Merge remote-tracking branch 'upstream/master'

src/animation.jl

@@ -93,13 +93,13 @@ end
 
 
 
-# write out html to view the gif... note the rand call which is a hack so the image doesn't get cached
+# write out html to view the gif
 function Base.show(io::IO, ::MIME"text/html", agif::AnimatedGif)
     ext = file_extension(agif.filename)
     write(io, if ext == "gif"
-        "<img src=\"$(relpath(agif.filename))?$(rand())>\" />"
+        "<img src=\"$(relpath(agif.filename))\" />"
     elseif ext in ("mov", "mp4")
-        "<video controls><source src=\"$(relpath(agif.filename))?$(rand())>\" type=\"video/$ext\"></video>"
+        "<video controls><source src=\"$(relpath(agif.filename)) type=\"video/$ext\"></video>"
     else
         error("Cannot show animation with extension $ext: $agif")
     end)

src/backends/plotlyjs.jl

@@ -1,5 +1,4 @@
-
+# https://github.com/sglyon/PlotlyJS.jl
-# https://github.com/spencerlyon2/PlotlyJS.jl
 
 # --------------------------------------------------------------------------------------
 
@@ -50,23 +49,11 @@ end
 
 # ----------------------------------------------------------------
 
-function _show(io::IO, ::MIME"text/html", plt::Plot{PlotlyJSBackend})
+_show(io::IO, ::MIME"text/html", plt::Plot{PlotlyJSBackend}) = show(io, MIME("text/html"), plt.o)
-    if isijulia() && !_use_remote[]
+_show(io::IO, ::MIME"image/svg+xml", plt::Plot{PlotlyJSBackend}) = PlotlyJS.savefig(io, plt.o, format="svg")
-        write(io, PlotlyJS.html_body(PlotlyJS.JupyterPlot(plt.o)))
+_show(io::IO, ::MIME"image/png", plt::Plot{PlotlyJSBackend}) = PlotlyJS.savefig(io, plt.o, format="png")
-    else
+_show(io::IO, ::MIME"application/pdf", plt::Plot{PlotlyJSBackend}) = PlotlyJS.savefig(io, plt.o, format="pdf")
-        show(io, MIME("text/html"), plt.o)
+_show(io::IO, ::MIME"image/eps", plt::Plot{PlotlyJSBackend}) = PlotlyJS.savefig(io, plt.o, format="eps")
-    end
-end
-
-function plotlyjs_save_hack(io::IO, plt::Plot{PlotlyJSBackend}, ext::String)
-    tmpfn = tempname() * "." * ext
-    PlotlyJS.savefig(plt.o, tmpfn)
-    write(io, read(open(tmpfn)))
-end
-_show(io::IO, ::MIME"image/svg+xml", plt::Plot{PlotlyJSBackend}) = plotlyjs_save_hack(io, plt, "svg")
-_show(io::IO, ::MIME"image/png", plt::Plot{PlotlyJSBackend}) = plotlyjs_save_hack(io, plt, "png")
-_show(io::IO, ::MIME"application/pdf", plt::Plot{PlotlyJSBackend}) = plotlyjs_save_hack(io, plt, "pdf")
-_show(io::IO, ::MIME"image/eps", plt::Plot{PlotlyJSBackend}) = plotlyjs_save_hack(io, plt, "eps")
 
 function write_temp_html(plt::Plot{PlotlyJSBackend})
     filename = string(tempname(), ".html")

src/recipes.jl

@@ -589,9 +589,9 @@ Plots.@deps stepbins path
 wand_edges(x...) = (@warn("Load the StatPlots package in order to use :wand bins. Defaulting to :auto", once = true); :auto)
 
 function _auto_binning_nbins(vs::NTuple{N,AbstractVector}, dim::Integer; mode::Symbol = :auto) where N
-    _cl(x) = ceil(Int, NaNMath.max(x, one(x)))
+    _cl(x) = ceil(Int, max(x, one(x)))
     _iqr(v) = (q = quantile(v, 0.75) - quantile(v, 0.25); q > 0 ? q : oftype(q, 1))
-    _span(v) = ignorenan_maximum(v) - ignorenan_minimum(v)
+    _span(v) = maximum(v) - minimum(v)
 
     n_samples = length(LinearIndices(first(vs)))
 
@@ -635,11 +635,19 @@ _hist_edges(vs::NTuple{N,AbstractVector}, binning::Union{Integer, Symbol, Abstra
 _hist_norm_mode(mode::Symbol) = mode
 _hist_norm_mode(mode::Bool) = mode ? :pdf : :none
 
+_filternans(vs::NTuple{1,AbstractVector}) = filter!.(isfinite, vs)
+function _filternans(vs::NTuple{N,AbstractVector}) where N
+    _invertedindex(v, not) = [j for (i,j) in enumerate(v) if !(i ∈ not)]
+    nots = union(Set.(findall.(!isfinite, vs))...)
+    _invertedindex.(vs, Ref(nots))
+end
+
 function _make_hist(vs::NTuple{N,AbstractVector}, binning; normed = false, weights = nothing) where N
-    edges = _hist_edges(vs, binning)
+    localvs = _filternans(vs)
+    edges = _hist_edges(localvs, binning)
     h = float( weights == nothing ?
-        StatsBase.fit(StatsBase.Histogram, vs, edges, closed = :left) :
+        StatsBase.fit(StatsBase.Histogram, localvs, edges, closed = :left) :
-        StatsBase.fit(StatsBase.Histogram, vs, StatsBase.Weights(weights), edges, closed = :left)
+        StatsBase.fit(StatsBase.Histogram, localvs, StatsBase.Weights(weights), edges, closed = :left)
     )
     normalize!(h, mode = _hist_norm_mode(normed))
 end