433 lines
14 KiB
Julia
433 lines
14 KiB
Julia
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type CurrentPlot
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nullableplot::Nullable{PlottingObject}
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end
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const CURRENT_PLOT = CurrentPlot(Nullable{PlottingObject}())
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isplotnull() = isnull(CURRENT_PLOT.nullableplot)
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function current()
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if isplotnull()
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error("No current plot/subplot")
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end
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get(CURRENT_PLOT.nullableplot)
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end
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current(plot::PlottingObject) = (CURRENT_PLOT.nullableplot = Nullable(plot))
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# ---------------------------------------------------------
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Base.string(plt::Plot) = "Plot{$(plt.backend) n=$(plt.n)}"
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Base.print(io::IO, plt::Plot) = print(io, string(plt))
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Base.show(io::IO, plt::Plot) = print(io, string(plt))
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getplot(plt::Plot) = plt
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getinitargs(plt::Plot, idx::Int = 1) = plt.initargs
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convertSeriesIndex(plt::Plot, n::Int) = n
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# ---------------------------------------------------------
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"""
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The main plot command. Use `plot` to create a new plot object, and `plot!` to add to an existing one:
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```
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plot(args...; kw...) # creates a new plot window, and sets it to be the current
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plot!(args...; kw...) # adds to the `current`
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plot!(plotobj, args...; kw...) # adds to the plot `plotobj`
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```
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There are lots of ways to pass in data, and lots of keyword arguments... just try it and it will likely work as expected.
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When you pass in matrices, it splits by columns. See the documentation for more info.
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"""
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# this creates a new plot with args/kw and sets it to be the current plot
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function plot(args...; kw...)
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pkg = backend()
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d = Dict(kw)
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preprocessArgs!(d)
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dumpdict(d, "After plot preprocessing")
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plotargs = getPlotArgs(pkg, d, 1)
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dumpdict(plotargs, "Plot args")
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plt = plot(pkg; plotargs...) # create a new, blank plot
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delete!(d, :background_color)
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plot!(plt, args...; d...) # add to it
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end
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# this adds to the current plot, or creates a new plot if none are current
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function plot!(args...; kw...)
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local plt
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try
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plt = current()
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catch
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return plot(args...; kw...)
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end
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plot!(current(), args...; kw...)
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end
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# not allowed:
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function plot!(subplt::Subplot, args...; kw...)
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error("Can't call plot! on a Subplot!")
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end
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# this adds to a specific plot... most plot commands will flow through here
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function plot!(plt::Plot, args...; kw...)
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d = Dict(kw)
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preprocessArgs!(d)
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dumpdict(d, "After plot! preprocessing")
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warnOnUnsupportedArgs(plt.backend, d)
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# grouping
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groupargs = get(d, :group, nothing) == nothing ? [] : [extractGroupArgs(d[:group], args...)]
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# just in case the backend needs to set up the plot (make it current or something)
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preparePlotUpdate(plt)
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# get the list of dictionaries, one per series
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kwList, xmeta, ymeta = createKWargsList(plt, groupargs..., args...; d...)
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# if we were able to extract guide information from the series inputs, then update the plot
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# @show xmeta, ymeta
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updateDictWithMeta(d, plt.initargs, xmeta, true)
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updateDictWithMeta(d, plt.initargs, ymeta, false)
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# now we can plot the series
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for (i,di) in enumerate(kwList)
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plt.n += 1
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setTicksFromStringVector(d, di, :x, :xticks)
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setTicksFromStringVector(d, di, :y, :yticks)
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# remove plot args
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for k in keys(_plotDefaults)
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delete!(di, k)
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end
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dumpdict(di, "Series $i")
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plot!(plt.backend, plt; di...)
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end
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addAnnotations(plt, d)
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warnOnUnsupportedScales(plt.backend, d)
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# add title, axis labels, ticks, etc
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if !haskey(d, :subplot)
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merge!(plt.initargs, d)
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dumpdict(plt.initargs, "Updating plot items")
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updatePlotItems(plt, plt.initargs)
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end
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updatePositionAndSize(plt, d)
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current(plt)
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# NOTE: lets ignore the show param and effectively use the semicolon at the end of the REPL statement
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# # do we want to show it?
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if haskey(d, :show) && d[:show]
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gui()
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end
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plt
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end
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# --------------------------------------------------------------------
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# if x or y are a vector of strings, we should create a list of unique strings,
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# and map x/y to be the index of the string... then set the x/y tick labels
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function setTicksFromStringVector(d::Dict, di::Dict, sym::Symbol, ticksym::Symbol)
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# if the x or y values are strings, set ticks to the unique values, and x/y to the indices of the ticks
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v = di[sym]
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isa(v, AbstractArray) || return
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T = eltype(v)
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if T <: @compat(AbstractString) || (!isempty(T.types) && all(x -> x <: @compat(AbstractString), T.types))
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ticks = unique(di[sym])
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di[sym] = Int[findnext(ticks, v, 1) for v in di[sym]]
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if !haskey(d, ticksym) || d[ticksym] == :auto
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d[ticksym] = (collect(1:length(ticks)), UTF8String[t for t in ticks])
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end
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end
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end
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# --------------------------------------------------------------------
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preparePlotUpdate(plt::Plot) = nothing
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# --------------------------------------------------------------------
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# should we update the x/y label given the meta info during input slicing?
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function updateDictWithMeta(d::Dict, initargs::Dict, meta::Symbol, isx::Bool)
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lsym = isx ? :xlabel : :ylabel
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if initargs[lsym] == default(lsym)
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d[lsym] = string(meta)
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end
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end
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updateDictWithMeta(d::Dict, initargs::Dict, meta, isx::Bool) = nothing
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# --------------------------------------------------------------------
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annotations(::@compat(Void)) = []
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annotations{X,Y,V}(v::AVec{@compat(Tuple{X,Y,V})}) = v
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annotations{X,Y,V}(t::@compat(Tuple{X,Y,V})) = [t]
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annotations(anns) = error("Expecting a tuple (or vector of tuples) for annotations: ",
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"(x, y, annotation)\n got: $(typeof(anns))")
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function addAnnotations(plt::Plot, d::Dict)
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anns = annotations(get(d, :annotation, nothing))
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if !isempty(anns)
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addAnnotations(plt, anns)
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end
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end
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# --------------------------------------------------------------------
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# create a new "createKWargsList" 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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# missing
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convertToAnyVector(v::@compat(Void); kw...) = Any[nothing], nothing
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# fixed number of blank series
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convertToAnyVector(n::Integer; kw...) = Any[zeros(0) for i in 1:n], nothing
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# numeric vector
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convertToAnyVector{T<:Real}(v::AVec{T}; kw...) = Any[v], nothing
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# string vector
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convertToAnyVector{T<:@compat(AbstractString)}(v::AVec{T}; kw...) = Any[v], nothing
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# numeric matrix
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convertToAnyVector{T<:Real}(v::AMat{T}; kw...) = Any[v[:,i] for i in 1:size(v,2)], nothing
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# function
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convertToAnyVector(f::Function; kw...) = Any[f], nothing
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# vector of OHLC
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convertToAnyVector(v::AVec{OHLC}; kw...) = Any[v], nothing
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# list of things (maybe other vectors, functions, or something else)
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convertToAnyVector(v::AVec; kw...) = Any[vi for vi in v], nothing
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# --------------------------------------------------------------------
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# in computeXandY, we take in any of the possible items, convert into proper x/y vectors, then return.
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# this is also where all the "set x to 1:length(y)" happens, and also where we assert on lengths.
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computeX(x::@compat(Void), y) = 1:length(y)
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computeX(x, y) = copy(x)
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computeY(x, y::Function) = map(y, x)
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computeY(x, y) = copy(y)
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function computeXandY(x, y)
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if x == nothing && isa(y, Function)
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error("If you want to plot the function `$y`, you need to define the x values somehow!")
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end
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x, y = computeX(x,y), computeY(x,y)
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@assert length(x) == length(y)
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x, y
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end
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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 createKWargsList(plt::PlottingObject, x, y; kw...)
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xs, xmeta = convertToAnyVector(x; kw...)
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ys, ymeta = convertToAnyVector(y; kw...)
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# _debugMode.on && dumpcallstack()
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mx = length(xs)
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my = length(ys)
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ret = Any[]
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for i in 1:max(mx, my)
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# try to set labels using ymeta
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d = Dict(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 = get(d, :numUncounted, 0)
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n = plt.n + i + numUncounted
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dumpdict(d, "before getSeriesArgs")
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d = getSeriesArgs(plt.backend, getinitargs(plt, n), d, i + numUncounted, convertSeriesIndex(plt, n), n)
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dumpdict(d, "after getSeriesArgs")
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d[:x], d[:y] = computeXandY(xs[mod1(i,mx)], ys[mod1(i,my)])
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if haskey(d, :idxfilter)
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d[:x] = d[:x][d[:idxfilter]]
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d[:y] = d[:y][d[:idxfilter]]
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end
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# for linetype `line`, need to sort by x values
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if d[:linetype] == :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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# cleanup those fields that were used only for generating kw args
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for k in (:idxfilter, :numUncounted, :dataframe)
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delete!(d, k)
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end
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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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# handle grouping
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function createKWargsList(plt::PlottingObject, groupby::GroupBy, args...; kw...)
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ret = Any[]
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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 = createKWargsList(plt, args...; kw...,
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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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# pass it off to the x/y version
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function createKWargsList(plt::PlottingObject, y; kw...)
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createKWargsList(plt, nothing, y; kw...)
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end
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# contours or surfaces... irregular data
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function createKWargsList(plt::PlottingObject, x::AVec, y::AVec, zvec::AVec; kw...)
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error("TODO: contours or surfaces... irregular data")
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end
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# contours or surfaces... function grid
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function createKWargsList(plt::PlottingObject, x::AVec, y::AVec, zf::Function; kw...)
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# only allow sorted x/y for now
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# TODO: auto sort x/y/z properly
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@assert x == sort(x)
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@assert y == sort(y)
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surface = Float64[zf(xi, yi) for xi in x, yi in y]
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createKWargsList(plt, x, y, surface; kw...) # passes it to the zmat version
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end
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# contours or surfaces... matrix grid
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function createKWargsList{T<:Real}(plt::PlottingObject, x::AVec, y::AVec, zmat::AMat{T}; kw...)
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# only allow sorted x/y for now
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# TODO: auto sort x/y/z properly
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@assert x == sort(x)
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@assert y == sort(y)
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@assert size(zmat) == (length(x), length(y))
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surf = Array(Any,1,1)
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surf[1,1] = convert(Matrix{Float64}, zmat)
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createKWargsList(plt, x, y; kw..., surface = surf, linetype = :contour)
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end
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function createKWargsList(plt::PlottingObject, f::FuncOrFuncs; kw...)
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error("Can't pass a Function or Vector{Function} for y without also passing x")
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end
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# list of functions
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function createKWargsList(plt::PlottingObject, f::FuncOrFuncs, x; kw...)
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@assert !(typeof(x) <: FuncOrFuncs) # otherwise we'd hit infinite recursion here
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createKWargsList(plt, x, f; kw...)
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end
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# special handling... xmin/xmax with function(s)
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function createKWargsList(plt::PlottingObject, f::FuncOrFuncs, xmin::Real, xmax::Real; kw...)
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width = plt.initargs[:size][1]
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x = collect(linspace(xmin, xmax, width)) # we don't need more than the width
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createKWargsList(plt, x, f; kw...)
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end
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mapFuncOrFuncs(f::Function, u::AVec) = map(f, u)
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mapFuncOrFuncs(fs::AVec{Function}, u::AVec) = [map(f, u) for f in fs]
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# special handling... xmin/xmax with parametric function(s)
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createKWargsList{T<:Real}(plt::PlottingObject, fx::FuncOrFuncs, fy::FuncOrFuncs, u::AVec{T}; kw...) = createKWargsList(plt, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u); kw...)
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createKWargsList{T<:Real}(plt::PlottingObject, u::AVec{T}, fx::FuncOrFuncs, fy::FuncOrFuncs; kw...) = createKWargsList(plt, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u); kw...)
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createKWargsList(plt::PlottingObject, fx::FuncOrFuncs, fy::FuncOrFuncs, umin::Real, umax::Real, numPoints::Int = 1000; kw...) = createKWargsList(plt, fx, fy, linspace(umin, umax, numPoints); kw...)
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# special handling... no args... 1 series
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function createKWargsList(plt::PlottingObject; kw...)
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d = Dict(kw)
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if !haskey(d, :y)
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# assume we just want to create an empty plot object which can be added to later
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return [], nothing, nothing
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# error("Called plot/subplot without args... must set y in the keyword args. Example: plot(; y=rand(10))")
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end
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if haskey(d, :x)
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return createKWargsList(plt, d[:x], d[:y]; kw...)
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else
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return createKWargsList(plt, d[:y]; kw...)
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end
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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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function dataframes()
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@eval import DataFrames
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@eval function createKWargsList(plt::PlottingObject, df::DataFrames.DataFrame, args...; kw...)
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createKWargsList(plt, args...; kw..., dataframe = df)
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end
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# expecting the column name of a dataframe that was passed in... anything else should error
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@eval function extractGroupArgs(s::Symbol, df::DataFrames.DataFrame, args...)
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if haskey(df, s)
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return extractGroupArgs(df[s])
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else
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error("Got a symbol, and expected that to be a key in d[:dataframe]. s=$s d=$d")
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end
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end
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@eval function getDataFrameFromKW(; kw...)
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for (k,v) in kw
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if k == :dataframe
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return v
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end
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end
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error("Missing dataframe argument in arguments!")
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end
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# the conversion functions for when we pass symbols or vectors of symbols to reference dataframes
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@eval convertToAnyVector(s::Symbol; kw...) = Any[getDataFrameFromKW(;kw...)[s]], s
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@eval convertToAnyVector(v::AVec{Symbol}; kw...) = (df = getDataFrameFromKW(;kw...); Any[df[s] for s in v]), v
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end
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# --------------------------------------------------------------------
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