diff --git a/README.md b/README.md index e1d7069a..f7fa3cd2 100644 --- a/README.md +++ b/README.md @@ -221,6 +221,10 @@ Keyword | Default | Type | Aliases `:linestyle` | `solid` | Series | `:linestyles`, `:ls`, `:s`, `:style` `:linetype` | `path` | Series | `:linetypes`, `:lt`, `:t`, `:type` `:linewidth` | `1` | Series | `:linewidths`, `:lw`, `:w`, `:width` +`:link` | `false` | Plot | +`:linkfunc` | `nothing` | Plot | +`:linkx` | `false` | Plot | `:xlink` +`:linky` | `false` | Plot | `:ylink` `:marker` | `nothing` | Series | `:m`, `:mark` `:markercolor` | `match` | Series | `:markercolors`, `:mc`, `:mcolor` `:markershape` | `none` | Series | `:markershapes`, `:shape` @@ -289,19 +293,25 @@ Type | Aliases ---- | ---- `:none` | `:n`, `:no` `:auto` | `:a` -`:ellipse` | `:c`, `:circle` -`:rect` | `:r`, `:sq`, `:square` -`:diamond` | `:d` -`:utriangle` | `:^`, `:uptri`, `:uptriangle`, `:ut`, `:utri` -`:dtriangle` | `:V`, `:downtri`, `:downtriangle`, `:dt`, `:dtri`, `:v` `:cross` | `:+`, `:plus` -`:xcross` | `:X`, `:x` -`:star1` | `:s`, `:star` -`:star2` | `:s2` +`:diamond` | `:d` +`:dtriangle` | `:V`, `:downtri`, `:downtriangle`, `:dt`, `:dtri`, `:v` +`:ellipse` | `:c`, `:circle` +`:heptagon` | `:hep` `:hexagon` | `:h`, `:hex` `:octagon` | `:o`, `:oct` +`:pentagon` | `:p`, `:pent` +`:rect` | `:r`, `:sq`, `:square` +`:star4` | +`:star5` | `:s`, `:star`, `:star1` +`:star6` | +`:star7` | +`:star8` | `:s2`, `:star2` +`:utriangle` | `:^`, `:uptri`, `:uptriangle`, `:ut`, `:utri` +`:xcross` | `:X`, `:x` +__Tip__: With supported backends, you can pass a `Plots.Shape` object for the `marker`/`markershape` arguments. `Shape` takes a vector of 2-tuples in the constructor, defining the points of the polygon's shape in a unit-scaled coordinate space. To make a square, for example, you could do `Shape([(1,1),(1,-1),(-1,-1),(-1,1)])` __Tip__: You can see the default value for a given argument with `default(arg::Symbol)`, and set the default value with `default(arg::Symbol, value)` or `default(; kw...)`. For example set the default window size and whether we should show a legend with `default(size=(600,400), leg=false)`. @@ -323,7 +333,7 @@ __Tip__: When plotting multiple lines, you can set all series to use the same va ```julia plot(rand(100,4); color = [:red RGB(0,0,1)], # (Matrix) lines 1 and 3 are red, lines 2 and 4 are blue axis = :auto, # lines 1 and 3 are on the left axis, lines 2 and 4 are on the right - markershape = [:rect, :star1] # (Vector) ALL lines are passed the vector [:rect, :star1] + markershape = [:rect, :star] # (Vector) ALL lines are passed the vector [:rect, :star1] width = 5) # all lines have a width of 5 ``` @@ -339,8 +349,9 @@ __Tip__: Call `gui()` to display the plot in a window. Interactivity depends on - [x] Annotations - [x] Scales - [x] Categorical Inputs (strings, etc... for hist, bar? or can split one series into multiple?) -- [ ] Custom markers -- [ ] Special plots (boxplot, ohlc?) +- [x] Custom markers +- [ ] Contours +- [ ] Boxplots - [x] Subplots - [x] Histograms - [ ] 3D plotting diff --git a/docs/example_generation.jl b/docs/example_generation.jl index 1c6224f4..1b8b787b 100644 --- a/docs/example_generation.jl +++ b/docs/example_generation.jl @@ -71,7 +71,7 @@ const examples = PlotExample[ [:(styles = setdiff(supportedStyles(), [:auto])'), :(plot(cumsum(randn(20,length(styles)),1); style=:auto, label=map(string,styles), w=5))]), PlotExample("Marker types", "", - [:(markers = setdiff(supportedMarkers(), [:none,:auto])'), :(scatter(0.5:9.5, [fill(i-0.5,10) for i=length(markers):-1:1]; marker=:auto, label=map(string,markers), ms=12, ylim=(0,length(markers)+1)))]), + [:(markers = setdiff(supportedMarkers(), [:none,:auto,Shape])'), :(n = length(markers)), :(scatter(0.5:9.5, [fill(i-0.5,10) for i=n:-1:1]; marker=(12,:auto), lab=map(string,markers), ms=12, ylim=(0,n), bg=RGB(0.2,0.2,0.2)))]), PlotExample("Bar", "x is the midpoint of the bar. (todo: allow passing of edges instead of midpoints)", [:(bar(randn(999)))]), diff --git a/docs/readme_template.md b/docs/readme_template.md index 01b2ec4b..1120ed38 100644 --- a/docs/readme_template.md +++ b/docs/readme_template.md @@ -215,6 +215,7 @@ Markers: [[MARKERS_TABLE]] +__Tip__: With supported backends, you can pass a `Plots.Shape` object for the `marker`/`markershape` arguments. `Shape` takes a vector of 2-tuples in the constructor, defining the points of the polygon's shape in a unit-scaled coordinate space. To make a square, for example, you could do `Shape([(1,1),(1,-1),(-1,-1),(-1,1)])` __Tip__: You can see the default value for a given argument with `default(arg::Symbol)`, and set the default value with `default(arg::Symbol, value)` or `default(; kw...)`. For example set the default window size and whether we should show a legend with `default(size=(600,400), leg=false)`. @@ -252,8 +253,9 @@ __Tip__: Call `gui()` to display the plot in a window. Interactivity depends on - [x] Annotations - [x] Scales - [x] Categorical Inputs (strings, etc... for hist, bar? or can split one series into multiple?) -- [ ] Custom markers -- [ ] Special plots (boxplot, ohlc?) +- [x] Custom markers +- [ ] Contours +- [ ] Boxplots - [x] Subplots - [x] Histograms - [ ] 3D plotting diff --git a/examples/meetup/slides_20151028.ipynb b/examples/meetup/slides_20151028.ipynb index 1ba41f00..33d58587 100644 --- a/examples/meetup/slides_20151028.ipynb +++ b/examples/meetup/slides_20151028.ipynb @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { @@ -148,7 +148,7 @@ " 5 " ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +162,6 @@ " n::Int\n", " ScaryVector(n::Integer) = new(sample(1:n), n)\n", "end\n", - "Base.length(sv::ScaryVector) = sv.n\n", "Base.size(sv::ScaryVector) = (sv.n,)\n", "Base.getindex(sv::ScaryVector, i::Integer) = (i == sv.boo ? \"BOO!\" : i)\n", "\n", @@ -172,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -187,7 +186,7 @@ " 5" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -243,6 +242,43 @@ "The AbstractArray of plotting..." ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "Plot{Plots.GadflyPackage() n=1}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# setup... choose Gadfly as the backend, set some session defaults\n", + "using Plots\n", + "gadfly()\n", + "default(size=(400,300), legend=false)\n", + "\n", + "# create parametric functions\n", + "fx(u) = 1.6sin(u)^3\n", + "fy(u) = 0.1 + 1.5cos(u) - 0.6cos(2u) - 0.25cos(3u) - cos(4u)/8\n", + "\n", + "# plot and annotate\n", + "plot(fx, fy, 0, 2π, line=(5,:darkred), xlim=(-2,2), ylim=(-2,2))\n", + "annotate!(0, 0, text(\" I ♡\\nPlots\", 26, -0.1π, :darkred))" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -255,9 +291,9 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "Plot{Plots.GadflyPackage() n=1}" + "Plot{Plots.GadflyPackage() n=2}" ] }, "execution_count": 8, @@ -266,42 +302,18 @@ } ], "source": [ - "using Plots\n", - "gadfly()\n", - "default(size=(300,300), legend=false)\n", - "fx(u) = 1.1sin(u)^3\n", - "fy(u) = cos(u) - 0.4cos(2u) - 0.15cos(3u) - cos(4u)/13\n", - "plot(fx, fy, 0, 2π, leg=false, xlim=(-2,2), ylim=(-2,2))\n", - "title!(\"I ♡ Plots\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "Plot{Plots.GadflyPackage() n=2}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ + "# use the same parametric functions to create a custom marker\n", "us = linspace(0, 2π, 100)\n", "heart = Shape([(fx(u), -fy(u)) for u in us])\n", - "x, y, z = Vector[0.5randn(20) for i in 1:3]\n", - "scatter!(x, y, z=z, c=:reds, m=(heart,15))" + "\n", + "# generate some data\n", + "n = 20\n", + "x, y, z = Vector[4rand(n)-2 for i in 1:3]\n", + "\n", + "# add to the plot\n", + "title!(\"Let me count the ways...\")\n", + "scatter!(x, y, z=z, c=:reds, m=(heart,15),\n", + "ann=[(x[i], y[i], text(i)) for i in 1:n])" ] }, { @@ -311,7 +323,10 @@ "slide_type": "slide" } }, - "source": [] + "source": [ + "## Statistics and Learning\n", + "- Established organizations and" + ] }, { "cell_type": "markdown", diff --git a/img/supported/Plots.supportGraphArgs.png b/img/supported/Plots.supportGraphArgs.png index 52874a48..d448aa6a 100644 Binary files a/img/supported/Plots.supportGraphArgs.png and b/img/supported/Plots.supportGraphArgs.png differ diff --git a/img/supported/Plots.supportGraphAxes.png b/img/supported/Plots.supportGraphAxes.png index 6c3d5962..72a0a9e8 100644 Binary files a/img/supported/Plots.supportGraphAxes.png and b/img/supported/Plots.supportGraphAxes.png differ diff --git a/img/supported/Plots.supportGraphMarkers.png b/img/supported/Plots.supportGraphMarkers.png index 862afc6e..f4bc5195 100644 Binary files a/img/supported/Plots.supportGraphMarkers.png and b/img/supported/Plots.supportGraphMarkers.png differ diff --git a/img/supported/Plots.supportGraphScales.png b/img/supported/Plots.supportGraphScales.png index e565cd31..379b7194 100644 Binary files a/img/supported/Plots.supportGraphScales.png and b/img/supported/Plots.supportGraphScales.png differ diff --git a/img/supported/Plots.supportGraphStyles.png b/img/supported/Plots.supportGraphStyles.png index 0a74acaf..b7f4d365 100644 Binary files a/img/supported/Plots.supportGraphStyles.png and b/img/supported/Plots.supportGraphStyles.png differ diff --git a/img/supported/Plots.supportGraphTypes.png b/img/supported/Plots.supportGraphTypes.png index 1ef71648..954c39c4 100644 Binary files a/img/supported/Plots.supportGraphTypes.png and b/img/supported/Plots.supportGraphTypes.png differ diff --git a/src/Plots.jl b/src/Plots.jl index 71eb4cec..2427745b 100644 --- a/src/Plots.jl +++ b/src/Plots.jl @@ -60,6 +60,8 @@ export dataframes, Shape, + PlotText, + text, OHLC, colorscheme, @@ -125,39 +127,43 @@ ohlc(args...; kw...) = plot(args...; kw..., linetype = :ohlc) ohlc!(args...; kw...) = plot!(args...; kw..., linetype = :ohlc) -title!(s::@compat(AbstractString)) = plot!(title = s) -xlabel!(s::@compat(AbstractString)) = plot!(xlabel = s) -ylabel!(s::@compat(AbstractString)) = plot!(ylabel = s) -xlims!{T<:Real,S<:Real}(lims::@compat(Tuple{T,S})) = plot!(xlims = lims) -ylims!{T<:Real,S<:Real}(lims::@compat(Tuple{T,S})) = plot!(ylims = lims) -xlims!(xmin::Real, xmax::Real) = plot!(xlims = (xmin,xmax)) -ylims!(ymin::Real, ymax::Real) = plot!(ylims = (ymin,ymax)) -xticks!{T<:Real}(v::AVec{T}) = plot!(xticks = v) -yticks!{T<:Real}(v::AVec{T}) = plot!(yticks = v) -xticks!{T<:Real,S<:@compat(AbstractString)}(ticks::AVec{T}, labels::AVec{S}) = plot!(xticks = (ticks,labels)) -yticks!{T<:Real,S<:@compat(AbstractString)}(ticks::AVec{T}, labels::AVec{S}) = plot!(yticks = (ticks,labels)) -annotate!(anns) = plot!(annotation = anns) -xflip!(flip::Bool = true) = plot!(xflip = flip) -yflip!(flip::Bool = true) = plot!(yflip = flip) -xaxis!(args...) = plot!(xaxis = args) -yaxis!(args...) = plot!(yaxis = args) +title!(s::@compat(AbstractString); kw...) = plot!(; title = s, kw...) +xlabel!(s::@compat(AbstractString); kw...) = plot!(; xlabel = s, kw...) +ylabel!(s::@compat(AbstractString); kw...) = plot!(; ylabel = s, kw...) +xlims!{T<:Real,S<:Real}(lims::@compat(Tuple{T,S}); kw...) = plot!(; xlims = lims, kw...) +ylims!{T<:Real,S<:Real}(lims::@compat(Tuple{T,S}); kw...) = plot!(; ylims = lims, kw...) +xlims!(xmin::Real, xmax::Real; kw...) = plot!(; xlims = (xmin,xmax), kw...) +ylims!(ymin::Real, ymax::Real; kw...) = plot!(; ylims = (ymin,ymax), kw...) +xticks!{T<:Real}(v::AVec{T}; kw...) = plot!(; xticks = v, kw...) +yticks!{T<:Real}(v::AVec{T}; kw...) = plot!(; yticks = v, kw...) +xticks!{T<:Real,S<:@compat(AbstractString)}( + ticks::AVec{T}, labels::AVec{S}; kw...) = plot!(; xticks = (ticks,labels), kw...) +yticks!{T<:Real,S<:@compat(AbstractString)}( + ticks::AVec{T}, labels::AVec{S}; kw...) = plot!(; yticks = (ticks,labels), kw...) +annotate!(anns...; kw...) = plot!(; annotation = anns, kw...) +xflip!(flip::Bool = true; kw...) = plot!(; xflip = flip, kw...) +yflip!(flip::Bool = true; kw...) = plot!(; yflip = flip, kw...) +xaxis!(args...; kw...) = plot!(; xaxis = args, kw...) +yaxis!(args...; kw...) = plot!(; yaxis = args, kw...) -title!(plt::Plot, s::@compat(AbstractString)) = plot!(plt; title = s) -xlabel!(plt::Plot, s::@compat(AbstractString)) = plot!(plt; xlabel = s) -ylabel!(plt::Plot, s::@compat(AbstractString)) = plot!(plt; ylabel = s) -xlims!{T<:Real,S<:Real}(plt::Plot, lims::@compat(Tuple{T,S})) = plot!(plt; xlims = lims) -ylims!{T<:Real,S<:Real}(plt::Plot, lims::@compat(Tuple{T,S})) = plot!(plt; ylims = lims) -xlims!(plt::Plot, xmin::Real, xmax::Real) = plot!(plt; xlims = (xmin,xmax)) -ylims!(plt::Plot, ymin::Real, ymax::Real) = plot!(plt; ylims = (ymin,ymax)) -xticks!{T<:Real}(plt::Plot, ticks::AVec{T}) = plot!(plt; xticks = ticks) -yticks!{T<:Real}(plt::Plot, ticks::AVec{T}) = plot!(plt; yticks = ticks) -xticks!{T<:Real,S<:@compat(AbstractString)}(plt::Plot, ticks::AVec{T}, labels::AVec{S}) = plot!(plt; xticks = (ticks,labels)) -yticks!{T<:Real,S<:@compat(AbstractString)}(plt::Plot, ticks::AVec{T}, labels::AVec{S}) = plot!(plt; yticks = (ticks,labels)) -annotate!(plt::Plot, anns) = plot!(plt; annotation = anns) -xflip!(plt::Plot, flip::Bool = true) = plot!(plt; xflip = flip) -yflip!(plt::Plot, flip::Bool = true) = plot!(plt; yflip = flip) -xaxis!(plt::Plot, args...) = plot!(plt; xaxis = args) -yaxis!(plt::Plot, args...) = plot!(plt; yaxis = args) +title!(plt::Plot, s::@compat(AbstractString); kw...) = plot!(plt; title = s, kw...) +xlabel!(plt::Plot, s::@compat(AbstractString); kw...) = plot!(plt; xlabel = s, kw...) +ylabel!(plt::Plot, s::@compat(AbstractString); kw...) = plot!(plt; ylabel = s, kw...) +xlims!{T<:Real,S<:Real}(plt::Plot, lims::@compat(Tuple{T,S}); kw...) = plot!(plt; xlims = lims, kw...) +ylims!{T<:Real,S<:Real}(plt::Plot, lims::@compat(Tuple{T,S}); kw...) = plot!(plt; ylims = lims, kw...) +xlims!(plt::Plot, xmin::Real, xmax::Real; kw...) = plot!(plt; xlims = (xmin,xmax), kw...) +ylims!(plt::Plot, ymin::Real, ymax::Real; kw...) = plot!(plt; ylims = (ymin,ymax), kw...) +xticks!{T<:Real}(plt::Plot, ticks::AVec{T}; kw...) = plot!(plt; xticks = ticks, kw...) +yticks!{T<:Real}(plt::Plot, ticks::AVec{T}; kw...) = plot!(plt; yticks = ticks, kw...) +xticks!{T<:Real,S<:@compat(AbstractString)}(plt::Plot, + ticks::AVec{T}, labels::AVec{S}; kw...) = plot!(plt; xticks = (ticks,labels), kw...) +yticks!{T<:Real,S<:@compat(AbstractString)}(plt::Plot, + ticks::AVec{T}, labels::AVec{S}; kw...) = plot!(plt; yticks = (ticks,labels), kw...) +annotate!(plt::Plot, anns...; kw...) = plot!(plt; annotation = anns, kw...) +xflip!(plt::Plot, flip::Bool = true; kw...) = plot!(plt; xflip = flip, kw...) +yflip!(plt::Plot, flip::Bool = true; kw...) = plot!(plt; yflip = flip, kw...) +xaxis!(plt::Plot, args...; kw...) = plot!(plt; xaxis = args, kw...) +yaxis!(plt::Plot, args...; kw...) = plot!(plt; yaxis = args, kw...) # --------------------------------------------------------- diff --git a/src/args.jl b/src/args.jl index 81116c02..f61ae239 100644 --- a/src/args.jl +++ b/src/args.jl @@ -615,7 +615,9 @@ function getSeriesArgs(pkg::PlottingPackage, initargs::Dict, kw, commandIndex::I aliasesAndAutopick(d, :markershape, _markerAliases, supportedMarkers(pkg), plotIndex) # update color + dumpdict(d, "before color") d[:color] = getSeriesRGBColor(d[:color], initargs, plotIndex) + dumpdict(d, "after color") # update markercolor mc = d[:markercolor] diff --git a/src/backends/gadfly.jl b/src/backends/gadfly.jl index 12e778fc..3f6cc9ee 100644 --- a/src/backends/gadfly.jl +++ b/src/backends/gadfly.jl @@ -497,6 +497,20 @@ function createGadflyAnnotationObject(x, y, val::@compat(AbstractString)) )) end +function createGadflyAnnotationObject(x, y, txt::PlotText) + halign = (txt.halign == :hcenter ? Compose.hcenter : (txt.halign == :left ? Compose.hleft : Compose.hright)) + valign = (txt.valign == :vcenter ? Compose.vcenter : (txt.valign == :top ? Compose.vtop : Compose.vbottom)) + rotations = (txt.rotation == 0.0 ? [] : [Compose.Rotation(txt.rotation, Compose.Point(Compose.x_measure(x), Compose.y_measure(y)))]) + Gadfly.Guide.annotation(Compose.compose( + Compose.context(), + Compose.text(x, y, txt.str, halign, valign, rotations...), + Compose.font(string(txt.family)), + Compose.fontsize(txt.pointsize * Gadfly.pt), + Compose.stroke(txt.color), + Compose.fill(txt.color) + )) +end + function addAnnotations{X,Y,V}(plt::Plot{GadflyPackage}, anns::AVec{@compat(Tuple{X,Y,V})}) for ann in anns push!(plt.o.guides, createGadflyAnnotationObject(ann...)) diff --git a/src/plot.jl b/src/plot.jl index b2109113..0617c68a 100644 --- a/src/plot.jl +++ b/src/plot.jl @@ -102,6 +102,7 @@ function plot!(plt::Plot, args...; kw...) preparePlotUpdate(plt) # get the list of dictionaries, one per series + dumpdict(d, "before createKWargsList") kwList, xmeta, ymeta = createKWargsList(plt, groupargs..., args...; d...) # if we were able to extract guide information from the series inputs, then update the plot @@ -185,8 +186,8 @@ updateDictWithMeta(d::Dict, initargs::Dict, meta, isx::Bool) = nothing # -------------------------------------------------------------------- annotations(::@compat(Void)) = [] -annotations{X<:Real, Y<:Real, V}(v::AVec{@compat(Tuple{X,Y,V})}) = v -annotations{X<:Real, Y<:Real, V}(t::@compat(Tuple{X,Y,V})) = [t] +annotations{X,Y,V}(v::AVec{@compat(Tuple{X,Y,V})}) = v +annotations{X,Y,V}(t::@compat(Tuple{X,Y,V})) = [t] annotations(anns) = error("Expecting a tuple (or vector of tuples) for annotations: ", "(x, y, annotation)\n got: $(typeof(anns))") @@ -259,6 +260,9 @@ end function createKWargsList(plt::PlottingObject, x, y; kw...) xs, xmeta = convertToAnyVector(x; kw...) ys, ymeta = convertToAnyVector(y; kw...) + + # _debugMode.on && dumpcallstack() + mx = length(xs) my = length(ys) ret = Any[] @@ -277,7 +281,9 @@ function createKWargsList(plt::PlottingObject, x, y; kw...) # build the series arg dict numUncounted = get(d, :numUncounted, 0) n = plt.n + i + numUncounted + dumpdict(d, "before getSeriesArgs") d = getSeriesArgs(plt.backend, getinitargs(plt, n), d, i + numUncounted, convertSeriesIndex(plt, n), n) + dumpdict(d, "after getSeriesArgs") d[:x], d[:y] = computeXandY(xs[mod1(i,mx)], ys[mod1(i,my)]) if haskey(d, :idxfilter) @@ -325,6 +331,16 @@ function createKWargsList(plt::PlottingObject, y; kw...) createKWargsList(plt, nothing, y; kw...) end +function createKWargsList(plt::PlottingObject, x::AVec, y::AVec, z::AVec; kw...) + error("TODO: contours or surfaces... irregular data") +end +function createKWargsList(plt::PlottingObject, x::AVec, y::AVec, z::Function; kw...) + error("TODO: contours or surfaces... function grid") +end +function createKWargsList(plt::PlottingObject, x::AVec, y::AVec, z::AMat; kw...) + error("TODO: contours or surfaces... grid") +end + function createKWargsList(plt::PlottingObject, f::FuncOrFuncs; kw...) error("Can't pass a Function or Vector{Function} for y without also passing x") end @@ -347,7 +363,7 @@ mapFuncOrFuncs(fs::AVec{Function}, u::AVec) = [map(f, u) for f in fs] # special handling... xmin/xmax with parametric function(s) createKWargsList{T<:Real}(plt::PlottingObject, fx::FuncOrFuncs, fy::FuncOrFuncs, u::AVec{T}; kw...) = createKWargsList(plt, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u); kw...) createKWargsList{T<:Real}(plt::PlottingObject, u::AVec{T}, fx::FuncOrFuncs, fy::FuncOrFuncs; kw...) = createKWargsList(plt, mapFuncOrFuncs(fx, u), mapFuncOrFuncs(fy, u); kw...) -createKWargsList(plt::PlottingObject, fx::FuncOrFuncs, fy::FuncOrFuncs, umin::Real, umax::Real, numPoints::Int = 1000; kw...) = createKWargsList(plt, fx, fy, linspace(umin, umax, numPoints)) +createKWargsList(plt::PlottingObject, fx::FuncOrFuncs, fy::FuncOrFuncs, umin::Real, umax::Real, numPoints::Int = 1000; kw...) = createKWargsList(plt, fx, fy, linspace(umin, umax, numPoints); kw...) diff --git a/src/types.jl b/src/types.jl index 3242c4e1..db3a6fe6 100644 --- a/src/types.jl +++ b/src/types.jl @@ -51,85 +51,6 @@ immutable Shape vertices::AVec end -# const _square = Shape(@compat(Tuple{Float64,Float64})[ -# ( 1.0, -1.0), -# ( 1.0, 1.0), -# (-1.0, 1.0), -# (-1.0, -1.0) -# ]) - -# const _diamond = Shape(@compat(Tuple{Float64,Float64})[ -# ( 0.0, -1.0), -# ( 1.0, 0.0), -# ( 0.0, 1.0), -# (-1.0, 0.0) -# ]) - -# const _cross = Shape(@compat(Tuple{Float64,Float64})[ -# (-1.0, -0.4), (-1.0, 0.4), # L edge -# (-0.4, 0.4), # BL inside -# (-0.4, 1.0), ( 0.4, 1.0), # B edge -# ( 0.4, 0.4), # BR inside -# ( 1.0, 0.4), ( 1.0, -0.4), # R edge -# ( 0.4, -0.4), # TR inside -# ( 0.4, -1.0), (-0.4, -1.0), # T edge -# (-0.4, -0.4) # TL inside -# ]) - -# const _xcross = Shape(@compat(Tuple{Float64,Float64})[ -# (x, y - u), (x + u, y - 2u), (x + 2u, y - u), -# (x + u, y), (x + 2u, y + u), (x + u, y + 2u), -# (x, y + u), (x - u, y + 2u), (x - 2u, y + u), -# (x - u, y), (x - 2u, y - u), (x - u, y - 2u) -# ] - - -# function xcross(xs::AbstractArray, ys::AbstractArray, rs::AbstractArray) -# n = max(length(xs), length(ys), length(rs)) -# polys = Array(Vector{@compat(Tuple{Compose.Measure, Compose.Measure})}, n) -# s = 1/sqrt(5) -# for i in 1:n -# x = Compose.x_measure(xs[mod1(i, length(xs))]) -# y = Compose.y_measure(ys[mod1(i, length(ys))]) -# r = rs[mod1(i, length(rs))] -# u = s*r -# polys[i] = [ -# (x, y - u), (x + u, y - 2u), (x + 2u, y - u), -# (x + u, y), (x + 2u, y + u), (x + u, y + 2u), -# (x, y + u), (x - u, y + 2u), (x - 2u, y + u), -# (x - u, y), (x - 2u, y - u), (x - u, y - 2u) -# ] -# end - -# return Gadfly.polygon(polys) -# end - - -# const _utriangle = Shape(@compat(Tuple{Float64,Float64})[ -# (x - r, y + u), -# (x + r, y + u), -# (x, y - u) -# ] - -# function utriangle(xs::AbstractArray, ys::AbstractArray, rs::AbstractArray, scalar = 1) -# n = max(length(xs), length(ys), length(rs)) -# polys = Array(Vector{@compat(Tuple{Compose.Measure, Compose.Measure})}, n) -# s = 0.8 -# for i in 1:n -# x = Compose.x_measure(xs[mod1(i, length(xs))]) -# y = Compose.y_measure(ys[mod1(i, length(ys))]) -# r = rs[mod1(i, length(rs))] -# u = 0.8 * scalar * r -# polys[i] = [ -# (x - r, y + u), -# (x + r, y + u), -# (x, y - u) -# ] -# end - -# return Gadfly.polygon(polys) -# end - "get an array of tuples of points on a circle with radius `r`" function partialcircle(start_θ, end_θ, n = 20, r=1) @compat(Tuple{Float64,Float64})[(r*cos(u),r*sin(u)) for u in linspace(start_θ, end_θ, n)] @@ -198,62 +119,57 @@ for n in [4,5,6,7,8] _shapes[symbol("star$n")] = makestar(n) end +# ----------------------------------------------------------------------- -# :ellipse, :rect, :diamond, :utriangle, :dtriangle, -# :cross, :xcross, :star1, :star2, :hexagon, :octagon +"Wrap a string with font info" +immutable PlotText + str::@compat(AbstractString) + family::Symbol + pointsize::Int + halign::Symbol + valign::Symbol + rotation::Float64 + color::Colorant +end +function text(str, args...) + + # defaults + family = :courier + pointsize = 12 + halign = :hcenter + valign = :vcenter + rotation = 0.0 + color = colorant"black" + for arg in args + if arg == :center + halign = :hcenter + valign = :vcenter + elseif arg in (:hcenter, :left, :right) + halign = arg + elseif arg in (:vcenter, :top, :bottom) + valign = arg + elseif typeof(arg) <: Colorant + color = arg + elseif isa(arg, Symbol) + try + color = parse(Colorant, string(arg)) + catch + family = arg + end + elseif typeof(arg) <: Integer + pointsize = arg + elseif typeof(arg) <: Real + rotation = convert(Float64, arg) + else + warn("Unused font arg: $arg ($(typeof(arg)))") + end + end -# const _xcross = Shape(@compat(Tuple{Float64,Float64})[ -# ] - -# # function hexagon(xs::AbstractArray, ys::AbstractArray, rs::AbstractArray) -# # n = max(length(xs), length(ys), length(rs)) - -# # polys = Array(Vector{@compat(Tuple{Compose.Measure, Compose.Measure})}, n) -# # for i in 1:n -# # x = Compose.x_measure(xs[mod1(i, length(xs))]) -# # y = Compose.y_measure(ys[mod1(i, length(ys))]) -# # r = rs[mod1(i, length(rs))] -# # u = 0.6r - -# # polys[i] = [ -# # (x-r, y-u), (x-r, y+u), # L edge -# # (x, y+r), # B -# # (x+r, y+u), (x+r, y-u), # R edge -# # (x, y-r) # T -# # ] -# # end - -# # return Gadfly.polygon(polys) -# # end - - - -# const _xcross = Shape(@compat(Tuple{Float64,Float64})[ -# ] - -# function octagon(xs::AbstractArray, ys::AbstractArray, rs::AbstractArray) -# n = max(length(xs), length(ys), length(rs)) - -# polys = Array(Vector{@compat(Tuple{Compose.Measure, Compose.Measure})}, n) -# for i in 1:n -# x = Compose.x_measure(xs[mod1(i, length(xs))]) -# y = Compose.y_measure(ys[mod1(i, length(ys))]) -# r = rs[mod1(i, length(rs))] -# u = 0.4r - -# polys[i] = [ -# (x-r, y-u), (x-r, y+u), # L edge -# (x-u, y+r), (x+u, y+r), # B edge -# (x+r, y+u), (x+r, y-u), # R edge -# (x+u, y-r), (x-u, y-r), # T edge -# ] -# end - -# return Gadfly.polygon(polys) -# end + PlotText(string(str), family, pointsize, halign, valign, rotation, color) +end # ----------------------------------------------------------------------- diff --git a/src/utils.jl b/src/utils.jl index b442c3d5..0b70c269 100644 --- a/src/utils.jl +++ b/src/utils.jl @@ -200,6 +200,11 @@ function dumpdict(d::Dict, prefix = "") println() end + +function dumpcallstack() + error() # well... you wanted the stacktrace, didn't you?!? +end + # ---------------------------------------------------------------