Merge pull request #1818 from JuliaRobotics/master

release v0.35.3-rc1

NEWS.md

@@ -24,6 +24,7 @@ The list below highlights breaking changes according to normal semver workflow -
 - Restore `DifferentialEquation.jl` factor `DERelative` functionality and tests that were suppressed in a previous upgrade (#1774, #1777).
 - Restore previously suppressed tests (#1781, #1721, #1780)
 - Improve DERelative factor on-manifold operations (#1775, #1802, #1803).
+- Fixed a typo via deprecation, `solveFactorParametric` replaces `solveFactorParameteric`.
 
 # Changes in v0.34
 
@@ -110,7 +111,7 @@ The list below highlights breaking changes according to normal semver workflow -
 - Upstream `calcHelix_T` canonical generator utility from RoME.jl.
 - Deserialization of factors with DFG needs new API and change of solverData and CCW type in factor.
 - Deprecate use of `getParametricMeasurement` and use `getMeasurementParametric` instead, and add `<:AbstractManifold` to API.
-- Deprecate use of `solveBinaryFactorParameteric`, instead use `solveFactorParameteric`.
+- Deprecate use of `solveBinaryFactorParameteric`, instead use `solveFactorParametric`.
 - Deprecating `approxConvBinary`, use `approxConvBelief` instead.
 - Removing obsolete `approxConvCircular`, use `approxConvBelief` instead.
 - `getSample` should return a single sample and no longer takes the N(number of samples) parameter.

Project.toml

@@ -2,7 +2,7 @@ name = "IncrementalInference"
 uuid = "904591bb-b899-562f-9e6f-b8df64c7d480"
 keywords = ["MM-iSAMv2", "Bayes tree", "junction tree", "Bayes network", "variable elimination", "graphical models", "SLAM", "inference", "sum-product", "belief-propagation"]
 desc = "Implements the Multimodal-iSAMv2 algorithm."
-version = "0.35.2"
+version = "0.35.3"
 
 [deps]
 ApproxManifoldProducts = "9bbbb610-88a1-53cd-9763-118ce10c1f89"
@@ -78,8 +78,9 @@ FiniteDifferences = "0.12"
 FunctionalStateMachine = "0.2.9"
 JSON3 = "1"
 KernelDensityEstimate = "0.5.6"
+LinearAlgebra = "1.10"
 ManifoldDiff = "0.3"
-Manifolds = "0.9"
+Manifolds = "0.9.12"
 ManifoldsBase = "0.15"
 Manopt = "0.4.40"
 MetaGraphs = "0.7"
@@ -91,12 +92,12 @@ RecursiveArrayTools = "2.31.1, 3"
 Reexport = "1"
 SparseDiffTools = "2"
 StaticArrays = "1"
-Statistics = "1"
+Statistics = "1.10"
 StatsBase = "0.32, 0.33, 0.34"
 StructTypes = "1"
 TensorCast = "0.3.3, 0.4"
 TimeZones = "1.3.1"
-julia = "1.9"
+julia = "1.10"
 
 [extras]
 AMD = "14f7f29c-3bd6-536c-9a0b-7339e30b5a3e"

src/Deprecated.jl

@@ -123,6 +123,7 @@ end
 ## Deprecate code below before v0.37
 ##==============================================================================
 
+@deprecate solveFactorParameteric(w...;kw...) solveFactorParametric(w...;kw...)
 
 ##==============================================================================
 ## Deprecate code below before v0.36

src/ExportAPI.jl

@@ -344,7 +344,7 @@ export approxDeconvBelief
 export cont2disc
 export rebaseFactorVariable!
 export accumulateFactorMeans
-export solveFactorParameteric
+export solveFactorParametric
 
 export repeatCSMStep!
 export attachCSM!

src/parametric/services/ConsolidateParametricRelatives.jl

@@ -16,11 +16,9 @@ DevNotes
 - TODO ensure type stability, likely returning types `Any` at this time.
 - TODO MeanMaxPPE currently stored as coordinates, complicating fast calculation.
 
-Related:
-
-[`getMeasurementParametric`](@ref), [`approxConv`](@ref), [`MutablePose2Pose2Gaussian`](@ref)
+Related: [`getMeasurementParametric`](@ref), [`approxConvBelief`](@ref), [`MutablePose2Pose2Gaussian`](@ref)
 """
-function solveFactorParameteric(
+function solveFactorParametric(
   dfg::AbstractDFG,
   fct::DFGFactor,
   # currval::P1,

src/parametric/services/ParametricManopt.jl

@@ -562,7 +562,11 @@ function autoinitParametric!(
 end
 
 
-##
+"""
+    $SIGNATURES
+
+Batch parametric graph solve using Riemannian Levenberg Marquardt.
+"""
 solveGraphParametric(args...; kwargs...) = solve_RLM(args...; kwargs...)
 
 function DFG.solveGraphParametric!(

src/parametric/services/ParametricUtils.jl

@@ -52,7 +52,7 @@ Notes
     - Originally written just for Lie Groups to support legacy, but future needs may well alter the design.
 - Original design driven by parametric solve and dead reckon tethering.
 
-See also: [`accumulateFactorMeans`](@ref), [`solveFactorParameteric`](@ref)
+See also: [`accumulateFactorMeans`](@ref), [`solveFactorParametric`](@ref)
 """
 function getMeasurementParametric end
 
@@ -648,7 +648,7 @@ DevNotes
 - WIP
 - Relates to: https://github.com/JuliaRobotics/IncrementalInference.jl/issues/466#issuecomment-562556953
 - Consolidation
-  - Definitely with [`solveFactorParameteric`](@ref)
+  - Definitely with [`solveFactorParametric`](@ref)
   - Maybe with [`solveGraphParametric`](@ref)
     - https://github.com/JuliaRobotics/IncrementalInference.jl/pull/1588#issuecomment-1210406683
 """

src/services/ApproxConv.jl

@@ -70,7 +70,7 @@ DevNotes
 
 Related
 
-[`approxDeconv`](@ref), `LocalDFG.findShortestPathDijkstra`, [`evalFactor`](@ref)
+[`approxDeconv`](@ref), `findShortestPathDijkstra`
 """
 function approxConvBelief(
   dfg::AbstractDFG,

src/services/CalcFactor.jl

@@ -118,7 +118,7 @@ residual = calcFactorResidual(fg, :x1x2f1, [1.0], [0.0], [0.0])
 
 Related
 
-[`calcFactorResidualTemporary`](@ref), [`_evalFactorTemporary!`](@ref), [`evalFactor`](@ref), [`approxConv`](@ref)
+[`calcFactorResidualTemporary`](@ref), [`_evalFactorTemporary!`](@ref), [`approxConvBelief`](@ref)
 """
 function calcFactorResidual(
   dfgfct::DFGFactor,
@@ -147,7 +147,7 @@ residual = calcFactorResidualTemporary(Pose2Pose2(...), (RoME.Pose2,RoME.Pose2),
 
 Related
 
-[`calcFactorResidual`](@ref), [`CalcResidual`](@ref), [`_evalFactorTemporary!`](@ref), [`approxConv`](@ref), [`_buildGraphByFactorAndTypes!`](@ref)
+[`calcFactorResidual`](@ref), [`CalcResidual`](@ref), [`_evalFactorTemporary!`](@ref), [`approxConvBelief`](@ref), [`_buildGraphByFactorAndTypes!`](@ref)
 """
 function calcFactorResidualTemporary(
   fct::AbstractRelative,

src/services/DeconvUtils.jl

@@ -168,12 +168,12 @@ Generalized deconvolution to find the predicted measurement values of the factor
 Inverse solve of predicted noise value and returns tuple of (newly predicted, and known "measured" noise) values.
 
 Notes
-- Opposite operation contained in `approxConv`.
+- Opposite operation contained in `approxConvBelief`.
 - For more notes see [`solveFactorMeasurements`](@ref).
 
 Related
 
-[`approxConv`](@ref), `deconvSolveKey`
+[`approxConvBelief`](@ref), `deconvSolveKey`
 """
 function approxDeconv(
   dfg::AbstractDFG,

src/services/EvalFactor.jl

@@ -620,9 +620,7 @@ B = _evalFactorTemporary!(EuclidDistance, (ContinuousScalar, ContinuousScalar),
 # should return `B = 10`
 ```
 
-Related
-
-[`evalFactor`](@ref), [`calcFactorResidual`](@ref), [`testFactorResidualBinary`](@ref), [`solveFactorParameteric`](@ref), [`approxConvBelief`](@ref)
+See also:  [`calcFactorResidual`](@ref), [`testFactorResidualBinary`](@ref), [`solveFactorParametric`](@ref), [`approxConvBelief`](@ref)
 """
 function _evalFactorTemporary!(
   fct::AbstractFactor,

src/services/FGOSUtils.jl

@@ -232,7 +232,7 @@ DevNotes
 
 Related
 
-[`getVariablePPE`](@ref), [`setVariablePosteriorEstimates!`](@ref), [`getVariablePPE!`](@ref), [`setPPE!`](@ref)
+[`getPPE`](@ref), [`setPPE!`](@ref), [`getVariablePPE`](@ref)
 """
 function calcPPE(
   var::DFGVariable,

src/services/GraphProductOperations.jl

@@ -11,7 +11,7 @@ Notes
 
 Related
 
-[`approxConv`](@ref), [`proposalbeliefs!`](@ref), [`AMP.manifoldProduct`](@ref)
+[`approxConvBelief`](@ref), [`proposalbeliefs!`](@ref), [`AMP.manifoldProduct`](@ref)
 """
 function propagateBelief(
   dfg::AbstractDFG,

src/services/SolverUtilities.jl

@@ -203,7 +203,7 @@ Notes
 - This function does not add new variables or factors to `fg`, user must do that themselves after.
   - Useful to use in combination with `setPPE!` on new variable.
 - At time of writing `accumulateFactorMeans` could only incorporate priors or binary relative factors.
-  - internal info, see [`solveFactorParameteric`](@ref),
+  - internal info, see [`solveFactorParametric`](@ref),
   - This means at time of writing `factor` must be a binary factor.
 - Tip, if simulations are inducing odometry bias, think of using two factors from caller (e.g. simPerfect and simBias).
 

src/services/TetherUtils.jl

@@ -114,7 +114,7 @@ DevNotes
 
 Related:
 
-[`approxConv`](@ref), [`solveFactorParameteric`](@ref), `RoME.MutablePose2Pose2Gaussian`
+[`approxConvBelief`](@ref), [`solveFactorParametric`](@ref), `RoME.MutablePose2Pose2Gaussian`
 """
 function accumulateFactorMeans(
   dfg::AbstractDFG, 
@@ -150,7 +150,7 @@ function accumulateFactorMeans(
     # first find direction of solve
     vars = getVariableOrder(fct)
     trgsym = setdiff(vars, [srcsym])[1]
-    val = solveFactorParameteric(dfg, fct, [srcsym => val;], trgsym; solveKey)
+    val = solveFactorParametric(dfg, fct, [srcsym => val;], trgsym; solveKey)
     srcsym = trgsym
   end