index)"
       localapi.deletevertex!(fgl,m)
     end
   end
@@ -871,7 +871,7 @@ function writeGraphPdf(fgl::FactorGraph;
   try
     viewerapp != nothing ? (@async run(`$(viewerapp) $(filepath)`)) : nothing
   catch e
-    warn("not able to show $(filepath) with viewerapp=$(viewerapp). Exception e=$(e)")
+    @warn "not able to show $(filepath) with viewerapp=$(viewerapp). Exception e=$(e)"
   end
   nothing
 end

src/FactorGraphTypes.jl

@@ -147,7 +147,7 @@ mutable struct VariableNodeData
   dontmargin::Bool
   VariableNodeData() = new()
   function VariableNodeData(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11)
-    warn("Deprecated use of VariableNodeData(11 param), use 13 parameters instead")
+    @warn "Deprecated use of VariableNodeData(11 param), use 13 parameters instead"
     new(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11, nothing, true, false, false) # TODO ensure this is initialized true is working for most cases
   end
   VariableNodeData(x1::Array{Float64,2},
@@ -385,7 +385,7 @@ function graphsGetEdge(fgl::FactorGraph, id::Int)
 end
 
 function graphsDeleteVertex!(fgl::FactorGraph, vert::Graphs.ExVertex)
-  warn("graphsDeleteVertex! -- not deleting Graphs.jl vertex id=$(vert.index)")
+  @warn "graphsDeleteVertex! -- not deleting Graphs.jl vertex id=$(vert.index)"
   nothing
 end
 

src/JunctionTree.jl

@@ -123,7 +123,6 @@ end
 function newPotential(tree::BayesTree, fg::FactorGraph, var::Int, prevVar::Int, p::Array{Int,1})
     firvert = localapi.getvertex(fg,var)
     if (length(getData(firvert).separator) == 0)
-      # warn("newPotential -- sep length is 0")
       if (length(tree.cliques) == 0)
         addClique!(tree, fg, var)
       else

src/SolveTree01.jl

@@ -262,7 +262,7 @@ function productbelief(fg::FactorGraph,
     @info "[drct]"
     pGM = getPoints(dens[1])
   else
-    warn("Unknown density product on vertid=$(vertid), lennonp=$(lennonp), lenpart=$(lenpart)")
+    @warn "Unknown density product on vertid=$(vertid), lennonp=$(lennonp), lenpart=$(lenpart)"
     pGM = Array{Float64,2}(0,1)
   end
 

src/SolverUtilities.jl

@@ -122,15 +122,14 @@ function numericRootGenericRandomizedFnc!(
       ccwl.cpt[Threads.threadid()].Y[1:ccwl.xDim] = ( r ).zero
     else
       # TODO print this output as needed
-      # warn("got NaN, ccwl.cpt[Threads.threadid()].particleidx = $(ccwl.cpt[Threads.threadid()].particleidx), r=$(r)")
       str = "ccw.thrid_=$(Threads.threadid()), got NaN, ccwl.cpt[Threads.threadid()].particleidx = $(ccwl.cpt[Threads.threadid()].particleidx), r=$(r)\n"
       ccall(:jl_, Nothing, (Any,), str)
       ccall(:jl_, Nothing, (Any,), ccwl.usrfnc!)
       @info str
       for thatlen in 1:length(ccwl.params)
         str = "thatlen=$thatlen, ccwl.params[thatlen][:, ccwl.cpt[Threads.threadid()].particleidx]=$(ccwl.params[thatlen][:, ccwl.cpt[Threads.threadid()].particleidx])\n"
         ccall(:jl_, Nothing, (Any,), str)
-        warn(str)
+        @warn str
       end
     end
   elseif ccwl.partial
@@ -171,7 +170,7 @@ Update the frozen node
 """
 function setfreeze!(fgl::FactorGraph, sym::Symbol)
   if !isInitialized(fgl, sym)
-    warn("Vertex $(sym) is not initialized, and won't be frozen at this time.")
+    @warn "Vertex $(sym) is not initialized, and won't be frozen at this time."
     return nothing
   end
   vert = getVert(fgl, sym)
@@ -192,7 +191,7 @@ Future:
 """
 function fifoFreeze!(fgl::FactorGraph)
   if fgl.qfl == 0
-    warn("Quasi fixed-lag is enabled buyt QFL horizon is zero. Please set a valid window with FactoGraph.qfl")
+    @warn "Quasi fixed-lag is enabled buyt QFL horizon is zero. Please set a valid window with FactoGraph.qfl"
   end
 
   tofreeze = fgl.fifo[1:(end-fgl.qfl)]

test/fourdoortest.jl

@@ -50,7 +50,7 @@ addFactor!(fg,[:x6;:x7], LinearConditional( Normal(60.0,2.0)) )
 
 ensureAllInitialized!(fg)
 
-mlc = MixturePrior(Normal.(doors[1,:], bws[1]), 0.25*ones(4))
+global mlc = MixturePrior(Normal.(doors[1,:], bws[1]), 0.25*ones(4))
 
 # getSample(mlc)
 
@@ -59,7 +59,7 @@ addFactor!(fg,[:x7], mlc )
 
 
 # HMM computed ground truth, extended for 7 poses with landmark
-gt = Dict{Symbol, Array{Float64,2}}()
+global gt = Dict{Symbol, Array{Float64,2}}()
 gt[:x0]=reshape(Float64[0.0;1.97304 ],2,1) # -0.0342366
 gt[:x2]=reshape(Float64[50.0; 2.83153 ],2,1) # 49.8797
 gt[:x3]=reshape(Float64[100.0; 1.65557 ],2,1) # 99.8351
@@ -70,7 +70,7 @@ gt[:x7]=reshape(Float64[300.0; 2.14353 ],2,1) # 298.467
 gt[:l1]=reshape(Float64[165.0; 1.17284 ],2,1) # 164.102
 
 
-tree = prepBatchTree!(fg, drawpdf=false);
+global tree = prepBatchTree!(fg, drawpdf=false);
 
 # list vertices in fg
 @show xx,ll = ls(fg)

test/testBayesTreeChordalProp.jl

@@ -1,44 +1,44 @@
 using IncrementalInference
-# using KernelDensityEstimate, Gadfly # for vstack
 
-fg = emptyFactorGraph()
 
-N=100
+global fg = emptyFactorGraph()
 
-doors = [-100.0;0.0;100.0;300.0]'
-cov = [3.0]
+global N=100
 
+global doors = [-100.0;0.0;100.0;300.0]'
+global cov = [3.0]
 
-v1 = addNode!(fg,:x1,ContinuousScalar,N=N)
-f1  = addFactor!(fg,[v1], Obsv2(doors, cov', [1.0]))
+
+global v1 = addNode!(fg,:x1,ContinuousScalar,N=N)
+global f1  = addFactor!(fg,[v1], Obsv2(doors, cov', [1.0]))
 
 # tem = 2.0*randn(1,N)+getVal(v1)+50.0
-v2 = addNode!(fg,:x2, ContinuousScalar, N=N)
+global v2 = addNode!(fg,:x2, ContinuousScalar, N=N)
 addFactor!(fg,[v1;v2],Odo([50.0]',[2.0]',[1.0]))
 
-v3=addNode!(fg,:x3,ContinuousScalar, N=N)
+global v3=addNode!(fg,:x3,ContinuousScalar, N=N)
 addFactor!(fg,[v2;v3],Odo([50.0]',[4.0]',[1.0]))
 
-v4=addNode!(fg,:x4,ContinuousScalar, N=N)
+global v4=addNode!(fg,:x4,ContinuousScalar, N=N)
 addFactor!(fg,[v3;v4],Odo([50.0]',[2.0]',[1.0]))
 
-v5=addNode!(fg,:x5,ContinuousScalar, N=N)
+global v5=addNode!(fg,:x5,ContinuousScalar, N=N)
 addFactor!(fg,[v4;v5],Odo([50.0]',[2.0]',[1.0]))
 
 
-l1=addNode!(fg, :l1, ContinuousScalar, N=N)
+global l1=addNode!(fg, :l1, ContinuousScalar, N=N)
 addFactor!(fg, [v3,l1], Ranged([64.0],[0.5],[1.0]))
 
-l2=addNode!(fg, :l2, ContinuousScalar, N=N)
+global l2=addNode!(fg, :l2, ContinuousScalar, N=N)
 addFactor!(fg, [v4,l2], Ranged([16.0],[0.5],[1.0]))
 
-l3=addNode!(fg, :l3, ContinuousScalar, N=N)
+global l3=addNode!(fg, :l3, ContinuousScalar, N=N)
 addFactor!(fg, [v5,l3], Ranged([16.0],[0.5],[1.0]))
 
 
 # writeGraphPdf(fg);
 
-tree = prepBatchTree!(fg,drawpdf=true);
+global tree = prepBatchTree!(fg,drawpdf=true);
 # run(`evince bt.pdf`)
 
 # do belief propagation inference over tree once