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move binnedpointlist to ExtendableGrids
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| @@ -1,7 +1,7 @@ | ||
| name = "ExtendableGrids" | ||
| uuid = "cfc395e8-590f-11e8-1f13-43a2532b2fa8" | ||
| authors = ["Juergen Fuhrmann <[email protected]>", "Christian Merdon <[email protected]>", "Johannes Taraz <[email protected]>"] | ||
| version = "1.3.2" | ||
| version = "1.4.0" | ||
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| [deps] | ||
| AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" | ||
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@@ -20,6 +20,12 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" | |
| UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" | ||
| WriteVTK = "64499a7a-5c06-52f2-abe2-ccb03c286192" | ||
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| [weakdeps] | ||
| Gmsh = "705231aa-382f-11e9-3f0c-b7cb4346fdeb" | ||
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| [extensions] | ||
| ExtendableGridsGmshExt = "Gmsh" | ||
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| [compat] | ||
| AbstractTrees = "0.3,0.4" | ||
| Bijections = "0.1.4" | ||
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@@ -39,11 +45,5 @@ UUIDs = "1.6" | |
| WriteVTK = "1.14" | ||
| julia = "1.6" | ||
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| [extensions] | ||
| ExtendableGridsGmshExt = "Gmsh" | ||
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| [extras] | ||
| Gmsh = "705231aa-382f-11e9-3f0c-b7cb4346fdeb" | ||
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| [weakdeps] | ||
| Gmsh = "705231aa-382f-11e9-3f0c-b7cb4346fdeb" | ||
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| # BinnedPointList | ||
| Used to find and identify points in space | ||
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| ## API | ||
| ```@autodocs | ||
| Modules = [ExtendableGrids] | ||
| Pages = ["binnedpointlist.jl"] | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
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| """ | ||
| $(TYPEDEF) | ||
| Binned point list structure allowing for fast | ||
| check for already existing points. | ||
| This provides better performance for indendifying | ||
| already inserted points than the naive linear search. | ||
| OTOH the implementation is still quite naive - it dynamically maintains | ||
| a cuboid bining region with a fixed number of bins. | ||
| Probably tree based adaptive methods (a la octree) will be more efficient, | ||
| however they will be harder to implement. | ||
| In an ideal world, we would maintain a dynamic Delaunay triangulation, which | ||
| at once could be the starting point of mesh generation which will follow here | ||
| anyway. | ||
| """ | ||
| mutable struct BinnedPointList{T} | ||
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| # Space dimension | ||
| dim::Int32 | ||
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| # Point distance tolerance. Points closer than tol | ||
| # (in Euclidean distance) will be identified, i.e. | ||
| # are collapsed to the first inserted. | ||
| tol::T | ||
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| # The union of all bins is the binning region - | ||
| # a cuboid given by two of its corners. It is calculated | ||
| # dynamically depending on the inserted points. | ||
| binning_region_min::Vector{T} | ||
| binning_region_max::Vector{T} | ||
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| # Increase factor of binning region (with respect to | ||
| # the cuboid defined by the coordinates of the binned points) | ||
| binning_region_increase_factor::T | ||
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| # The actual point list | ||
| points::ElasticArray{T, 2} | ||
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| # The bins are vectors of indices of points in the point list | ||
| # We store them in a dim-dimensional array of length number_of_directional_bins^dim | ||
| bins::Array{Vector{Int32}} | ||
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| # Number of bins in each space dimension | ||
| number_of_directional_bins::Int32 | ||
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| # Some points will fall outside of the binning region. | ||
| # We collect them in vector of ubinned point indices | ||
| unbinned::Vector{Int32} | ||
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| # Number of unbinned points tolerated without rebinning | ||
| num_allowed_unbinned_points::Int32 | ||
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| # Maximum ratio of unbinned points in point list | ||
| max_unbinned_ratio::T | ||
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| # Storage of current point bin | ||
| current_bin::Vector{Int32} | ||
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| BinnedPointList{T}(::Nothing) where {T} = new() | ||
| end | ||
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| """ | ||
| $(SIGNATURES) | ||
| Create and initialize binned point list | ||
| """ | ||
| function BinnedPointList(::Type{T}, dim; | ||
| tol = 1.0e-12, | ||
| number_of_directional_bins = 10, | ||
| binning_region_increase_factor = 0.01, | ||
| num_allowed_unbinned_points = 5, | ||
| max_unbinned_ratio = 0.05) where {T} | ||
| bpl = BinnedPointList{T}(nothing) | ||
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| bpl.dim = dim | ||
| bpl.tol = tol | ||
| bpl.number_of_directional_bins = number_of_directional_bins | ||
| bpl.binning_region_increase_factor = binning_region_increase_factor | ||
| bpl.num_allowed_unbinned_points = num_allowed_unbinned_points | ||
| bpl.max_unbinned_ratio = max_unbinned_ratio | ||
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| bpl.points = ElasticArray{Cdouble}(undef, dim, 0) | ||
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| bpl.binning_region_min = fill(floatmax(T), dim) | ||
| bpl.binning_region_max = fill(-floatmax(T), dim) | ||
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| bpl.unbinned = Vector{Int32}(undef, 0) | ||
| bpl.bins = Array{Vector{Int32}, dim}(undef, zeros(Int32, dim)...) | ||
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| bpl.current_bin = zeros(Int32, bpl.dim) | ||
| bpl | ||
| end | ||
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| """ | ||
| $(SIGNATURES) | ||
| Create and initialize binned point list | ||
| """ | ||
| BinnedPointList(dim; kwargs...) = BinnedPointList(Cdouble, dim; kwargs...) | ||
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| # | ||
| # Find point in index list (by linear search) | ||
| # Return its index, or zero if not found | ||
| # | ||
| function _findpoint(bpl, index, p) | ||
| for i = 1:length(index) | ||
| @views if norm(bpl.points[:, index[i]] - p) < bpl.tol | ||
| return index[i] | ||
| end | ||
| end | ||
| return 0 | ||
| end | ||
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| # | ||
| # Calculate the bin of the point. Result is stored | ||
| # in bpl.current_bin | ||
| # | ||
| function _bin_of_point!(bpl, p) | ||
| for idim = 1:(bpl.dim) | ||
| # scaled value for particular dimension | ||
| s = (p[idim] - bpl.binning_region_min[idim]) / (bpl.binning_region_max[idim] - bpl.binning_region_min[idim]) | ||
| if s > 1 || s < 0 | ||
| # point lies outside of binning area | ||
| bpl.current_bin[idim] = 0 | ||
| else | ||
| # calculate bin in particular dimension | ||
| bpl.current_bin[idim] = ceil(Int32, bpl.number_of_directional_bins * s) | ||
| end | ||
| end | ||
| end | ||
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| # | ||
| # Re-calculate binning if there are too many unbinned points | ||
| # This amounts to two steps: | ||
| # - Enlarge binning area in order to include all points | ||
| # - Re-calculate all point bins | ||
| function _rebin_all_points!(bpl) | ||
| if length(bpl.unbinned) > max(bpl.num_allowed_unbinned_points, | ||
| bpl.max_unbinned_ratio * size(bpl.points, 2)) | ||
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| # Calculate extrema of unbinned points | ||
| @views e = extrema(bpl.points[:, bpl.unbinned]; dims = 2) | ||
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| for i = 1:(bpl.dim) | ||
| # Increase binning region according to unbinned extrema | ||
| bpl.binning_region_min[i] = min(bpl.binning_region_min[i], e[i][1]) | ||
| bpl.binning_region_max[i] = max(bpl.binning_region_max[i], e[i][2]) | ||
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| # Slightly increase binning region further in order to | ||
| # include all existing points with tolerance | ||
| delta = max(bpl.binning_region_max[i] - bpl.binning_region_min[i], bpl.tol) | ||
| bpl.binning_region_min[i] -= bpl.binning_region_increase_factor * delta | ||
| bpl.binning_region_max[i] += bpl.binning_region_increase_factor * delta | ||
| end | ||
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| # Re-allocate all bins | ||
| if bpl.dim == 1 | ||
| bpl.bins = [zeros(Int32, 0) for i = 1:(bpl.number_of_directional_bins)] | ||
| elseif bpl.dim == 2 | ||
| bpl.bins = [zeros(Int32, 0) for i = 1:(bpl.number_of_directional_bins), j = 1:(bpl.number_of_directional_bins)] | ||
| elseif bpl.dim == 3 | ||
| bpl.bins = [zeros(Int32, 0) | ||
| for i = 1:(bpl.number_of_directional_bins), j = 1:(bpl.number_of_directional_bins), | ||
| k = 1:(bpl.number_of_directional_bins)] | ||
| end | ||
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| # Register all points in their respctive bins | ||
| for i = 1:size(bpl.points, 2) | ||
| @views _bin_of_point!(bpl, bpl.points[:, i]) | ||
| push!(bpl.bins[bpl.current_bin...], i) | ||
| end | ||
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| # Re-allocate unbinned index | ||
| bpl.unbinned = zeros(Int32, 0) | ||
| end | ||
| end | ||
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| """ | ||
| $(SIGNATURES) | ||
| If another point with distance less the tol from p is | ||
| in pointlist, return its index. Otherwise, insert point into pointlist. | ||
| """ | ||
| function Base.insert!(bpl::BinnedPointList{T}, p) where {T} | ||
| _rebin_all_points!(bpl) | ||
| _bin_of_point!(bpl, p) | ||
| if reduce(*, bpl.current_bin) > 0 | ||
| i = _findpoint(bpl, bpl.bins[bpl.current_bin...], p) | ||
| if i > 0 | ||
| return i | ||
| else | ||
| append!(bpl.points, p) | ||
| i = size(bpl.points, 2) | ||
| push!(bpl.bins[bpl.current_bin...], i) | ||
| return i | ||
| end | ||
| else | ||
| i = _findpoint(bpl, bpl.unbinned, p) | ||
| if i > 0 | ||
| return i | ||
| else | ||
| append!(bpl.points, p) | ||
| i = size(bpl.points, 2) | ||
| push!(bpl.unbinned, i) | ||
| return i | ||
| end | ||
| end | ||
| end | ||
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| Base.insert!(bpl::BinnedPointList{T}, x::Number) where {T} = insert!(bpl, (x)) | ||
| Base.insert!(bpl::BinnedPointList{T}, x::Number, y::Number) where {T} = insert!(bpl, (x, y)) | ||
| Base.insert!(bpl::BinnedPointList{T}, x::Number, y::Number, z::Number) where {T} = insert!(bpl, (x, y, z)) | ||
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| """ | ||
| $(SIGNATURES) | ||
| Return the array of points in the point list. | ||
| """ | ||
| points(bpl::BinnedPointList{T}) where {T} = bpl.points | ||
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| # Just for being able to check of all of the above was worth the effort... | ||
| function naiveinsert!(bpl::BinnedPointList{T}, p) where {T} | ||
| for i = 1:size(bpl.points, 2) | ||
| @views if norm(bpl.points[:, i] - p) < bpl.tol | ||
| return i | ||
| end | ||
| end | ||
| append!(bpl.points, p) | ||
| size(bpl.points, 2) | ||
| end |
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