880 lines
25 KiB
Julia
880 lines
25 KiB
Julia
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calcMidpoints(edges::AbstractVector) = Float64[0.5 * (edges[i] + edges[i+1]) for i in 1:length(edges)-1]
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"Make histogram-like bins of data"
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function binData(data, nbins)
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lo, hi = ignorenan_extrema(data)
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edges = collect(linspace(lo, hi, nbins+1))
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midpoints = calcMidpoints(edges)
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buckets = Int[max(2, min(searchsortedfirst(edges, x), length(edges)))-1 for x in data]
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counts = zeros(Int, length(midpoints))
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for b in buckets
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counts[b] += 1
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end
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edges, midpoints, buckets, counts
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end
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"""
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A hacky replacement for a histogram when the backend doesn't support histograms directly.
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Convert it into a bar chart with the appropriate x/y values.
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"""
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function histogramHack(; kw...)
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d = KW(kw)
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# we assume that the y kwarg is set with the data to be binned, and nbins is also defined
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edges, midpoints, buckets, counts = binData(d[:y], d[:bins])
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d[:x] = midpoints
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d[:y] = float(counts)
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d[:seriestype] = :bar
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d[:fillrange] = d[:fillrange] == nothing ? 0.0 : d[:fillrange]
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d
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end
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"""
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A hacky replacement for a bar graph when the backend doesn't support bars directly.
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Convert it into a line chart with fillrange set.
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"""
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function barHack(; kw...)
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d = KW(kw)
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midpoints = d[:x]
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heights = d[:y]
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fillrange = d[:fillrange] == nothing ? 0.0 : d[:fillrange]
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# estimate the edges
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dists = diff(midpoints) * 0.5
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edges = zeros(length(midpoints)+1)
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for i in 1:length(edges)
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if i == 1
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edge = midpoints[1] - dists[1]
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elseif i == length(edges)
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edge = midpoints[i-1] + dists[i-2]
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else
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edge = midpoints[i-1] + dists[i-1]
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end
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edges[i] = edge
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end
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x = Float64[]
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y = Float64[]
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for i in 1:length(heights)
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e1, e2 = edges[i:i+1]
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append!(x, [e1, e1, e2, e2])
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append!(y, [fillrange, heights[i], heights[i], fillrange])
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end
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d[:x] = x
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d[:y] = y
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d[:seriestype] = :path
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d[:fillrange] = fillrange
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d
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end
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"""
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A hacky replacement for a sticks graph when the backend doesn't support sticks directly.
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Convert it into a line chart that traces the sticks, and a scatter that sets markers at the points.
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"""
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function sticksHack(; kw...)
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dLine = KW(kw)
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dScatter = copy(dLine)
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# these are the line vertices
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x = Float64[]
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y = Float64[]
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fillrange = dLine[:fillrange] == nothing ? 0.0 : dLine[:fillrange]
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# calculate the vertices
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yScatter = dScatter[:y]
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for (i,xi) in enumerate(dScatter[:x])
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yi = yScatter[i]
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for j in 1:3 push!(x, xi) end
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append!(y, [fillrange, yScatter[i], fillrange])
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end
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# change the line args
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dLine[:x] = x
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dLine[:y] = y
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dLine[:seriestype] = :path
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dLine[:markershape] = :none
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dLine[:fillrange] = nothing
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# change the scatter args
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dScatter[:seriestype] = :none
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dLine, dScatter
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end
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function regressionXY(x, y)
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# regress
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β, α = convert(Matrix{Float64}, [x ones(length(x))]) \ convert(Vector{Float64}, y)
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# make a line segment
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regx = [ignorenan_minimum(x), ignorenan_maximum(x)]
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regy = β * regx + α
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regx, regy
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end
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function replace_image_with_heatmap{T<:Colorant}(z::Array{T})
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@show T, size(z)
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n, m = size(z)
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# idx = 0
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colors = ColorGradient(vec(z))
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newz = reshape(linspace(0, 1, n*m), n, m)
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newz, colors
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# newz = zeros(n, m)
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# for i=1:n, j=1:m
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# push!(colors, T(z[i,j]...))
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# newz[i,j] = idx / (n*m-1)
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# idx += 1
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# end
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# newz, ColorGradient(colors)
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end
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function imageHack(d::KW)
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is_seriestype_supported(:heatmap) || error("Neither :image or :heatmap are supported!")
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d[:seriestype] = :heatmap
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d[:z], d[:fillcolor] = replace_image_with_heatmap(d[:z].surf)
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end
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# ---------------------------------------------------------------
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type Segments{T}
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pts::Vector{T}
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end
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# Segments() = Segments{Float64}(zeros(0))
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Segments() = Segments(Float64)
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Segments{T}(::Type{T}) = Segments(T[])
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Segments(p::Int) = Segments(NTuple{2,Float64}[])
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# Segments() = Segments(zeros(0))
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to_nan(::Type{Float64}) = NaN
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to_nan(::Type{NTuple{2,Float64}}) = (NaN, NaN)
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coords(segs::Segments{Float64}) = segs.pts
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coords(segs::Segments{NTuple{2,Float64}}) = Float64[p[1] for p in segs.pts], Float64[p[2] for p in segs.pts]
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function Base.push!{T}(segments::Segments{T}, vs...)
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if !isempty(segments.pts)
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push!(segments.pts, to_nan(T))
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end
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for v in vs
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push!(segments.pts, convert(T,v))
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end
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segments
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end
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function Base.push!{T}(segments::Segments{T}, vs::AVec)
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if !isempty(segments.pts)
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push!(segments.pts, to_nan(T))
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end
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for v in vs
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push!(segments.pts, convert(T,v))
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end
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segments
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end
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# -----------------------------------------------------
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# helper to manage NaN-separated segments
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type SegmentsIterator
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args::Tuple
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n::Int
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end
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function iter_segments(args...)
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tup = Plots.wraptuple(args)
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n = maximum(map(length, tup))
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SegmentsIterator(tup, n)
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end
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# helpers to figure out if there are NaN values in a list of array types
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anynan(i::Int, args::Tuple) = any(a -> !isfinite(_cycle(a,i)), args)
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anynan(istart::Int, iend::Int, args::Tuple) = any(i -> anynan(i, args), istart:iend)
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allnan(istart::Int, iend::Int, args::Tuple) = all(i -> anynan(i, args), istart:iend)
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function Base.start(itr::SegmentsIterator)
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nextidx = 1
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if anynan(1, itr.args)
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_, nextidx = next(itr, 1)
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end
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nextidx
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end
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Base.done(itr::SegmentsIterator, nextidx::Int) = nextidx > itr.n
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function Base.next(itr::SegmentsIterator, nextidx::Int)
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i = istart = iend = nextidx
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# find the next NaN, and iend is the one before
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while i <= itr.n + 1
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if i > itr.n || anynan(i, itr.args)
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# done... array end or found NaN
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iend = i-1
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break
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end
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i += 1
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end
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# find the next non-NaN, and set nextidx
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while i <= itr.n
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if !anynan(i, itr.args)
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break
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end
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i += 1
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end
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istart:iend, i
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end
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# Find minimal type that can contain NaN and x
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# To allow use of NaN separated segments with categorical x axis
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float_extended_type{T}(x::AbstractArray{T}) = Union{T,Float64}
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float_extended_type{T<:Real}(x::AbstractArray{T}) = Float64
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# ------------------------------------------------------------------------------------
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nop() = nothing
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notimpl() = error("This has not been implemented yet")
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isnothing(x::Void) = true
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isnothing(x) = false
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_cycle(wrapper::InputWrapper, idx::Int) = wrapper.obj
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_cycle(wrapper::InputWrapper, idx::AVec{Int}) = wrapper.obj
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_cycle(v::AVec, idx::Int) = v[mod1(idx, length(v))]
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_cycle(v::AMat, idx::Int) = size(v,1) == 1 ? v[1, mod1(idx, size(v,2))] : v[:, mod1(idx, size(v,2))]
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_cycle(v, idx::Int) = v
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_cycle(v::AVec, indices::AVec{Int}) = map(i -> _cycle(v,i), indices)
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_cycle(v::AMat, indices::AVec{Int}) = map(i -> _cycle(v,i), indices)
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_cycle(v, indices::AVec{Int}) = fill(v, length(indices))
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_cycle(grad::ColorGradient, idx::Int) = _cycle(grad.colors, idx)
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_cycle(grad::ColorGradient, indices::AVec{Int}) = _cycle(grad.colors, indices)
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makevec(v::AVec) = v
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makevec{T}(v::T) = T[v]
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"duplicate a single value, or pass the 2-tuple through"
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maketuple(x::Real) = (x,x)
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maketuple{T,S}(x::Tuple{T,S}) = x
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mapFuncOrFuncs(f::Function, u::AVec) = map(f, u)
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mapFuncOrFuncs{F<:Function}(fs::AVec{F}, u::AVec) = [map(f, u) for f in fs]
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unzip{X,Y}(xy::AVec{Tuple{X,Y}}) = [t[1] for t in xy], [t[2] for t in xy]
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unzip{X,Y,Z}(xyz::AVec{Tuple{X,Y,Z}}) = [t[1] for t in xyz], [t[2] for t in xyz], [t[3] for t in xyz]
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unzip{X,Y,U,V}(xyuv::AVec{Tuple{X,Y,U,V}}) = [t[1] for t in xyuv], [t[2] for t in xyuv], [t[3] for t in xyuv], [t[4] for t in xyuv]
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unzip{T}(xy::AVec{FixedSizeArrays.Vec{2,T}}) = T[t[1] for t in xy], T[t[2] for t in xy]
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unzip{T}(xy::FixedSizeArrays.Vec{2,T}) = T[xy[1]], T[xy[2]]
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unzip{T}(xyz::AVec{FixedSizeArrays.Vec{3,T}}) = T[t[1] for t in xyz], T[t[2] for t in xyz], T[t[3] for t in xyz]
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unzip{T}(xyz::FixedSizeArrays.Vec{3,T}) = T[xyz[1]], T[xyz[2]], T[xyz[3]]
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unzip{T}(xyuv::AVec{FixedSizeArrays.Vec{4,T}}) = T[t[1] for t in xyuv], T[t[2] for t in xyuv], T[t[3] for t in xyuv], T[t[4] for t in xyuv]
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unzip{T}(xyuv::FixedSizeArrays.Vec{4,T}) = T[xyuv[1]], T[xyuv[2]], T[xyuv[3]], T[xyuv[4]]
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# given 2-element lims and a vector of data x, widen lims to account for the extrema of x
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function _expand_limits(lims, x)
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try
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e1, e2 = ignorenan_extrema(x)
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lims[1] = NaNMath.min(lims[1], e1)
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lims[2] = NaNMath.max(lims[2], e2)
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# catch err
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# warn(err)
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end
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nothing
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end
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expand_data(v, n::Integer) = [_cycle(v, i) for i=1:n]
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# if the type exists in a list, replace the first occurence. otherwise add it to the end
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function addOrReplace(v::AbstractVector, t::DataType, args...; kw...)
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for (i,vi) in enumerate(v)
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if isa(vi, t)
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v[i] = t(args...; kw...)
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return
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end
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end
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push!(v, t(args...; kw...))
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return
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end
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function replaceType(vec, val)
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filter!(x -> !isa(x, typeof(val)), vec)
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push!(vec, val)
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end
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function replaceAlias!(d::KW, k::Symbol, aliases::Dict{Symbol,Symbol})
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if haskey(aliases, k)
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d[aliases[k]] = pop!(d, k)
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end
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end
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function replaceAliases!(d::KW, aliases::Dict{Symbol,Symbol})
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ks = collect(keys(d))
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for k in ks
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replaceAlias!(d, k, aliases)
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end
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end
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createSegments(z) = collect(repmat(z',2,1))[2:end]
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Base.first(c::Colorant) = c
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Base.first(x::Symbol) = x
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sortedkeys(d::Dict) = sort(collect(keys(d)))
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"create an (n+1) list of the outsides of heatmap rectangles"
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function heatmap_edges(v::AVec)
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vmin, vmax = ignorenan_extrema(v)
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extra = 0.5 * (vmax-vmin) / (length(v)-1)
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vcat(vmin-extra, 0.5 * (v[1:end-1] + v[2:end]), vmax+extra)
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end
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function calc_r_extrema(x, y)
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xmin, xmax = ignorenan_extrema(x)
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ymin, ymax = ignorenan_extrema(y)
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r = 0.5 * NaNMath.min(xmax - xmin, ymax - ymin)
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ignorenan_extrema(r)
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end
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function convert_to_polar(x, y, r_extrema = calc_r_extrema(x, y))
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rmin, rmax = r_extrema
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phi, r = x, y
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r = (r - rmin) / (rmax - rmin)
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n = max(length(phi), length(r))
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x = zeros(n)
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y = zeros(n)
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for i in 1:n
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x[i] = _cycle(r,i) * cos.(_cycle(phi,i))
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y[i] = _cycle(r,i) * sin.(_cycle(phi,i))
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end
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x, y
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end
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function fakedata(sz...)
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y = zeros(sz...)
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for r in 2:size(y,1)
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y[r,:] = 0.95 * vec(y[r-1,:]) + randn(size(y,2))
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end
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y
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end
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isijulia() = isdefined(Main, :IJulia) && Main.IJulia.inited
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isatom() = isdefined(Main, :Atom) && Main.Atom.isconnected()
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function is_installed(pkgstr::AbstractString)
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try
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Pkg.installed(pkgstr) === nothing ? false: true
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catch
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false
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end
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end
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istuple(::Tuple) = true
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istuple(::Any) = false
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isvector(::AVec) = true
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isvector(::Any) = false
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ismatrix(::AMat) = true
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ismatrix(::Any) = false
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isscalar(::Real) = true
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isscalar(::Any) = false
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is_2tuple(v) = typeof(v) <: Tuple && length(v) == 2
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isvertical(d::KW) = get(d, :orientation, :vertical) in (:vertical, :v, :vert)
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isvertical(series::Series) = isvertical(series.d)
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ticksType{T<:Real}(ticks::AVec{T}) = :ticks
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ticksType{T<:AbstractString}(ticks::AVec{T}) = :labels
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ticksType{T<:AVec,S<:AVec}(ticks::Tuple{T,S}) = :ticks_and_labels
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ticksType(ticks) = :invalid
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limsType{T<:Real,S<:Real}(lims::Tuple{T,S}) = :limits
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limsType(lims::Symbol) = lims == :auto ? :auto : :invalid
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limsType(lims) = :invalid
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# axis_Symbol(letter, postfix) = Symbol(letter * postfix)
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# axis_symbols(letter, postfix...) = map(s -> axis_Symbol(letter, s), postfix)
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Base.convert{T<:Real}(::Type{Vector{T}}, rng::Range{T}) = T[x for x in rng]
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Base.convert{T<:Real,S<:Real}(::Type{Vector{T}}, rng::Range{S}) = T[x for x in rng]
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Base.merge(a::AbstractVector, b::AbstractVector) = sort(unique(vcat(a,b)))
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nanpush!(a::AbstractVector, b) = (push!(a, NaN); push!(a, b))
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nanappend!(a::AbstractVector, b) = (push!(a, NaN); append!(a, b))
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function nansplit(v::AVec)
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vs = Vector{eltype(v)}[]
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while true
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idx = findfirst(isnan, v)
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if idx <= 0
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# no nans
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push!(vs, v)
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break
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elseif idx > 1
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push!(vs, v[1:idx-1])
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end
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v = v[idx+1:end]
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end
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vs
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end
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function nanvcat(vs::AVec)
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v_out = zeros(0)
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for v in vs
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nanappend!(v_out, v)
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end
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v_out
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end
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# given an array of discrete values, turn it into an array of indices of the unique values
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# returns the array of indices (znew) and a vector of unique values (vals)
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function indices_and_unique_values(z::AbstractArray)
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vals = sort(unique(z))
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vmap = Dict([(v,i) for (i,v) in enumerate(vals)])
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newz = map(zi -> vmap[zi], z)
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newz, vals
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end
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# this is a helper function to determine whether we need to transpose a surface matrix.
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# it depends on whether the backend matches rows to x (transpose_on_match == true) or vice versa
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# for example: PyPlot sends rows to y, so transpose_on_match should be true
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function transpose_z(d, z, transpose_on_match::Bool = true)
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if d[:match_dimensions] == transpose_on_match
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# z'
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permutedims(z, [2,1])
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else
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z
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end
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end
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function ok(x::Number, y::Number, z::Number = 0)
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isfinite(x) && isfinite(y) && isfinite(z)
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end
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ok(tup::Tuple) = ok(tup...)
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# compute one side of a fill range from a ribbon
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function make_fillrange_side(y, rib)
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frs = zeros(length(y))
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for (i, (yi, ri)) in enumerate(zip(y, Base.Iterators.cycle(rib)))
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frs[i] = yi + ri
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end
|
||
frs
|
||
end
|
||
|
||
# turn a ribbon into a fillrange
|
||
function make_fillrange_from_ribbon(kw::KW)
|
||
y, rib = kw[:y], kw[:ribbon]
|
||
rib = wraptuple(rib)
|
||
rib1, rib2 = -first(rib), last(rib)
|
||
kw[:ribbon] = nothing
|
||
kw[:fillrange] = make_fillrange_side(y, rib1), make_fillrange_side(y, rib2)
|
||
end
|
||
|
||
function get_sp_lims(sp::Subplot, letter::Symbol)
|
||
axis_limits(sp[Symbol(letter, :axis)])
|
||
end
|
||
xlims(sp::Subplot) = get_sp_lims(sp, :x)
|
||
ylims(sp::Subplot) = get_sp_lims(sp, :y)
|
||
zlims(sp::Subplot) = get_sp_lims(sp, :z)
|
||
xlims(plt::Plot, sp_idx::Int = 1) = xlims(plt[sp_idx])
|
||
ylims(plt::Plot, sp_idx::Int = 1) = ylims(plt[sp_idx])
|
||
zlims(plt::Plot, sp_idx::Int = 1) = zlims(plt[sp_idx])
|
||
xlims(sp_idx::Int = 1) = xlims(current(), sp_idx)
|
||
ylims(sp_idx::Int = 1) = ylims(current(), sp_idx)
|
||
zlims(sp_idx::Int = 1) = zlims(current(), sp_idx)
|
||
|
||
# ---------------------------------------------------------------
|
||
|
||
makekw(; kw...) = KW(kw)
|
||
|
||
wraptuple(x::Tuple) = x
|
||
wraptuple(x) = (x,)
|
||
|
||
trueOrAllTrue(f::Function, x::AbstractArray) = all(f, x)
|
||
trueOrAllTrue(f::Function, x) = f(x)
|
||
|
||
allLineTypes(arg) = trueOrAllTrue(a -> get(_typeAliases, a, a) in _allTypes, arg)
|
||
allStyles(arg) = trueOrAllTrue(a -> get(_styleAliases, a, a) in _allStyles, arg)
|
||
allShapes(arg) = trueOrAllTrue(a -> is_marker_supported(get(_markerAliases, a, a)), arg) ||
|
||
trueOrAllTrue(a -> isa(a, Shape), arg)
|
||
allAlphas(arg) = trueOrAllTrue(a -> (typeof(a) <: Real && a > 0 && a < 1) ||
|
||
(typeof(a) <: AbstractFloat && (a == zero(typeof(a)) || a == one(typeof(a)))), arg)
|
||
allReals(arg) = trueOrAllTrue(a -> typeof(a) <: Real, arg)
|
||
allFunctions(arg) = trueOrAllTrue(a -> isa(a, Function), arg)
|
||
|
||
# ---------------------------------------------------------------
|
||
# ---------------------------------------------------------------
|
||
|
||
|
||
"""
|
||
Allows temporary setting of backend and defaults for Plots. Settings apply only for the `do` block. Example:
|
||
```
|
||
with(:gadfly, size=(400,400), type=:histogram) do
|
||
plot(rand(10))
|
||
plot(rand(10))
|
||
end
|
||
```
|
||
"""
|
||
function with(f::Function, args...; kw...)
|
||
newdefs = KW(kw)
|
||
|
||
if :canvas in args
|
||
newdefs[:xticks] = nothing
|
||
newdefs[:yticks] = nothing
|
||
newdefs[:grid] = false
|
||
newdefs[:legend] = false
|
||
end
|
||
|
||
# dict to store old and new keyword args for anything that changes
|
||
olddefs = KW()
|
||
for k in keys(newdefs)
|
||
olddefs[k] = default(k)
|
||
end
|
||
|
||
# save the backend
|
||
if CURRENT_BACKEND.sym == :none
|
||
pickDefaultBackend()
|
||
end
|
||
oldbackend = CURRENT_BACKEND.sym
|
||
|
||
for arg in args
|
||
|
||
# change backend?
|
||
if arg in backends()
|
||
backend(arg)
|
||
end
|
||
|
||
# # TODO: generalize this strategy to allow args as much as possible
|
||
# # as in: with(:gadfly, :scatter, :legend, :grid) do; ...; end
|
||
# # TODO: can we generalize this enough to also do something similar in the plot commands??
|
||
|
||
# k = :seriestype
|
||
# if arg in _allTypes
|
||
# olddefs[k] = default(k)
|
||
# newdefs[k] = arg
|
||
# elseif haskey(_typeAliases, arg)
|
||
# olddefs[k] = default(k)
|
||
# newdefs[k] = _typeAliases[arg]
|
||
# end
|
||
|
||
k = :legend
|
||
if arg in (k, :leg)
|
||
olddefs[k] = default(k)
|
||
newdefs[k] = true
|
||
end
|
||
|
||
k = :grid
|
||
if arg == k
|
||
olddefs[k] = default(k)
|
||
newdefs[k] = true
|
||
end
|
||
end
|
||
|
||
# display(olddefs)
|
||
# display(newdefs)
|
||
|
||
# now set all those defaults
|
||
default(; newdefs...)
|
||
|
||
# call the function
|
||
ret = f()
|
||
|
||
# put the defaults back
|
||
default(; olddefs...)
|
||
|
||
# revert the backend
|
||
if CURRENT_BACKEND.sym != oldbackend
|
||
backend(oldbackend)
|
||
end
|
||
|
||
# return the result of the function
|
||
ret
|
||
end
|
||
|
||
# ---------------------------------------------------------------
|
||
# ---------------------------------------------------------------
|
||
|
||
type DebugMode
|
||
on::Bool
|
||
end
|
||
const _debugMode = DebugMode(false)
|
||
|
||
function debugplots(on = true)
|
||
_debugMode.on = on
|
||
end
|
||
|
||
debugshow(x) = show(x)
|
||
debugshow(x::AbstractArray) = print(summary(x))
|
||
|
||
function dumpdict(d::KW, prefix = "", alwaysshow = false)
|
||
_debugMode.on || alwaysshow || return
|
||
println()
|
||
if prefix != ""
|
||
println(prefix, ":")
|
||
end
|
||
for k in sort(collect(keys(d)))
|
||
@printf("%14s: ", k)
|
||
debugshow(d[k])
|
||
println()
|
||
end
|
||
println()
|
||
end
|
||
DD(d::KW, prefix = "") = dumpdict(d, prefix, true)
|
||
|
||
|
||
function dumpcallstack()
|
||
error() # well... you wanted the stacktrace, didn't you?!?
|
||
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) + ignorenan_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)
|
||
extendSeriesData{T}(v::AVec{T}, z::AVec) = (append!(v, convert(Vector{T}, z)); v)
|
||
|
||
|
||
# -------------------------------------------------------
|
||
# NOTE: backends should implement the following methods to get/set the x/y/z data objects
|
||
|
||
tovec(v::AbstractVector) = v
|
||
tovec(v::Void) = zeros(0)
|
||
|
||
function getxy(plt::Plot, i::Integer)
|
||
d = plt.series_list[i].d
|
||
tovec(d[:x]), tovec(d[:y])
|
||
end
|
||
function getxyz(plt::Plot, i::Integer)
|
||
d = plt.series_list[i].d
|
||
tovec(d[:x]), tovec(d[:y]), tovec(d[:z])
|
||
end
|
||
|
||
function setxy!{X,Y}(plt::Plot, xy::Tuple{X,Y}, i::Integer)
|
||
series = plt.series_list[i]
|
||
series.d[:x], series.d[:y] = xy
|
||
sp = series.d[:subplot]
|
||
reset_extrema!(sp)
|
||
_series_updated(plt, series)
|
||
end
|
||
function setxyz!{X,Y,Z}(plt::Plot, xyz::Tuple{X,Y,Z}, i::Integer)
|
||
series = plt.series_list[i]
|
||
series.d[:x], series.d[:y], series.d[:z] = xyz
|
||
sp = series.d[:subplot]
|
||
reset_extrema!(sp)
|
||
_series_updated(plt, series)
|
||
end
|
||
|
||
function setxyz!{X,Y,Z<:AbstractMatrix}(plt::Plot, xyz::Tuple{X,Y,Z}, i::Integer)
|
||
setxyz!(plt, (xyz[1], xyz[2], Surface(xyz[3])), i)
|
||
end
|
||
|
||
|
||
# -------------------------------------------------------
|
||
# indexing notation
|
||
|
||
# Base.getindex(plt::Plot, i::Integer) = getxy(plt, i)
|
||
Base.setindex!{X,Y}(plt::Plot, xy::Tuple{X,Y}, i::Integer) = (setxy!(plt, xy, i); plt)
|
||
Base.setindex!{X,Y,Z}(plt::Plot, xyz::Tuple{X,Y,Z}, i::Integer) = (setxyz!(plt, xyz, i); plt)
|
||
|
||
# -------------------------------------------------------
|
||
|
||
# operate on individual series
|
||
|
||
function push_x!(series::Series, xi)
|
||
push!(series[:x], xi)
|
||
expand_extrema!(series[:subplot][:xaxis], xi)
|
||
return
|
||
end
|
||
function push_y!(series::Series, yi)
|
||
push!(series[:y], yi)
|
||
expand_extrema!(series[:subplot][:yaxis], yi)
|
||
return
|
||
end
|
||
function push_z!(series::Series, zi)
|
||
push!(series[:z], zi)
|
||
expand_extrema!(series[:subplot][:zaxis], zi)
|
||
return
|
||
end
|
||
|
||
function Base.push!(series::Series, yi)
|
||
x = extendSeriesByOne(series[:x])
|
||
expand_extrema!(series[:subplot][:xaxis], x[end])
|
||
series[:x] = x
|
||
push_y!(series, yi)
|
||
end
|
||
Base.push!(series::Series, xi, yi) = (push_x!(series,xi); push_y!(series,yi))
|
||
Base.push!(series::Series, xi, yi, zi) = (push_x!(series,xi); push_y!(series,yi); push_z!(series,zi))
|
||
|
||
# -------------------------------------------------------
|
||
|
||
function attr!(series::Series; kw...)
|
||
d = KW(kw)
|
||
preprocessArgs!(d)
|
||
for (k,v) in d
|
||
if haskey(_series_defaults, k)
|
||
series[k] = v
|
||
else
|
||
warn("unused key $k in series attr")
|
||
end
|
||
end
|
||
_series_updated(series[:subplot].plt, series)
|
||
series
|
||
end
|
||
|
||
function attr!(sp::Subplot; kw...)
|
||
d = KW(kw)
|
||
preprocessArgs!(d)
|
||
for (k,v) in d
|
||
if haskey(_subplot_defaults, k)
|
||
sp[k] = v
|
||
else
|
||
warn("unused key $k in subplot attr")
|
||
end
|
||
end
|
||
sp
|
||
end
|
||
|
||
# -------------------------------------------------------
|
||
# push/append for one series
|
||
|
||
# push value to first series
|
||
Base.push!(plt::Plot, y::Real) = push!(plt, 1, y)
|
||
Base.push!(plt::Plot, x::Real, y::Real) = push!(plt, 1, x, y)
|
||
Base.push!(plt::Plot, x::Real, y::Real, z::Real) = push!(plt, 1, x, y, z)
|
||
|
||
# y only
|
||
function Base.push!(plt::Plot, i::Integer, y::Real)
|
||
xdata, ydata = getxy(plt, i)
|
||
setxy!(plt, (extendSeriesByOne(xdata), extendSeriesData(ydata, y)), i)
|
||
plt
|
||
end
|
||
function Base.append!(plt::Plot, i::Integer, y::AVec)
|
||
xdata, ydata = plt[i]
|
||
if !isa(xdata, UnitRange{Int})
|
||
error("Expected x is a UnitRange since you're trying to push a y value only")
|
||
end
|
||
plt[i] = (extendSeriesByOne(xdata, length(y)), extendSeriesData(ydata, y))
|
||
plt
|
||
end
|
||
|
||
# x and y
|
||
function Base.push!(plt::Plot, i::Integer, x::Real, y::Real)
|
||
xdata, ydata = getxy(plt, i)
|
||
setxy!(plt, (extendSeriesData(xdata, x), extendSeriesData(ydata, y)), i)
|
||
plt
|
||
end
|
||
function Base.append!(plt::Plot, i::Integer, x::AVec, y::AVec)
|
||
@assert length(x) == length(y)
|
||
xdata, ydata = getxy(plt, i)
|
||
setxy!(plt, (extendSeriesData(xdata, x), extendSeriesData(ydata, y)), i)
|
||
plt
|
||
end
|
||
|
||
# x, y, and z
|
||
function Base.push!(plt::Plot, i::Integer, x::Real, y::Real, z::Real)
|
||
# @show i, x, y, z
|
||
xdata, ydata, zdata = getxyz(plt, i)
|
||
# @show xdata, ydata, zdata
|
||
setxyz!(plt, (extendSeriesData(xdata, x), extendSeriesData(ydata, y), extendSeriesData(zdata, z)), i)
|
||
plt
|
||
end
|
||
function Base.append!(plt::Plot, i::Integer, x::AVec, y::AVec, z::AVec)
|
||
@assert length(x) == length(y) == length(z)
|
||
xdata, ydata, zdata = getxyz(plt, i)
|
||
setxyz!(plt, (extendSeriesData(xdata, x), extendSeriesData(ydata, y), extendSeriesData(zdata, z)), i)
|
||
plt
|
||
end
|
||
|
||
# tuples
|
||
Base.push!{X,Y}(plt::Plot, xy::Tuple{X,Y}) = push!(plt, 1, xy...)
|
||
Base.push!{X,Y,Z}(plt::Plot, xyz::Tuple{X,Y,Z}) = push!(plt, 1, xyz...)
|
||
Base.push!{X,Y}(plt::Plot, i::Integer, xy::Tuple{X,Y}) = push!(plt, i, xy...)
|
||
Base.push!{X,Y,Z}(plt::Plot, i::Integer, xyz::Tuple{X,Y,Z}) = push!(plt, i, xyz...)
|
||
|
||
# -------------------------------------------------------
|
||
# push/append for all series
|
||
|
||
# push y[i] to the ith series
|
||
function Base.push!(plt::Plot, y::AVec)
|
||
ny = length(y)
|
||
for i in 1:plt.n
|
||
push!(plt, i, y[mod1(i,ny)])
|
||
end
|
||
plt
|
||
end
|
||
|
||
# push y[i] to the ith series
|
||
# same x for each series
|
||
function Base.push!(plt::Plot, x::Real, y::AVec)
|
||
push!(plt, [x], y)
|
||
end
|
||
|
||
# push (x[i], y[i]) to the ith series
|
||
function Base.push!(plt::Plot, x::AVec, y::AVec)
|
||
nx = length(x)
|
||
ny = length(y)
|
||
for i in 1:plt.n
|
||
push!(plt, i, x[mod1(i,nx)], y[mod1(i,ny)])
|
||
end
|
||
plt
|
||
end
|
||
|
||
# push (x[i], y[i], z[i]) to the ith series
|
||
function Base.push!(plt::Plot, x::AVec, y::AVec, z::AVec)
|
||
nx = length(x)
|
||
ny = length(y)
|
||
nz = length(z)
|
||
for i in 1:plt.n
|
||
push!(plt, i, x[mod1(i,nx)], y[mod1(i,ny)], z[mod1(i,nz)])
|
||
end
|
||
plt
|
||
end
|
||
|
||
|
||
|
||
|
||
# ---------------------------------------------------------------
|
||
|
||
|
||
# Some conversion functions
|
||
# note: I borrowed these conversion constants from Compose.jl's Measure
|
||
|
||
const PX_PER_INCH = 100
|
||
const DPI = PX_PER_INCH
|
||
const MM_PER_INCH = 25.4
|
||
const MM_PER_PX = MM_PER_INCH / PX_PER_INCH
|
||
|
||
inch2px(inches::Real) = float(inches * PX_PER_INCH)
|
||
px2inch(px::Real) = float(px / PX_PER_INCH)
|
||
inch2mm(inches::Real) = float(inches * MM_PER_INCH)
|
||
mm2inch(mm::Real) = float(mm / MM_PER_INCH)
|
||
px2mm(px::Real) = float(px * MM_PER_PX)
|
||
mm2px(mm::Real) = float(px / MM_PER_PX)
|
||
|
||
|
||
"Smallest x in plot"
|
||
xmin(plt::Plot) = ignorenan_minimum([ignorenan_minimum(series.d[:x]) for series in plt.series_list])
|
||
"Largest x in plot"
|
||
xmax(plt::Plot) = ignorenan_maximum([ignorenan_maximum(series.d[:x]) for series in plt.series_list])
|
||
|
||
"Extrema of x-values in plot"
|
||
ignorenan_extrema(plt::Plot) = (xmin(plt), xmax(plt))
|