Change histogram implementation, use StatsPlots, add new histogram styles
New series recipes for binned data: * barbins * scatterbins * stepbins New series recipes for histogram: * barhist (histogram is now an alias for this) * scatterhist * stephist Supports plotting 1D and 2D StatsBase histograms, seriestype can be set to bar(bins), scatter(bins) or step(bins). Also adds support for some common auto-binning modes: * :sturges, :auto - Sturges' formula * :sqrt - Square-root choice * :rice - Rice Rule * :scott - Scott's normal reference rule * :fd - Freedman–Diaconis rule Maybe these could be contributed to StatsBase at some point. Error bars currently don't work correctly for scatterbins and scatterhist, due to problem with manipulating error bars in a series recipe, but do work for "plot(h::StatsBase.Histogram, seriestype = :scatter)" (works around the problem by calling scatter directly, it seems that error bars can be manipulated correctly in a type recipe).
This commit is contained in:
Oliver Schulz
parent
e760b42560
commit
1188230641
@ -9,6 +9,7 @@ using Base.Meta
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@reexport using PlotUtils
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@reexport using PlotThemes
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import Showoff
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import StatsBase
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export
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grid,
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@ -148,6 +149,9 @@ end
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@shorthands bar
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@shorthands barh
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@shorthands histogram
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@shorthands barhist
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@shorthands stephist
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@shorthands scatterhist
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@shorthands histogram2d
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@shorthands density
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@shorthands heatmap
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@ -21,7 +21,7 @@ const _arg_desc = KW(
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:markerstrokewidth => "Number. Width of the marker stroke (border. in pixels)",
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:markerstrokecolor => "Color Type. Color of the marker stroke (border). `:match` will take the value from `:foreground_color_subplot`.",
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:markerstrokealpha => "Number in [0,1]. The alpha/opacity override for the marker stroke (border). `nothing` (the default) means it will take the alpha value of markerstrokecolor.",
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:bins => "Integer, NTuple{2,Integer}, AbstractVector. For histogram-types, defines the number of bins, or the edges, of the histogram.",
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:bins => "Integer, NTuple{2,Integer}, AbstractVector or Symbol. For histogram-types, defines the number of bins, or the edges, of the histogram, or the auto-binning algorithm to use (:sturges, :sqrt, :rice, :scott or :fd)",
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:smooth => "Bool. Add a regression line?",
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:group => "AbstractVector. Data is split into a separate series, one for each unique value in `group`.",
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:x => "Various. Input data. First Dimension",
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@ -40,7 +40,7 @@ const _arg_desc = KW(
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:ribbon => "Number or AbstractVector. Creates a fillrange around the data points.",
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:quiver => "AbstractVector or 2-Tuple of vectors. The directional vectors U,V which specify velocity/gradient vectors for a quiver plot.",
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:arrow => "nothing (no arrows), Bool (if true, default arrows), Arrow object, or arg(s) that could be style or head length/widths. Defines arrowheads that should be displayed at the end of path line segments (just before a NaN and the last non-NaN point). Used in quiverplot, streamplot, or similar.",
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:normalize => "Bool. Should normalize histogram types? Trying for area == 1.",
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:normalize => "Bool or Symbol. Histogram normalization mode. Possible values are: false/:none (no normalization, default), true/:pdf (normalize to a PDF with integral of 1) and :density (only normalize in respect to bin sizes).",
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:weights => "AbstractVector. Used in histogram types for weighted counts.",
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:contours => "Bool. Add contours to the side-grids of 3D plots? Used in surface/wireframe.",
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:match_dimensions => "Bool. For heatmap types... should the first dimension of a matrix (rows) correspond to the first dimension of the plot (x-axis)? The default is false, which matches the behavior of Matplotlib, Plotly, and others. Note: when passing a function for z, the function should still map `(x,y) -> z`.",
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@ -35,7 +35,9 @@ const _3dTypes = [
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]
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const _allTypes = vcat([
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:none, :line, :path, :steppre, :steppost, :sticks, :scatter,
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:heatmap, :hexbin, :histogram, :histogram2d, :histogram3d, :density, :bar, :hline, :vline,
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:heatmap, :hexbin, :barbins, :barhist, :histogram, :scatterbins,
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:scatterhist, :stepbins, :stephist, :bins2d, :histogram2d, :histogram3d,
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:density, :bar, :hline, :vline,
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:contour, :pie, :shape, :image
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], _3dTypes)
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@ -78,7 +80,7 @@ const _typeAliases = Dict{Symbol,Symbol}(
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add_non_underscore_aliases!(_typeAliases)
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like_histogram(seriestype::Symbol) = seriestype == :histogram
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like_histogram(seriestype::Symbol) = seriestype in (:histogram, :barhist, :barbins)
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like_line(seriestype::Symbol) = seriestype in (:line, :path, :steppre, :steppost)
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like_surface(seriestype::Symbol) = seriestype in (:contour, :contourf, :contour3d, :heatmap, :surface, :wireframe, :image)
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@ -1261,7 +1263,7 @@ function _add_defaults!(d::KW, plt::Plot, sp::Subplot, commandIndex::Int)
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end
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# scatter plots don't have a line, but must have a shape
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if d[:seriestype] in (:scatter, :scatter3d)
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if d[:seriestype] in (:scatter, :scatterbins, :scatterhist, :scatter3d)
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d[:linewidth] = 0
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if d[:markershape] == :none
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d[:markershape] = :circle
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@ -357,7 +357,7 @@ function _expand_subplot_extrema(sp::Subplot, d::KW, st::Symbol)
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expand_extrema!(sp[:xaxis], (0,w))
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expand_extrema!(sp[:yaxis], (0,h))
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sp[:yaxis].d[:flip] = true
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elseif !(st in (:pie, :histogram, :histogram2d))
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elseif !(st in (:pie, :histogram, :bins2d, :histogram2d))
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expand_extrema!(sp, d)
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end
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end
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289
src/recipes.jl
289
src/recipes.jl
@ -378,109 +378,256 @@ end
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end
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@deps bar shape
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# ---------------------------------------------------------------------------
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# Histograms
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# edges from number of bins
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function calc_edges(v, bins::Integer)
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vmin, vmax = extrema(v)
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linspace(vmin, vmax, bins+1)
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_bin_centers(v::AVec) = (v[1:end-1] + v[2:end]) / 2
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@recipe function f(::Type{Val{:barbins}}, x, y, z)
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edge, weights = x, y
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if (d[:bar_width] == nothing)
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bar_width := diff(edge)
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end
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x := _bin_centers(edge)
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y := weights
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seriestype := :bar
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()
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end
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@deps barbins bins
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@recipe function f(::Type{Val{:scatterbins}}, x, y, z)
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edge, weights = x, y
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xerror := diff(edge)/2
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x := _bin_centers(edge)
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y := weights
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seriestype := :scatter
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()
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end
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@deps scatterbins scatter
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function _stepbins_path(edge, weights)
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nbins = length(linearindices(weights))
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if length(linearindices(edge)) != nbins + 1
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error("Edge vector must be 1 longer than weight vector")
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end
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it_e, it_w = start(edge), start(weights)
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px, it_e = next(edge, it_e)
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py = zero(eltype(weights))
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npathpts = 2 * nbins + 2
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x = Vector{eltype(px)}(npathpts)
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y = Vector{eltype(py)}(npathpts)
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x[1], y[1] = px, py
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i = 2
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while (i < npathpts - 1)
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py, it_w = next(weights, it_w)
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x[i], y[i] = px, py
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i += 1
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px, it_e = next(edge, it_e)
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x[i], y[i] = px, py
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i += 1
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end
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assert(i == npathpts)
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x[end], y[end] = px, zero(py)
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(x, y)
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end
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# just pass through arrays
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calc_edges(v, bins::AVec) = bins
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@recipe function f(::Type{Val{:stepbins}}, x, y, z)
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edge, weights = x, y
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# find the bucket index of this value
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function bucket_index(vi, edges)
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for (i,e) in enumerate(edges)
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if vi <= e
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return max(1,i-1)
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axis = d[:subplot][Plots.isvertical(d) ? :xaxis : :yaxis]
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xpts, ypts = _stepbins_path(edge, weights)
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if !Plots.isvertical(d)
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xpts, ypts = ypts, xpts
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end
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# create a secondary series for the markers
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if d[:markershape] != :none
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@series begin
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seriestype := :scatter
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x := Plots._bin_centers(edge)
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y := weights
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fillrange := nothing
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label := ""
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primary := false
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()
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end
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markershape := :none
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xerror := :none
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yerror := :none
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end
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x := xpts
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y := ypts
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seriestype := :path
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ylims --> [0, 1.1 * maximum(weights)]
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()
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end
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Plots.@deps stepbins path
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function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto)
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_cl(x) = max(ceil(Int, x), 1)
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_iqr(v) = quantile(v, 0.75) - quantile(v, 0.25)
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_span(v) = maximum(v) - minimum(v)
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n_samples = length(linearindices(first(vs)))
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# Estimator for number of samples in one row/column of bins along each axis:
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n = max(1, n_samples^(1/N))
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v = vs[dim]
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if mode == :sqrt # Square-root choice
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_cl(sqrt(n))
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elseif mode == :sturges || mode ==:auto # Sturges' formula
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_cl(log2(n)) + 1
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elseif mode == :rice # Rice Rule
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_cl(2 * n^(1/3))
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elseif mode == :scott # Scott's normal reference rule
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_cl(_span(v) / (3.5 * std(v) / n^(1/3)))
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elseif mode == :fd # Freedman–Diaconis rule
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_cl(_span(v) / (2 * _iqr(v) / n^(1/3)))
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else
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error("Unknown auto-binning mode $mode")
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end
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return length(edges)-1
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end
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function my_hist(v, bins; normed = false, weights = nothing)
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edges = calc_edges(v, bins)
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counts = zeros(length(edges)-1)
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_hist_edge{N}(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Integer) = StatsBase.histrange(vs[dim], binning, :left)
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_hist_edge{N}(vs::NTuple{N,AbstractVector}, dim::Integer, binning::Symbol) = _hist_edge(vs, dim, _auto_binning_nbins(vs, dim, mode = binning))
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_hist_edge{N}(vs::NTuple{N,AbstractVector}, dim::Integer, binning::AbstractVector) = binning
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# add a weighted count
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for (i,vi) in enumerate(v)
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idx = bucket_index(vi, edges)
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counts[idx] += (weights == nothing ? 1.0 : weights[i])
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end
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_hist_edges{N}(vs::NTuple{N,AbstractVector}, binning::NTuple{N}) =
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map(dim -> _hist_edge(vs, dim, binning[dim]), (1:N...))
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counts = isapprox(extrema(diff(edges))...) ? counts : counts ./ diff(edges) # for uneven bins, normalize to area.
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_hist_edges{N}(vs::NTuple{N,AbstractVector}, binning::Union{Integer, Symbol, AbstractVector}) =
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map(dim -> _hist_edge(vs, dim, binning), (1:N...))
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# normalize by bar area?
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norm_denom = normed ? sum(diff(edges) .* counts) : 1.0
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if norm_denom == 0
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norm_denom = 1.0
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end
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_hist_norm_mode(mode::Symbol) = mode
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_hist_norm_mode(mode::Bool) = mode ? :pdf : :none
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edges, counts ./ norm_denom
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function _make_hist{N}(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing)
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edges = _hist_edges(vs, binning)
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h = float( weights == nothing ?
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StatsBase.fit(StatsBase.Histogram, vs, edges, closed = :left) :
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StatsBase.fit(StatsBase.Histogram, vs, weights, edges, closed = :left)
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)
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normalize!(h, mode = _hist_norm_mode(normed))
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end
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@recipe function f(::Type{Val{:histogram}}, x, y, z)
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edges, counts = my_hist(y, d[:bins],
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normed = d[:normalize],
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weights = d[:weights])
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bar_width := diff(edges)
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x := centers(edges)
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y := counts
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seriestype := :bar
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seriestype := :barhist
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()
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end
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@deps histogram bar
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@deps histogram barhist
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@recipe function f(::Type{Val{:barhist}}, x, y, z)
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h = _make_hist((y,), d[:bins], normed = d[:normalize], weights = d[:weights])
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x := h.edges[1]
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y := h.weights
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seriestype := :barbins
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()
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end
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@deps barhist barbins
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@recipe function f(::Type{Val{:stephist}}, x, y, z)
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h = _make_hist((y,), d[:bins], normed = d[:normalize], weights = d[:weights])
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x := h.edges[1]
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y := h.weights
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seriestype := :stepbins
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()
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end
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@deps stephist stepbins
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@recipe function f(::Type{Val{:scatterhist}}, x, y, z)
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h = _make_hist((y,), d[:bins], normed = d[:normalize], weights = d[:weights])
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x := h.edges[1]
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y := h.weights
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seriestype := :scatterbins
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()
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end
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@deps scatterhist scatterbins
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@recipe function f{T, E}(h::StatsBase.Histogram{T, 1, E})
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seriestype --> :barbins
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st_map = Dict(
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:bar => :barbins, :scatter => :scatterbins, :step => :stepbins,
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:steppost => :stepbins # :step can be mapped to :steppost in pre-processing
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)
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seriestype := get(st_map, d[:seriestype], d[:seriestype])
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if d[:seriestype] == :scatterbins
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# Workaround, error bars currently not set correctly by scatterbins
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xerror --> diff(h.edges[1])/2
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seriestype := :scatter
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(Plots._bin_centers(h.edges[1]), h.weights)
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else
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(h.edges[1], h.weights)
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end
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end
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@recipe function f{H <: StatsBase.Histogram}(hv::AbstractVector{H})
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for h in hv
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@series begin
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h
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end
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end
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end
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# ---------------------------------------------------------------------------
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# Histogram 2D
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# if tuple, map out bins, otherwise use the same for both
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calc_edges_2d(x, y, bins) = calc_edges(x, bins), calc_edges(y, bins)
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calc_edges_2d{X,Y}(x, y, bins::Tuple{X,Y}) = calc_edges(x, bins[1]), calc_edges(y, bins[2])
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@recipe function f(::Type{Val{:bins2d}}, x, y, z)
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edge_x, edge_y, weights = x, y, z.surf
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# the 2D version
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function my_hist_2d(x, y, bins; normed = false, weights = nothing)
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xedges, yedges = calc_edges_2d(x, y, bins)
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counts = zeros(length(yedges)-1, length(xedges)-1)
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# add a weighted count
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for i=1:length(x)
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r = bucket_index(y[i], yedges)
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c = bucket_index(x[i], xedges)
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counts[r,c] += (weights == nothing ? 1.0 : weights[i])
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float_weights = float(weights)
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if is(float_weights, weights)
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float_weights = deepcopy(float_weights)
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end
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# normalize to cubic area of the imaginary surface towers
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norm_denom = normed ? sum((diff(yedges) * diff(xedges)') .* counts) : 1.0
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if norm_denom == 0
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norm_denom = 1.0
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end
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xedges, yedges, counts ./ norm_denom
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end
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centers(v::AVec) = 0.5 * (v[1:end-1] + v[2:end])
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@recipe function f(::Type{Val{:histogram2d}}, x, y, z)
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xedges, yedges, counts = my_hist_2d(x, y, d[:bins],
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normed = d[:normalize],
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weights = d[:weights])
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for (i,c) in enumerate(counts)
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for (i, c) in enumerate(float_weights)
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if c == 0
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counts[i] = NaN
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float_weights[i] = NaN
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end
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end
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x := centers(xedges)
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y := centers(yedges)
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z := Surface(counts)
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linewidth := 0
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x := Plots._bin_centers(edge_x)
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y := Plots._bin_centers(edge_y)
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z := Surface(float_weights)
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match_dimensions := true
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seriestype := :heatmap
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()
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end
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@deps histogram2d heatmap
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Plots.@deps bins2d heatmap
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@recipe function f(::Type{Val{:histogram2d}}, x, y, z)
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h = _make_hist((x, y), d[:bins], normed = d[:normalize], weights = d[:weights])
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x := h.edges[1]
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y := h.edges[2]
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z := Surface(h.weights)
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seriestype := :bins2d
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()
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end
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@deps histogram2d bins2d
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@recipe function f{T, E}(h::StatsBase.Histogram{T, 2, E})
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seriestype --> :bins2d
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(h.edges[1], h.edges[2], Surface(h.weights))
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end
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# ---------------------------------------------------------------------------
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@ -39,7 +39,7 @@ series_list(sp::Subplot) = sp.series_list # filter(series -> series.d[:subplot]
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function should_add_to_legend(series::Series)
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series.d[:primary] && series.d[:label] != "" &&
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!(series.d[:seriestype] in (
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:hexbin,:histogram2d,:hline,:vline,
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:hexbin,:bins2d,:histogram2d,:hline,:vline,
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:contour,:contourf,:contour3d,:surface,:wireframe,
|
||||
:heatmap, :pie, :image
|
||||
))
|
||||
|
||||
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Reference in New Issue
Block a user