From 96181926d78a34a856f4a5ac68fd8c1908b7f7e2 Mon Sep 17 00:00:00 2001 From: "Michael K. Borregaard" Date: Wed, 31 May 2017 10:39:18 +0200 Subject: [PATCH] Use NaNMath explicitly everywhere --- src/Plots.jl | 10 +--------- src/axes.jl | 8 ++++---- src/backends.jl | 2 +- src/backends/glvisualize.jl | 22 +++++++++++----------- src/backends/gr.jl | 6 +++--- src/backends/plotly.jl | 2 +- src/backends/pyplot.jl | 4 ++-- src/components.jl | 8 ++++---- src/layouts.jl | 4 ++-- src/pipeline.jl | 2 +- src/recipes.jl | 20 ++++++++++---------- src/utils.jl | 26 +++++++++++++------------- test/runtests.jl | 4 ++-- 13 files changed, 55 insertions(+), 63 deletions(-) diff --git a/src/Plots.jl b/src/Plots.jl index bb554bcf..4842a039 100644 --- a/src/Plots.jl +++ b/src/Plots.jl @@ -10,6 +10,7 @@ using Base.Meta @reexport using PlotThemes import Showoff import StatsBase +import NaNMath # define functions that ignores NaNs. To overcome the destructive effects of https://github.com/JuliaLang/julia/pull/12563 export grid, @@ -106,15 +107,6 @@ export # --------------------------------------------------------- -import NaNMath -# define functions (e.g. `_extrema`, that ignores NaNs, when the type is applicable. To overcome the destructive effects of https://github.com/JuliaLang/julia/pull/12563 -for fun in (:extrema, :minimum, :maximum) - @eval $(Symbol(string("_",fun))){F<:AbstractFloat}(x::AbstractArray{F}) = NaNMath.$(fun)(x) - @eval $(Symbol(string("_",fun)))(x) = Base.$(fun)(x) -end - -# --------------------------------------------------------- - import Measures import Measures: Length, AbsoluteLength, Measure, BoundingBox, mm, cm, inch, pt, width, height, w, h const BBox = Measures.Absolute2DBox diff --git a/src/axes.jl b/src/axes.jl index 64adc90f..0d855190 100644 --- a/src/axes.jl +++ b/src/axes.jl @@ -118,7 +118,7 @@ Base.show(io::IO, axis::Axis) = dumpdict(axis.d, "Axis", true) # Base.getindex(axis::Axis, k::Symbol) = getindex(axis.d, k) Base.setindex!(axis::Axis, v, ks::Symbol...) = setindex!(axis.d, v, ks...) Base.haskey(axis::Axis, k::Symbol) = haskey(axis.d, k) -_extrema(axis::Axis) = (ex = axis[:extrema]; (ex.emin, ex.emax)) +NaNMath.extrema(axis::Axis) = (ex = axis[:extrema]; (ex.emin, ex.emax)) const _scale_funcs = Dict{Symbol,Function}( @@ -349,11 +349,11 @@ function expand_extrema!(sp::Subplot, d::KW) bw = d[:bar_width] if bw == nothing - bw = d[:bar_width] = _mean(diff(data)) + bw = d[:bar_width] = NaNMath.mean(diff(data)) end axis = sp.attr[Symbol(dsym, :axis)] - expand_extrema!(axis, _maximum(data) + 0.5_maximum(bw)) - expand_extrema!(axis, _minimum(data) - 0.5_minimum(bw)) + expand_extrema!(axis, NaNMath.maximum(data) + 0.5maximum(bw)) + expand_extrema!(axis, NaNMath.minimum(data) - 0.5minimum(bw)) end end diff --git a/src/backends.jl b/src/backends.jl index 72bef57e..a402bc5f 100644 --- a/src/backends.jl +++ b/src/backends.jl @@ -75,7 +75,7 @@ function tick_padding(axis::Axis) vals, labs = ticks isempty(labs) && return 0mm # ptsz = axis[:tickfont].pointsize * pt - longest_label = _maximum(length(lab) for lab in labs) + longest_label = maximum(length(lab) for lab in labs) # generalize by "rotating" y labels rot = axis[:rotation] + (axis[:letter] == :y ? 90 : 0) diff --git a/src/backends/glvisualize.jl b/src/backends/glvisualize.jl index b9f8006f..e91005ce 100644 --- a/src/backends/glvisualize.jl +++ b/src/backends/glvisualize.jl @@ -175,7 +175,7 @@ end function gl_marker(shape::Shape) points = Point2f0[Vec{2,Float32}(p) for p in zip(shape.x, shape.y)] bb = GeometryTypes.AABB(points) - mini, maxi = _minimum(bb), _maximum(bb) + mini, maxi = minimum(bb), maximum(bb) w3 = maxi-mini origin, width = Point2f0(mini[1], mini[2]), Point2f0(w3[1], w3[2]) map!(p -> ((p - origin) ./ width) - 0.5f0, points) # normalize and center @@ -304,7 +304,7 @@ function extract_any_color(d, kw_args) kw_args[:color_norm] = Vec2f0(clims) end elseif clims == :auto - kw_args[:color_norm] = Vec2f0(_extrema(d[:y])) + kw_args[:color_norm] = Vec2f0(NaNMath.extrema(d[:y])) end end else @@ -315,7 +315,7 @@ function extract_any_color(d, kw_args) kw_args[:color_norm] = Vec2f0(clims) end elseif clims == :auto - kw_args[:color_norm] = Vec2f0(_extrema(d[:y])) + kw_args[:color_norm] = Vec2f0(NaNMath.extrema(d[:y])) else error("Unsupported limits: $clims") end @@ -470,7 +470,7 @@ function hover(to_hover, to_display, window) end GLVisualize._view(robj, popup, camera = cam) bb = GLAbstraction.boundingbox(robj).value - mini = _minimum(bb) + mini = minimum(bb) w = GeometryTypes.widths(bb) wborder = w * 0.08f0 #8 percent border bb = GeometryTypes.AABB{Float32}(mini - wborder, w + 2 * wborder) @@ -482,7 +482,7 @@ function hover(to_hover, to_display, window) end function extract_extrema(d, kw_args) - xmin, xmax = _extrema(d[:x]); ymin, ymax = _extrema(d[:y]) + xmin, xmax = NaNMath.extrema(d[:x]); ymin, ymax = NaNMath.extrema(d[:y]) kw_args[:primitive] = GeometryTypes.SimpleRectangle{Float32}(xmin, ymin, xmax-xmin, ymax-ymin) nothing end @@ -509,7 +509,7 @@ function extract_colornorm(d, kw_args) else d[:y] end - kw_args[:color_norm] = Vec2f0(_extrema(z)) + kw_args[:color_norm] = Vec2f0(NaNMath.extrema(z)) kw_args[:intensity] = map(Float32, collect(z)) end end @@ -781,7 +781,7 @@ function gl_bar(d, kw_args) # compute half-width of bars bw = nothing hw = if bw == nothing - _mean(diff(x)) + NaNMath.mean(diff(x)) else Float64[cycle(bw,i)*0.5 for i=1:length(x)] end @@ -864,7 +864,7 @@ function gl_boxplot(d, kw_args) end # change q1 and q5 to show outliers # using maximum and minimum values inside the limits - q1, q5 = _extrema(inside) + q1, q5 = NaNMath.extrema(inside) end # Box if notch @@ -1318,7 +1318,7 @@ function gl_contour(x, y, z, kw_args) T = eltype(z) levels = Contour.contours(map(T, x), map(T, y), z, h) result = Point2f0[] - zmin, zmax = get(kw_args, :limits, Vec2f0(_extrema(z))) + zmin, zmax = get(kw_args, :limits, Vec2f0(NaNMath.extrema(z))) cmap = get(kw_args, :color_map, get(kw_args, :color, RGBA{Float32}(0,0,0,1))) colors = RGBA{Float32}[] for c in levels.contours @@ -1339,7 +1339,7 @@ end function gl_heatmap(x,y,z, kw_args) - get!(kw_args, :color_norm, Vec2f0(_extrema(z))) + get!(kw_args, :color_norm, Vec2f0(NaNMath.extrema(z))) get!(kw_args, :color_map, Plots.make_gradient(cgrad())) delete!(kw_args, :intensity) I = GLVisualize.Intensity{1, Float32} @@ -1382,7 +1382,7 @@ function label_scatter(d, w, ho) if isapprox(bbw[3], 0) bbw = Vec3f0(bbw[1], bbw[2], 1) end - mini = _minimum(bb) + mini = NaNMath.minimum(bb) m = GLAbstraction.translationmatrix(-mini) m *= GLAbstraction.scalematrix(1 ./ bbw) kw[:primitive] = m * p diff --git a/src/backends/gr.jl b/src/backends/gr.jl index c381ba3b..919eb02d 100644 --- a/src/backends/gr.jl +++ b/src/backends/gr.jl @@ -264,7 +264,7 @@ end normalize_zvals(zv::Void) = zv function normalize_zvals(zv::AVec) - vmin, vmax = _extrema(zv) + vmin, vmax = NaNMath.extrema(zv) if vmin == vmax zeros(length(zv)) else @@ -639,7 +639,7 @@ function gr_display(sp::Subplot{GRBackend}, w, h, viewport_canvas) elseif ispolar(sp) r = gr_set_viewport_polar() - rmin, rmax = GR.adjustrange(_minimum(r), _maximum(r)) + rmin, rmax = GR.adjustrange(minimum(r), maximum(r)) # rmin, rmax = axis_limits(sp[:yaxis]) gr_polaraxes(rmin, rmax) @@ -824,7 +824,7 @@ function gr_display(sp::Subplot{GRBackend}, w, h, viewport_canvas) # create the colorbar of contour levels if sp[:colorbar] != :none gr_set_viewport_cmap(sp) - l = round(Int32, 1000 + (h - _minimum(h)) / (_maximum(h) - _minimum(h)) * 255) + l = round(Int32, 1000 + (h - NaNMath.minimum(h)) / (NaNMath.maximum(h) - NaNMath.minimum(h)) * 255) #much in doubt whether to use the NaNMath minimum here GR.setwindow(xmin, xmax, zmin, zmax) GR.cellarray(xmin, xmax, zmax, zmin, 1, length(l), l) ztick = 0.5 * GR.tick(zmin, zmax) diff --git a/src/backends/plotly.jl b/src/backends/plotly.jl index 2dba181e..9232fb85 100644 --- a/src/backends/plotly.jl +++ b/src/backends/plotly.jl @@ -546,7 +546,7 @@ function plotly_series(plt::Plot, series::Series) else # grad = ColorGradient(series[:markercolor], alpha=series[:markeralpha]) grad = as_gradient(series[:markercolor], series[:markeralpha]) - zmin, zmax = _extrema(series[:marker_z]) + zmin, zmax = NaNMath.extrema(series[:marker_z]) zrange = zmax == zmin ? 1 : zmax - zmin # if all marker_z values are the same, plot all markers same color (avoids division by zero in next line) [rgba_string(grad[(zi - zmin) / zrange]) for zi in series[:marker_z]] end diff --git a/src/backends/pyplot.jl b/src/backends/pyplot.jl index 124871e5..1aff8091 100644 --- a/src/backends/pyplot.jl +++ b/src/backends/pyplot.jl @@ -778,7 +778,7 @@ function py_add_series(plt::Plot{PyPlotBackend}, series::Series) end clims = sp[:clims] - zmin, zmax = _extrema(z) + zmin, zmax = NaNMath.extrema(z) extrakw[:vmin] = (is_2tuple(clims) && isfinite(clims[1])) ? clims[1] : zmin extrakw[:vmax] = (is_2tuple(clims) && isfinite(clims[2])) ? clims[2] : zmax @@ -926,7 +926,7 @@ function py_compute_axis_minval(axis::Axis) for series in series_list(sp) v = series.d[axis[:letter]] if !isempty(v) - minval = NaNMath.min(minval, _minimum(abs(v))) + minval = NaNMath.min(minval, NaNMath.minimum(abs(v))) end end end diff --git a/src/components.jl b/src/components.jl index 37e0e1c3..da41ed55 100644 --- a/src/components.jl +++ b/src/components.jl @@ -501,7 +501,7 @@ immutable ZValues zrange::Tuple{Float64,Float64} end -function zvalues{T<:Real}(values::AVec{T}, zrange::Tuple{T,T} = (_minimum(values), _maximum(values))) +function zvalues{T<:Real}(values::AVec{T}, zrange::Tuple{T,T} = (NaNMath.minimum(values), NaNMath.maximum(values))) ZValues(collect(float(values)), map(Float64, zrange)) end @@ -645,8 +645,8 @@ function (bc::BezierCurve)(t::Real) p end -_mean(x::Real, y::Real) = 0.5*(x+y) -_mean{N,T<:Real}(ps::FixedSizeArrays.Vec{N,T}...) = sum(ps) / length(ps) +# mean(x::Real, y::Real) = 0.5*(x+y) #commented out as I cannot see this used anywhere and it overwrites a Base method with different functionality +NaNMath.mean{N,T<:Real}(ps::FixedSizeArrays.Vec{N,T}...) = NaNMath.sum(ps) / length(ps) @deprecate curve_points coords @@ -659,7 +659,7 @@ function directed_curve(args...; kw...) end function extrema_plus_buffer(v, buffmult = 0.2) - vmin,vmax = _extrema(v) + vmin,vmax = NaNMath.extrema(v) vdiff = vmax-vmin buffer = vdiff * buffmult vmin - buffer, vmax + buffer diff --git a/src/layouts.jl b/src/layouts.jl index 654b81f2..6a380b1f 100644 --- a/src/layouts.jl +++ b/src/layouts.jl @@ -642,7 +642,7 @@ end function create_grid_vcat(expr::Expr) rowsizes = map(rowsize, expr.args) - rmin, rmax = _extrema(rowsizes) + rmin, rmax = extrema(rowsizes) if rmin > 0 && rmin == rmax # we have a grid... build the whole thing # note: rmin is the number of columns @@ -704,7 +704,7 @@ function link_axes!(axes::Axis...) a1 = axes[1] for i=2:length(axes) a2 = axes[i] - expand_extrema!(a1, _extrema(a2)) + expand_extrema!(a1, NaNMath.extrema(a2)) for k in (:extrema, :discrete_values, :continuous_values, :discrete_map) a2[k] = a1[k] end diff --git a/src/pipeline.jl b/src/pipeline.jl index e385b8a8..3a68e4a4 100644 --- a/src/pipeline.jl +++ b/src/pipeline.jl @@ -153,7 +153,7 @@ function _add_smooth_kw(kw_list::Vector{KW}, kw::KW) if get(kw, :smooth, false) x, y = kw[:x], kw[:y] β, α = convert(Matrix{Float64}, [x ones(length(x))]) \ convert(Vector{Float64}, y) - sx = [_minimum(x), _maximum(x)] + sx = [NaNMath.minimum(x), NaNMath.maximum(x)] sy = β * sx + α push!(kw_list, merge(copy(kw), KW( :seriestype => :path, diff --git a/src/recipes.jl b/src/recipes.jl index 2d52e21a..1cf01b05 100644 --- a/src/recipes.jl +++ b/src/recipes.jl @@ -225,7 +225,7 @@ end fr = if yaxis[:scale] == :identity 0.0 else - NaNMath.min(axis_limits(yaxis)[1], _minimum(y)) + NaNMath.min(axis_limits(yaxis)[1], NaNMath.minimum(y)) end end newx, newy = zeros(3n), zeros(3n) @@ -338,7 +338,7 @@ end # compute half-width of bars bw = d[:bar_width] hw = if bw == nothing - 0.5_mean(diff(procx)) + 0.5NaNMath.mean(diff(procx)) else Float64[0.5cycle(bw,i) for i=1:length(procx)] end @@ -366,7 +366,7 @@ end end # widen limits out a bit - expand_extrema!(axis, widen(_extrema(xseg.pts)...)) + expand_extrema!(axis, widen(NaNMath.extrema(xseg.pts)...)) # switch back if !isvertical(d) @@ -414,8 +414,8 @@ end function _preprocess_binbarlike_weights{T<:AbstractFloat}(::Type{T}, w, wscale::Symbol) w_adj = _scale_adjusted_values(T, w, wscale) - w_min = _minimum(w_adj) - w_max = _maximum(w_adj) + w_min = NaNMath.minimum(w_adj) + w_max = NaNMath.maximum(w_adj) baseline = _binbarlike_baseline(w_min, wscale) w_adj, baseline end @@ -550,7 +550,7 @@ Plots.@deps stepbins path function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto) _cl(x) = NaNMath.max(ceil(Int, x), 1) _iqr(v) = quantile(v, 0.75) - quantile(v, 0.25) - _span(v) = _maximum(v) - _minimum(v) + _span(v) = NaNMath.maximum(v) - NaNMath.minimum(v) n_samples = length(linearindices(first(vs))) # Estimator for number of samples in one row/column of bins along each axis: @@ -919,7 +919,7 @@ end # get the joined vector function get_xy(v::AVec{OHLC}, x = 1:length(v)) - xdiff = 0.3_mean(abs(diff(x))) + xdiff = 0.3NaNMath.mean(abs(diff(x))) x_out, y_out = zeros(0), zeros(0) for (i,ohlc) in enumerate(v) ox,oy = get_xy(ohlc, x[i], xdiff) @@ -984,8 +984,8 @@ end yflip := true aspect_ratio := 1 rs, cs, zs = findnz(z.surf) - xlim := _extrema(cs) - ylim := _extrema(rs) + xlim := NaNMath.extrema(cs) + ylim := NaNMath.extrema(rs) if d[:markershape] == :none markershape := :circle end @@ -1006,7 +1006,7 @@ end "Adds a+bx... straight line over the current plot" function abline!(plt::Plot, a, b; kw...) - plot!(plt, [_extrema(plt)...], x -> b + a*x; kw...) + plot!(plt, [NaNMath.extrema(plt)...], x -> b + a*x; kw...) end abline!(args...; kw...) = abline!(current(), args...; kw...) diff --git a/src/utils.jl b/src/utils.jl index 82fec6aa..0ad5194a 100644 --- a/src/utils.jl +++ b/src/utils.jl @@ -3,7 +3,7 @@ calcMidpoints(edges::AbstractVector) = Float64[0.5 * (edges[i] + edges[i+1]) for "Make histogram-like bins of data" function binData(data, nbins) - lo, hi = _extrema(data) + lo, hi = NaNMath.extrema(data) edges = collect(linspace(lo, hi, nbins+1)) midpoints = calcMidpoints(edges) buckets = Int[NaNMath.max(2, NaNMath.min(searchsortedfirst(edges, x), length(edges)))-1 for x in data] @@ -109,7 +109,7 @@ function regressionXY(x, y) β, α = convert(Matrix{Float64}, [x ones(length(x))]) \ convert(Vector{Float64}, y) # make a line segment - regx = [_minimum(x), _maximum(x)] + regx = [NaNMath.minimum(x), NaNMath.maximum(x)] regy = β * regx + α regx, regy end @@ -187,7 +187,7 @@ type SegmentsIterator end function iter_segments(args...) tup = Plots.wraptuple(args) - n = _maximum(map(length, tup)) + n = maximum(map(length, tup)) SegmentsIterator(tup, n) end @@ -283,7 +283,7 @@ unzip{T}(xyuv::FixedSizeArrays.Vec{4,T}) = T[xyuv[1]], T[xyuv[2]], T[xyuv[ # given 2-element lims and a vector of data x, widen lims to account for the extrema of x function _expand_limits(lims, x) try - e1, e2 = _extrema(x) + e1, e2 = NaNMath.extrema(x) lims[1] = NaNMath.min(lims[1], e1) lims[2] = NaNMath.max(lims[2], e2) # catch err @@ -334,17 +334,17 @@ sortedkeys(d::Dict) = sort(collect(keys(d))) "create an (n+1) list of the outsides of heatmap rectangles" function heatmap_edges(v::AVec) - vmin, vmax = _extrema(v) + vmin, vmax = NaNMath.extrema(v) extra = 0.5 * (vmax-vmin) / (length(v)-1) vcat(vmin-extra, 0.5 * (v[1:end-1] + v[2:end]), vmax+extra) end function calc_r_extrema(x, y) - xmin, xmax = _extrema(x) - ymin, ymax = _extrema(y) + xmin, xmax = NaNMath.extrema(x) + ymin, ymax = NaNMath.extrema(y) r = 0.5 * NaNMath.min(xmax - xmin, ymax - ymin) - _extrema(r) + NaNMath.extrema(r) end function convert_to_polar(x, y, r_extrema = calc_r_extrema(x, y)) @@ -644,8 +644,8 @@ end # --------------------------------------------------------------- # used in updating an existing series -extendSeriesByOne(v::UnitRange{Int}, n::Int = 1) = isempty(v) ? (1:n) : (_minimum(v):_maximum(v)+n) -extendSeriesByOne(v::AVec, n::Integer = 1) = isempty(v) ? (1:n) : vcat(v, (1:n) + _maximum(v)) +extendSeriesByOne(v::UnitRange{Int}, n::Int = 1) = isempty(v) ? (1:n) : (minimum(v):maximum(v)+n) +extendSeriesByOne(v::AVec, n::Integer = 1) = isempty(v) ? (1:n) : vcat(v, (1:n) + NaNMath.maximum(v)) extendSeriesData{T}(v::Range{T}, z::Real) = extendSeriesData(float(collect(v)), z) extendSeriesData{T}(v::Range{T}, z::AVec) = extendSeriesData(float(collect(v)), z) extendSeriesData{T}(v::AVec{T}, z::Real) = (push!(v, convert(T, z)); v) @@ -871,9 +871,9 @@ mm2px(mm::Real) = float(px / MM_PER_PX) "Smallest x in plot" -xmin(plt::Plot) = _minimum([_minimum(series.d[:x]) for series in plt.series_list]) +xmin(plt::Plot) = NaNMath.minimum([NaNMath.minimum(series.d[:x]) for series in plt.series_list]) "Largest x in plot" -xmax(plt::Plot) = _maximum([_maximum(series.d[:x]) for series in plt.series_list]) +xmax(plt::Plot) = NaNMath.maximum([NaNMath.maximum(series.d[:x]) for series in plt.series_list]) "Extrema of x-values in plot" -_extrema(plt::Plot) = (xmin(plt), xmax(plt)) +NaNMath.extrema(plt::Plot) = (xmin(plt), xmax(plt)) diff --git a/test/runtests.jl b/test/runtests.jl index 6e4d3f98..d7d4441c 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -78,12 +78,12 @@ facts("Axes") do @fact typeof(axis) --> Plots.Axis @fact Plots.discrete_value!(axis, "HI") --> (0.5, 1) @fact Plots.discrete_value!(axis, :yo) --> (1.5, 2) - @fact Plots._extrema(axis) --> (0.5,1.5) + @fact Plots.NaNMath.extrema(axis) --> (0.5,1.5) @fact axis[:discrete_map] --> Dict{Any,Any}(:yo => 2, "HI" => 1) Plots.discrete_value!(axis, ["x$i" for i=1:5]) Plots.discrete_value!(axis, ["x$i" for i=0:2]) - @fact Plots._extrema(axis) --> (0.5, 7.5) + @fact Plots.NaNMath.extrema(axis) --> (0.5, 7.5) end