467 lines
17 KiB
Julia
467 lines
17 KiB
Julia
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# create a new "build_series_args" which converts all inputs into xs = Any[xitems], ys = Any[yitems].
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# Special handling for: no args, xmin/xmax, parametric, dataframes
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# Then once inputs have been converted, build the series args, map functions, etc.
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# This should cut down on boilerplate code and allow more focused dispatch on type
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# note: returns meta information... mainly for use with automatic labeling from DataFrames for now
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typealias FuncOrFuncs @compat(Union{Function, AVec{Function}})
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all3D(d::KW) = trueOrAllTrue(lt -> lt in (:contour, :heatmap, :surface, :wireframe, :contour3d), get(d, :linetype, :none))
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# missing
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convertToAnyVector(v::@compat(Void), d::KW) = Any[nothing], nothing
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# fixed number of blank series
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convertToAnyVector(n::Integer, d::KW) = Any[zeros(0) for i in 1:n], nothing
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# numeric vector
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convertToAnyVector{T<:Number}(v::AVec{T}, d::KW) = Any[v], nothing
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# string vector
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convertToAnyVector{T<:@compat(AbstractString)}(v::AVec{T}, d::KW) = Any[v], nothing
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# numeric matrix
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function convertToAnyVector{T<:Number}(v::AMat{T}, d::KW)
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if all3D(d)
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Any[Surface(v)]
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else
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Any[v[:,i] for i in 1:size(v,2)]
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end, nothing
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end
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# other matrix... vector of columns
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function convertToAnyVector(m::AMat, d::KW)
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Any[begin
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v = vec(m[:,i])
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length(v) == 1 ? v[1] : v
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end for i=1:size(m,2)], nothing
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end
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# function
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convertToAnyVector(f::Function, d::KW) = Any[f], nothing
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# surface
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convertToAnyVector(s::Surface, d::KW) = Any[s], nothing
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# vector of OHLC
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convertToAnyVector(v::AVec{OHLC}, d::KW) = Any[v], nothing
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# dates
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convertToAnyVector{D<:Union{Date,DateTime}}(dts::AVec{D}, d::KW) = Any[dts], nothing
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# list of things (maybe other vectors, functions, or something else)
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function convertToAnyVector(v::AVec, d::KW)
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if all(x -> typeof(x) <: Number, v)
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# all real numbers wrap the whole vector as one item
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Any[convert(Vector{Float64}, v)], nothing
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else
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# something else... treat each element as an item
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vcat(Any[convertToAnyVector(vi, d)[1] for vi in v]...), nothing
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# Any[vi for vi in v], nothing
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end
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end
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# --------------------------------------------------------------------
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# TODO: can we avoid the copy here? one error that crops up is that mapping functions over the same array
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# result in that array being shared. push!, etc will add too many items to that array
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compute_x(x::Void, y::Void, z) = 1:size(z,1)
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compute_x(x::Void, y, z) = 1:size(y,1)
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compute_x(x::Function, y, z) = map(x, y)
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compute_x(x, y, z) = copy(x)
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# compute_y(x::Void, y::Function, z) = error()
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compute_y(x::Void, y::Void, z) = 1:size(z,2)
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compute_y(x, y::Function, z) = map(y, x)
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compute_y(x, y, z) = copy(y)
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compute_z(x, y, z::Function) = map(z, x, y)
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compute_z(x, y, z::AbstractMatrix) = Surface(z)
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compute_z(x, y, z::Void) = nothing
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compute_z(x, y, z) = copy(z)
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@noinline function compute_xyz(x, y, z)
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x = compute_x(x,y,z)
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y = compute_y(x,y,z)
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z = compute_z(x,y,z)
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x, y, z
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end
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# not allowed
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compute_xyz(x::Void, y::FuncOrFuncs, z) = error("If you want to plot the function `$y`, you need to define the x values!")
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compute_xyz(x::Void, y::Void, z::FuncOrFuncs) = error("If you want to plot the function `$z`, you need to define x and y values!")
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compute_xyz(x::Void, y::Void, z::Void) = error("x/y/z are all nothing!")
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# --------------------------------------------------------------------
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# create n=max(mx,my) series arguments. the shorter list is cycled through
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# note: everything should flow through this
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function build_series_args(plt::AbstractPlot, kw::KW) #, idxfilter)
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x, y, z = map(sym -> pop!(kw, sym, nothing), (:x, :y, :z))
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if nothing == x == y == z
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return [], nothing, nothing
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end
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xs, xmeta = convertToAnyVector(x, kw)
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ys, ymeta = convertToAnyVector(y, kw)
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zs, zmeta = convertToAnyVector(z, kw)
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fr = pop!(kw, :fillrange, nothing)
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fillranges, _ = if typeof(fr) <: Number
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([fr],nothing)
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else
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convertToAnyVector(fr, kw)
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end
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mx = length(xs)
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my = length(ys)
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mz = length(zs)
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ret = Any[]
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for i in 1:max(mx, my, mz)
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# try to set labels using ymeta
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d = copy(kw)
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if !haskey(d, :label) && ymeta != nothing
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if isa(ymeta, Symbol)
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d[:label] = string(ymeta)
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elseif isa(ymeta, AVec{Symbol})
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d[:label] = string(ymeta[mod1(i,length(ymeta))])
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end
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end
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# build the series arg dict
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numUncounted = pop!(d, :numUncounted, 0)
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commandIndex = i + numUncounted
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n = plt.n + i
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dumpdict(d, "before getSeriesArgs")
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d = getSeriesArgs(plt.backend, getplotargs(plt, n), d, commandIndex, convertSeriesIndex(plt, n), n)
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dumpdict(d, "after getSeriesArgs")
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d[:x], d[:y], d[:z] = compute_xyz(xs[mod1(i,mx)], ys[mod1(i,my)], zs[mod1(i,mz)])
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lt = d[:linetype]
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# for linetype `line`, need to sort by x values
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if lt == :line
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# order by x
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indices = sortperm(d[:x])
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d[:x] = d[:x][indices]
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d[:y] = d[:y][indices]
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d[:linetype] = :path
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end
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# special handling for missing x in box plot... all the same category
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if lt == :box && xs[mod1(i,mx)] == nothing
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d[:x] = ones(Int, length(d[:y]))
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end
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# map functions to vectors
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if isa(d[:marker_z], Function)
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d[:marker_z] = map(d[:marker_z], d[:x])
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end
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# @show fillranges
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d[:fillrange] = fillranges[mod1(i,length(fillranges))]
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if isa(d[:fillrange], Function)
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d[:fillrange] = map(d[:fillrange], d[:x])
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end
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# handle error bars
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for esym in (:xerror, :yerror)
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if get(d, esym, nothing) != nothing
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# we make a copy of the KW and apply an errorbar recipe
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append!(ret, apply_series_recipe(copy(d), Val{esym}))
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end
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end
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# handle ribbons
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if get(d, :ribbon, nothing) != nothing
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rib = d[:ribbon]
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d[:fillrange] = (d[:y] - rib, d[:y] + rib)
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end
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# handle quiver plots
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if lt == :quiver
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d[:linetype] = lt = :path
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d[:linewidth] = 0
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end
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if get(d, :quiver, nothing) != nothing
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append!(ret, apply_series_recipe(copy(d), Val{:quiver}))
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end
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# now that we've processed a given series... optionally split into
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# multiple dicts through a recipe (for example, a box plot is split into component
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# parts... polygons, lines, and scatters)
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# note: we pass in a Val type (i.e. Val{:box}) so that we can dispatch on the linetype
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kwlist = apply_series_recipe(d, Val{lt})
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append!(ret, kwlist)
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# # add it to our series list
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# push!(ret, d)
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end
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ret, xmeta, ymeta
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end
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# --------------------------------------------------------------------
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# process_inputs
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# --------------------------------------------------------------------
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# These methods take a plot and the keyword arguments, and processes the input
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# arguments (x/y/z, group, etc), populating the KW dict with appropriate values.
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# --------------------------------------------------------------------
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# 0 arguments
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# --------------------------------------------------------------------
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# don't do anything
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function process_inputs(plt::AbstractPlot, d::KW)
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end
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# --------------------------------------------------------------------
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# 1 argument
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# --------------------------------------------------------------------
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function process_inputs(plt::AbstractPlot, d::KW, n::Integer)
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d[:x], d[:y], d[:z] = zeros(0), zeros(0), zeros(0)
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end
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# no special handling... assume x and z are nothing
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function process_inputs(plt::AbstractPlot, d::KW, y)
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d[:y] = y
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end
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# matrix... is it z or y?
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, mat::AMat{T})
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if all3D(d)
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n,m = size(mat)
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d[:x], d[:y], d[:z] = 1:n, 1:m, mat
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else
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d[:y] = mat
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end
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end
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# plotting arbitrary shapes/polygons
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function process_inputs(plt::AbstractPlot, d::KW, shape::Shape)
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d[:x], d[:y] = shape_coords(shape)
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d[:linetype] = :shape
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end
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function process_inputs(plt::AbstractPlot, d::KW, shapes::AVec{Shape})
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d[:x], d[:y] = shape_coords(shapes)
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d[:linetype] = :shape
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end
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function process_inputs(plt::AbstractPlot, d::KW, shapes::AMat{Shape})
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x, y = [], []
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for j in 1:size(shapes, 2)
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tmpx, tmpy = shape_coords(vec(shapes[:,j]))
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push!(x, tmpx)
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push!(y, tmpy)
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end
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d[:x], d[:y] = x, y
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d[:linetype] = :shape
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end
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# function without range... use the current range of the x-axis
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function process_inputs(plt::AbstractPlot, d::KW, f::FuncOrFuncs)
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process_inputs(plt, d, f, xmin(plt), xmax(plt))
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end
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# --------------------------------------------------------------------
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# 2 arguments
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# --------------------------------------------------------------------
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function process_inputs(plt::AbstractPlot, d::KW, x, y)
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d[:x], d[:y] = x, y
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end
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# if functions come first, just swap the order (not to be confused with parametric functions...
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# as there would be more than one function passed in)
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function process_inputs(plt::AbstractPlot, d::KW, f::FuncOrFuncs, x)
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@assert !(typeof(x) <: FuncOrFuncs) # otherwise we'd hit infinite recursion here
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process_inputs(plt, d, x, f)
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end
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# --------------------------------------------------------------------
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# 3 arguments
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# --------------------------------------------------------------------
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# no special handling... just pass them through
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function process_inputs(plt::AbstractPlot, d::KW, x, y, z)
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d[:x], d[:y], d[:z] = x, y, z
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end
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# 3d line or scatter
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function process_inputs(plt::AbstractPlot, d::KW, x::AVec, y::AVec, zvec::AVec)
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# default to path3d if we haven't set a 3d linetype
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lt = get(d, :linetype, :none)
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if lt == :scatter
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d[:linetype] = :scatter3d
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elseif !(lt in _3dTypes)
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d[:linetype] = :path3d
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end
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d[:x], d[:y], d[:z] = x, y, zvec
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end
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# surface-like... function
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function process_inputs{TX,TY}(plt::AbstractPlot, d::KW, x::AVec{TX}, y::AVec{TY}, zf::Function)
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x = TX <: Number ? sort(x) : x
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y = TY <: Number ? sort(y) : y
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# x, y = sort(x), sort(y)
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d[:z] = Surface(zf, x, y) # TODO: replace with SurfaceFunction when supported
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d[:x], d[:y] = x, y
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end
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# surface-like... matrix grid
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function process_inputs{TX,TY,TZ}(plt::AbstractPlot, d::KW, x::AVec{TX}, y::AVec{TY}, zmat::AMat{TZ})
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@assert size(zmat) == (length(x), length(y))
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if TX <: Number && !issorted(x)
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idx = sortperm(x)
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x, zmat = x[idx], zmat[idx, :]
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end
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if TY <: Number && !issorted(y)
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idx = sortperm(y)
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y, zmat = y[idx], zmat[:, idx]
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end
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d[:x], d[:y], d[:z] = x, y, Surface{Matrix{TZ}}(zmat)
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if !like_surface(get(d, :linetype, :none))
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d[:linetype] = :contour
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end
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end
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# surfaces-like... general x, y grid
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, x::AMat{T}, y::AMat{T}, zmat::AMat{T})
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@assert size(zmat) == size(x) == size(y)
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# d[:x], d[:y], d[:z] = Any[x], Any[y], Surface{Matrix{Float64}}(zmat)
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d[:x], d[:y], d[:z] = map(Surface{Matrix{Float64}}, (x, y, zmat))
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if !like_surface(get(d, :linetype, :none))
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d[:linetype] = :contour
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end
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end
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# --------------------------------------------------------------------
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# Parametric functions
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# --------------------------------------------------------------------
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# special handling... xmin/xmax with function(s)
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function process_inputs(plt::AbstractPlot, d::KW, f::FuncOrFuncs, xmin::Number, xmax::Number)
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width = get(plt.plotargs, :size, (100,))[1]
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x = linspace(xmin, xmax, width)
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process_inputs(plt, d, x, f)
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end
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# special handling... xmin/xmax with parametric function(s)
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process_inputs{T<:Number}(plt::AbstractPlot, d::KW, fx::FuncOrFuncs, fy::FuncOrFuncs, u::AVec{T}) = process_inputs(plt, d, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u))
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process_inputs{T<:Number}(plt::AbstractPlot, d::KW, u::AVec{T}, fx::FuncOrFuncs, fy::FuncOrFuncs) = process_inputs(plt, d, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u))
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process_inputs(plt::AbstractPlot, d::KW, fx::FuncOrFuncs, fy::FuncOrFuncs, umin::Number, umax::Number, numPoints::Int = 1000) = process_inputs(plt, d, fx, fy, linspace(umin, umax, numPoints))
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# special handling... 3D parametric function(s)
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process_inputs{T<:Number}(plt::AbstractPlot, d::KW, fx::FuncOrFuncs, fy::FuncOrFuncs, fz::FuncOrFuncs, u::AVec{T}) = process_inputs(plt, d, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u), mapFuncOrFuncs(fz, u))
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process_inputs{T<:Number}(plt::AbstractPlot, d::KW, u::AVec{T}, fx::FuncOrFuncs, fy::FuncOrFuncs, fz::FuncOrFuncs) = process_inputs(plt, d, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u), mapFuncOrFuncs(fz, u))
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process_inputs(plt::AbstractPlot, d::KW, fx::FuncOrFuncs, fy::FuncOrFuncs, fz::FuncOrFuncs, umin::Number, umax::Number, numPoints::Int = 1000) = process_inputs(plt, d, fx, fy, fz, linspace(umin, umax, numPoints))
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# --------------------------------------------------------------------
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# Lists of tuples and FixedSizeArrays
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# --------------------------------------------------------------------
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# if we get an unhandled tuple, just splat it in
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function process_inputs(plt::AbstractPlot, d::KW, tup::Tuple)
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process_inputs(plt, d, tup...)
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end
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# (x,y) tuples
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function process_inputs{R1<:Number,R2<:Number}(plt::AbstractPlot, d::KW, xy::AVec{Tuple{R1,R2}})
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process_inputs(plt, d, unzip(xy)...)
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end
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function process_inputs{R1<:Number,R2<:Number}(plt::AbstractPlot, d::KW, xy::Tuple{R1,R2})
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process_inputs(plt, d, [xy[1]], [xy[2]])
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end
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# (x,y,z) tuples
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function process_inputs{R1<:Number,R2<:Number,R3<:Number}(plt::AbstractPlot, d::KW, xyz::AVec{Tuple{R1,R2,R3}})
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process_inputs(plt, d, unzip(xyz)...)
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end
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function process_inputs{R1<:Number,R2<:Number,R3<:Number}(plt::AbstractPlot, d::KW, xyz::Tuple{R1,R2,R3})
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process_inputs(plt, d, [xyz[1]], [xyz[2]], [xyz[3]])
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end
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# 2D FixedSizeArrays
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, xy::AVec{FixedSizeArrays.Vec{2,T}})
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process_inputs(plt, d, unzip(xy)...)
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end
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, xy::FixedSizeArrays.Vec{2,T})
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process_inputs(plt, d, [xy[1]], [xy[2]])
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end
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# 3D FixedSizeArrays
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, xyz::AVec{FixedSizeArrays.Vec{3,T}})
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process_inputs(plt, d, unzip(xyz)...)
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end
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function process_inputs{T<:Number}(plt::AbstractPlot, d::KW, xyz::FixedSizeArrays.Vec{3,T})
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process_inputs(plt, d, [xyz[1]], [xyz[2]], [xyz[3]])
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end
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# --------------------------------------------------------------------
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# handle grouping
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# --------------------------------------------------------------------
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# function process_inputs(plt::AbstractPlot, d::KW, groupby::GroupBy, args...)
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# ret = Any[]
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# error("unfinished after series reorg")
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# for (i,glab) in enumerate(groupby.groupLabels)
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# # TODO: don't automatically overwrite labels
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# kwlist, xmeta, ymeta = process_inputs(plt, d, args...,
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# idxfilter = groupby.groupIds[i],
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# label = string(glab),
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# numUncounted = length(ret)) # we count the idx from plt.n + numUncounted + i
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# append!(ret, kwlist)
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# end
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# ret, nothing, nothing # TODO: handle passing meta through
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# end
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# --------------------------------------------------------------------
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# For DataFrame support. Imports DataFrames and defines the necessary methods which support them.
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# --------------------------------------------------------------------
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function setup_dataframes()
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@require DataFrames begin
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get_data(df::DataFrames.AbstractDataFrame, arg::Symbol) = df[arg]
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get_data(df::DataFrames.AbstractDataFrame, arg) = arg
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function process_inputs(plt::AbstractPlot, d::KW, df::DataFrames.AbstractDataFrame, args...)
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# d[:dataframe] = df
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process_inputs(plt, d, map(arg -> get_data(df, arg), args)...)
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|
end
|
|
|
|
# expecting the column name of a dataframe that was passed in... anything else should error
|
|
function extractGroupArgs(s::Symbol, df::DataFrames.AbstractDataFrame, args...)
|
|
if haskey(df, s)
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|
return extractGroupArgs(df[s])
|
|
else
|
|
error("Got a symbol, and expected that to be a key in d[:dataframe]. s=$s d=$d")
|
|
end
|
|
end
|
|
|
|
# function getDataFrameFromKW(d::KW)
|
|
# get(d, :dataframe) do
|
|
# error("Missing dataframe argument!")
|
|
# end
|
|
# end
|
|
|
|
# # the conversion functions for when we pass symbols or vectors of symbols to reference dataframes
|
|
# convertToAnyVector(s::Symbol, d::KW) = Any[getDataFrameFromKW(d)[s]], s
|
|
# convertToAnyVector(v::AVec{Symbol}, d::KW) = (df = getDataFrameFromKW(d); Any[df[s] for s in v]), v
|
|
|
|
end
|
|
end
|