mantao/Plots.jl
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/master'

Browse Source
This commit is contained in:
Samuel S. Watson 2019-01-16 09:44:03 -05:00
commit f0a3ca4314
3 changed files with 22 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
src/animation.jl
View File

@ -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)

25
src/backends/plotlyjs.jl
View File

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

18
src/recipes.jl
View File

@ -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, vs, StatsBase.Weights(weights), edges, closed = :left)
StatsBase.fit(StatsBase.Histogram, localvs, edges, closed = :left) :
StatsBase.fit(StatsBase.Histogram, localvs, StatsBase.Weights(weights), edges, closed = :left)
)
normalize!(h, mode = _hist_norm_mode(normed))
end