RFC: Ignore NaNs on 0.6 (like on 0.5): fixes #796 (#876)

Replaces min, max, minimum, mean, maximum and extrema with NaNMath versions in places where NaNs can occur.

To avoid returning NaN when there are NaNs in the Vector

* Also add maximum and minimum

* define _-prefaced versions of mean, maximum, minimum, extrema

* variable arg numbers for Base methods

* Different implementation of the override

* remove underscore from 2-arg versions of maximum

* some forgotten extrema -> _extrema

* Fix bug in _extrema definition

* edit comment

* replace min and max with _min and _max

* Base NaN-compliant functions on NaNMath


replace _min and _max with NaNMath versions

* Use NaNMath explicitly everywhere

* remove unneccesary NaNMath calls

* Ensure ceil does not error on NaN

* Added one more maximum in gr

REQUIRE

@@ -9,3 +9,4 @@ Measures
 Showoff
 StatsBase 0.14.0
 JSON
+NaNMath

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,

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)
-Base.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}(
@@ -260,8 +260,8 @@ end
 
 
 function expand_extrema!(ex::Extrema, v::Number)
-    ex.emin = min(v, ex.emin)
+    ex.emin = NaNMath.min(v, ex.emin)
-    ex.emax = max(v, ex.emax)
+    ex.emax = NaNMath.max(v, ex.emax)
     ex
 end
 
@@ -276,8 +276,8 @@ expand_extrema!(axis::Axis, ::Bool) = axis[:extrema]
 
 function expand_extrema!{MIN<:Number,MAX<:Number}(axis::Axis, v::Tuple{MIN,MAX})
     ex = axis[:extrema]
-    ex.emin = min(v[1], ex.emin)
+    ex.emin = NaNMath.min(v[1], ex.emin)
-    ex.emax = max(v[2], ex.emax)
+    ex.emax = NaNMath.max(v[2], ex.emax)
     ex
 end
 function expand_extrema!{N<:Number}(axis::Axis, v::AVec{N})
@@ -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) + 0.5maximum(bw))
+        expand_extrema!(axis, NaNMath.maximum(data) + 0.5maximum(bw))
-        expand_extrema!(axis, minimum(data) - 0.5minimum(bw))
+        expand_extrema!(axis, NaNMath.minimum(data) - 0.5minimum(bw))
     end
 
 end
@@ -368,8 +368,8 @@ end
 # push the limits out slightly
 function widen(lmin, lmax)
     span = lmax - lmin
-    # eps = max(1e-16, min(1e-2span, 1e-10))
+    # eps = NaNMath.max(1e-16, min(1e-2span, 1e-10))
-    eps = max(1e-16, 0.03span)
+    eps = NaNMath.max(1e-16, 0.03span)
     lmin-eps, lmax+eps
 end
 
@@ -425,7 +425,7 @@ function discrete_value!(axis::Axis, dv)
     # @show axis[:discrete_map], axis[:discrete_values], dv
     if cv_idx == -1
         ex = axis[:extrema]
-        cv = max(0.5, ex.emax + 1.0)
+        cv = NaNMath.max(0.5, ex.emax + 1.0)
         expand_extrema!(axis, cv)
         push!(axis[:discrete_values], dv)
         push!(axis[:continuous_values], cv)

src/backends/glvisualize.jl

@@ -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
@@ -367,14 +367,14 @@ end
 
 
 dist(a, b) = abs(a-b)
-mindist(x, a, b) = min(dist(a, x), dist(b, x))
+mindist(x, a, b) = NaNMath.min(dist(a, x), dist(b, x))
 
 function gappy(x, ps)
     n = length(ps)
     x <= first(ps) && return first(ps) - x
     for j=1:(n-1)
         p0 = ps[j]
-        p1 = ps[min(j+1, n)]
+        p1 = ps[NaNMath.min(j+1, n)]
         if p0 <= x && p1 >= x
             return mindist(x, p0, p1) * (isodd(j) ? 1 : -1)
         end
@@ -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}

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
@@ -428,7 +428,7 @@ function gr_set_viewport_polar()
     ymax -= 0.05 * (xmax - xmin)
     xcenter = 0.5 * (xmin + xmax)
     ycenter = 0.5 * (ymin + ymax)
-    r = 0.5 * min(xmax - xmin, ymax - ymin)
+    r = 0.5 * NaNMath.min(xmax - xmin, ymax - ymin)
     GR.setviewport(xcenter -r, xcenter + r, ycenter - r, ycenter + r)
     GR.setwindow(-1, 1, -1, 1)
     r
@@ -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(NaNMath.minimum(r), NaNMath.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)
                 GR.setwindow(xmin, xmax, zmin, zmax)
                 GR.cellarray(xmin, xmax, zmax, zmin, 1, length(l), l)
                 ztick = 0.5 * GR.tick(zmin, zmax)

src/backends/inspectdr.jl

@@ -349,7 +349,7 @@ function _inspectdr_setupsubplot(sp::Subplot{InspectDRBackend})
         ymin, ymax  = axis_limits(yaxis)
         if ispolar(sp)
             #Plots.jl appears to give (xmin,xmax) ≜ (Θmin,Θmax) & (ymin,ymax) ≜ (rmin,rmax)
-            rmax = max(abs(ymin), abs(ymax))
+            rmax = NaNMath.max(abs(ymin), abs(ymax))
             xmin, xmax = -rmax, rmax
             ymin, ymax = -rmax, rmax
         end

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

src/backends/pyplot.jl

@@ -705,11 +705,11 @@ function py_add_series(plt::Plot{PyPlotBackend}, series::Series)
             # contours on the axis planes
             if series[:contours]
                 for (zdir,mat) in (("x",x), ("y",y), ("z",z))
-                    offset = (zdir == "y" ? maximum : minimum)(mat)
+                    offset = (zdir == "y" ? NaNMath.maximum : NaNMath.minimum)(mat)
                     handle = ax[:contourf](x, y, z, levelargs...;
                         zdir = zdir,
                         cmap = py_fillcolormap(series),
-                        offset = (zdir == "y" ? maximum : minimum)(mat)  # where to draw the contour plane
+                        offset = (zdir == "y" ? NaNMath.maximum : NaNMath.minimum)(mat)  # where to draw the contour plane
                     )
                     push!(handles, handle)
                     needs_colorbar = true
@@ -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
 
@@ -926,14 +926,14 @@ function py_compute_axis_minval(axis::Axis)
         for series in series_list(sp)
             v = series.d[axis[:letter]]
             if !isempty(v)
-                minval = min(minval, minimum(abs(v)))
+                minval = NaNMath.min(minval, NaNMath.minimum(abs(v)))
             end
         end
     end
 
     # now if the axis limits go to a smaller abs value, use that instead
     vmin, vmax = axis_limits(axis)
-    minval = min(minval, abs(vmin), abs(vmax))
+    minval = NaNMath.min(minval, abs(vmin), abs(vmax))
 
     minval
 end
@@ -954,7 +954,7 @@ function py_set_scale(ax, axis::Axis)
         elseif scale == :log10
             10
         end
-        kw[Symbol(:linthresh,letter)] = max(1e-16, py_compute_axis_minval(axis))
+        kw[Symbol(:linthresh,letter)] = NaNMath.max(1e-16, py_compute_axis_minval(axis))
         "symlog"
     end
     func(arg; kw...)

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
 
-Base.mean(x::Real, y::Real) = 0.5*(x+y)
+# 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
-Base.mean{N,T<:Real}(ps::FixedSizeArrays.Vec{N,T}...) = sum(ps) / length(ps)
+# mean{N,T<:Real}(ps::FixedSizeArrays.Vec{N,T}...) = sum(ps) / length(ps) # I also could not see this used anywhere, and it's type piracy - implementing a NaNMath version for this would just involve converting to a standard array
 
 @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

src/layouts.jl

@@ -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

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,

src/recipes.jl

@@ -225,7 +225,7 @@ end
         fr = if yaxis[:scale] == :identity
             0.0
         else
-            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
-        0.5mean(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_min = NaNMath.minimum(w_adj)
-    w_max = maximum(w_adj)
+    w_max = NaNMath.maximum(w_adj)
     baseline = _binbarlike_baseline(w_min, wscale)
     w_adj, baseline
 end
@@ -548,9 +548,9 @@ Plots.@deps stepbins path
 
 
 function _auto_binning_nbins{N}(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto)
-    _cl(x) = max(ceil(Int, x), 1)
+    _cl(x) = ceil(Int, NaNMath.max(x, one(x)))
     _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 = 0.3mean(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)
+    xlim := NaNMath.extrema(cs)
-    ylim := extrema(rs)
+    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...)

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[max(2, 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
@@ -283,9 +283,9 @@ 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] = min(lims[1], e1)
+    lims[1] = NaNMath.min(lims[1], e1)
-    lims[2] = max(lims[2], e2)
+    lims[2] = NaNMath.max(lims[2], e2)
   # catch err
   #   warn(err)
   end
@@ -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)
+    xmin, xmax = NaNMath.extrema(x)
-    ymin, ymax = extrema(y)
+    ymin, ymax = NaNMath.extrema(y)
-    r = 0.5 * min(xmax - xmin, ymax - ymin)
+    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))
@@ -645,7 +645,7 @@ 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::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"
-Base.extrema(plt::Plot) = (xmin(plt), xmax(plt))
+NaNMath.extrema(plt::Plot) = (xmin(plt), xmax(plt))

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 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 extrema(axis) --> (0.5, 7.5)
+    @fact Plots.NaNMath.extrema(axis) --> (0.5, 7.5)
 end