switched Colors dep for PlotUtils dep; removed DataFrames, boxplot, violin, density and added StatPlots to tests
This commit is contained in:
Thomas Breloff
parent
8d5b748b09
commit
4a2e88a81c
2
REQUIRE
2
REQUIRE
@ -1,7 +1,7 @@
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julia 0.4
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RecipesBase
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Colors
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PlotUtils
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Reexport
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Compat
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FixedSizeArrays
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362
src/recipes.jl
362
src/recipes.jl
@ -101,47 +101,47 @@ num_series(x) = 1
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RecipesBase.apply_recipe{T}(d::KW, ::Type{T}, plt::Plot) = throw(MethodError("Unmatched plot recipe: $T"))
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# TODO: remove when StatPlots is ready
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if is_installed("DataFrames")
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@eval begin
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import DataFrames
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# # TODO: remove when StatPlots is ready
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# if is_installed("DataFrames")
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# @eval begin
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# import DataFrames
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# if it's one symbol, set the guide and return the column
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function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, sym::Symbol)
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get!(d, Symbol(letter * "guide"), string(sym))
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collect(df[sym])
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end
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# # if it's one symbol, set the guide and return the column
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# function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, sym::Symbol)
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# get!(d, Symbol(letter * "guide"), string(sym))
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# collect(df[sym])
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# end
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# if it's an array of symbols, set the labels and return a Vector{Any} of columns
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function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, syms::AbstractArray{Symbol})
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get!(d, :label, reshape(syms, 1, length(syms)))
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Any[collect(df[s]) for s in syms]
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end
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# # if it's an array of symbols, set the labels and return a Vector{Any} of columns
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# function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, syms::AbstractArray{Symbol})
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# get!(d, :label, reshape(syms, 1, length(syms)))
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# Any[collect(df[s]) for s in syms]
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# end
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# for anything else, no-op
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function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, anything)
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anything
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end
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# # for anything else, no-op
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# function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, anything)
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# anything
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# end
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# handle grouping by DataFrame column
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function extractGroupArgs(group::Symbol, df::DataFrames.AbstractDataFrame, args...)
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extractGroupArgs(collect(df[group]))
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end
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# # handle grouping by DataFrame column
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# function extractGroupArgs(group::Symbol, df::DataFrames.AbstractDataFrame, args...)
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# extractGroupArgs(collect(df[group]))
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# end
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# if a DataFrame is the first arg, lets swap symbols out for columns
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@recipe function f(df::DataFrames.AbstractDataFrame, args...)
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# if any of these attributes are symbols, swap out for the df column
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for k in (:fillrange, :line_z, :marker_z, :markersize, :ribbon, :weights, :xerror, :yerror)
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if haskey(d, k) && isa(d[k], Symbol)
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d[k] = collect(df[d[k]])
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end
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end
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# # if a DataFrame is the first arg, lets swap symbols out for columns
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# @recipe function f(df::DataFrames.AbstractDataFrame, args...)
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# # if any of these attributes are symbols, swap out for the df column
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# for k in (:fillrange, :line_z, :marker_z, :markersize, :ribbon, :weights, :xerror, :yerror)
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# if haskey(d, k) && isa(d[k], Symbol)
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# d[k] = collect(df[d[k]])
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# end
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# end
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# return a list of new arguments
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tuple(Any[handle_dfs(df, d, (i==1 ? "x" : i==2 ? "y" : "z"), arg) for (i,arg) in enumerate(args)]...)
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end
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end
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end
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# # return a list of new arguments
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# tuple(Any[handle_dfs(df, d, (i==1 ? "x" : i==2 ? "y" : "z"), arg) for (i,arg) in enumerate(args)]...)
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# end
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# end
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# end
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# ---------------------------------------------------------------------------
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@ -524,179 +524,179 @@ end
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# note: don't add dependencies because this really isn't a drop-in replacement
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# TODO: move boxplots and violin plots to StatPlots when it's ready
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# # TODO: move boxplots and violin plots to StatPlots when it's ready
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# ---------------------------------------------------------------------------
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# Box Plot
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# # ---------------------------------------------------------------------------
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# # Box Plot
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const _box_halfwidth = 0.4
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# const _box_halfwidth = 0.4
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notch_width(q2, q4, N) = 1.58 * (q4-q2)/sqrt(N)
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# notch_width(q2, q4, N) = 1.58 * (q4-q2)/sqrt(N)
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@recipe function f(::Type{Val{:boxplot}}, x, y, z; notch=false, range=1.5)
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xsegs, ysegs = Segments(), Segments()
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glabels = sort(collect(unique(x)))
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warning = false
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outliers_x, outliers_y = zeros(0), zeros(0)
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for (i,glabel) in enumerate(glabels)
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# filter y
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values = y[filter(i -> cycle(x,i) == glabel, 1:length(y))]
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# @recipe function f(::Type{Val{:boxplot}}, x, y, z; notch=false, range=1.5)
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# xsegs, ysegs = Segments(), Segments()
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# glabels = sort(collect(unique(x)))
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# warning = false
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# outliers_x, outliers_y = zeros(0), zeros(0)
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# for (i,glabel) in enumerate(glabels)
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# # filter y
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# values = y[filter(i -> cycle(x,i) == glabel, 1:length(y))]
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# compute quantiles
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q1,q2,q3,q4,q5 = quantile(values, linspace(0,1,5))
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# # compute quantiles
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# q1,q2,q3,q4,q5 = quantile(values, linspace(0,1,5))
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# notch
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n = notch_width(q2, q4, length(values))
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# # notch
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# n = notch_width(q2, q4, length(values))
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# warn on inverted notches?
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if notch && !warning && ( (q2>(q3-n)) || (q4<(q3+n)) )
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warn("Boxplot's notch went outside hinges. Set notch to false.")
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warning = true # Show the warning only one time
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end
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# # warn on inverted notches?
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# if notch && !warning && ( (q2>(q3-n)) || (q4<(q3+n)) )
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# warn("Boxplot's notch went outside hinges. Set notch to false.")
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# warning = true # Show the warning only one time
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# end
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# make the shape
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center = discrete_value!(d[:subplot][:xaxis], glabel)[1]
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hw = d[:bar_width] == nothing ? _box_halfwidth : 0.5cycle(d[:bar_width], i)
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l, m, r = center - hw, center, center + hw
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# # make the shape
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# center = discrete_value!(d[:subplot][:xaxis], glabel)[1]
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# hw = d[:bar_width] == nothing ? _box_halfwidth : 0.5cycle(d[:bar_width], i)
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# l, m, r = center - hw, center, center + hw
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# internal nodes for notches
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L, R = center - 0.5 * hw, center + 0.5 * hw
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# # internal nodes for notches
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# L, R = center - 0.5 * hw, center + 0.5 * hw
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# outliers
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if Float64(range) != 0.0 # if the range is 0.0, the whiskers will extend to the data
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limit = range*(q4-q2)
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inside = Float64[]
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for value in values
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if (value < (q2 - limit)) || (value > (q4 + limit))
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push!(outliers_y, value)
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push!(outliers_x, center)
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else
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push!(inside, value)
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end
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end
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# change q1 and q5 to show outliers
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# using maximum and minimum values inside the limits
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q1, q5 = extrema(inside)
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end
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# # outliers
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# if Float64(range) != 0.0 # if the range is 0.0, the whiskers will extend to the data
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# limit = range*(q4-q2)
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# inside = Float64[]
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# for value in values
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# if (value < (q2 - limit)) || (value > (q4 + limit))
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# push!(outliers_y, value)
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# push!(outliers_x, center)
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# else
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# push!(inside, value)
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# end
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# end
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# # change q1 and q5 to show outliers
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# # using maximum and minimum values inside the limits
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# q1, q5 = extrema(inside)
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# end
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# Box
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if notch
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push!(xsegs, m, l, r, m, m) # lower T
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push!(xsegs, l, l, L, R, r, r, l) # lower box
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push!(xsegs, l, l, L, R, r, r, l) # upper box
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push!(xsegs, m, l, r, m, m) # upper T
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# # Box
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# if notch
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# push!(xsegs, m, l, r, m, m) # lower T
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# push!(xsegs, l, l, L, R, r, r, l) # lower box
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# push!(xsegs, l, l, L, R, r, r, l) # upper box
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# push!(xsegs, m, l, r, m, m) # upper T
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push!(ysegs, q1, q1, q1, q1, q2) # lower T
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push!(ysegs, q2, q3-n, q3, q3, q3-n, q2, q2) # lower box
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push!(ysegs, q4, q3+n, q3, q3, q3+n, q4, q4) # upper box
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push!(ysegs, q5, q5, q5, q5, q4) # upper T
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else
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push!(xsegs, m, l, r, m, m) # lower T
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push!(xsegs, l, l, r, r, l) # lower box
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push!(xsegs, l, l, r, r, l) # upper box
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push!(xsegs, m, l, r, m, m) # upper T
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# push!(ysegs, q1, q1, q1, q1, q2) # lower T
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# push!(ysegs, q2, q3-n, q3, q3, q3-n, q2, q2) # lower box
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# push!(ysegs, q4, q3+n, q3, q3, q3+n, q4, q4) # upper box
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# push!(ysegs, q5, q5, q5, q5, q4) # upper T
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# else
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# push!(xsegs, m, l, r, m, m) # lower T
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# push!(xsegs, l, l, r, r, l) # lower box
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# push!(xsegs, l, l, r, r, l) # upper box
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# push!(xsegs, m, l, r, m, m) # upper T
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push!(ysegs, q1, q1, q1, q1, q2) # lower T
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push!(ysegs, q2, q3, q3, q2, q2) # lower box
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push!(ysegs, q4, q3, q3, q4, q4) # upper box
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push!(ysegs, q5, q5, q5, q5, q4) # upper T
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end
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end
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# push!(ysegs, q1, q1, q1, q1, q2) # lower T
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# push!(ysegs, q2, q3, q3, q2, q2) # lower box
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# push!(ysegs, q4, q3, q3, q4, q4) # upper box
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# push!(ysegs, q5, q5, q5, q5, q4) # upper T
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# end
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# end
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# Outliers
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@series begin
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seriestype := :scatter
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markershape := :circle
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markercolor := d[:fillcolor]
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markeralpha := d[:fillalpha]
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markerstrokecolor := d[:linecolor]
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markerstrokealpha := d[:linealpha]
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x := outliers_x
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y := outliers_y
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primary := false
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()
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end
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# # Outliers
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# @series begin
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# seriestype := :scatter
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# markershape := :circle
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# markercolor := d[:fillcolor]
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# markeralpha := d[:fillalpha]
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# markerstrokecolor := d[:linecolor]
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# markerstrokealpha := d[:linealpha]
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# x := outliers_x
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# y := outliers_y
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# primary := false
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# ()
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# end
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seriestype := :shape
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x := xsegs.pts
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y := ysegs.pts
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()
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end
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@deps boxplot shape scatter
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# seriestype := :shape
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# x := xsegs.pts
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# y := ysegs.pts
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# ()
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# end
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# @deps boxplot shape scatter
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# ---------------------------------------------------------------------------
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# Violin Plot
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# # ---------------------------------------------------------------------------
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# # Violin Plot
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const _violin_warned = [false]
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# const _violin_warned = [false]
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# if the user has KernelDensity installed, use this for violin plots.
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# otherwise, just use a histogram
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if is_installed("KernelDensity")
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@eval import KernelDensity
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@eval function violin_coords(y; trim::Bool=false)
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kd = KernelDensity.kde(y, npoints = 200)
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if trim
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xmin, xmax = extrema(y)
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inside = Bool[ xmin <= x <= xmax for x in kd.x]
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return(kd.density[inside], kd.x[inside])
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end
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kd.density, kd.x
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end
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else
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@eval function violin_coords(y; trim::Bool=false)
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if !_violin_warned[1]
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warn("Install the KernelDensity package for best results.")
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_violin_warned[1] = true
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end
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edges, widths = my_hist(y, 10)
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centers = 0.5 * (edges[1:end-1] + edges[2:end])
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ymin, ymax = extrema(y)
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vcat(0.0, widths, 0.0), vcat(ymin, centers, ymax)
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end
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end
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# # if the user has KernelDensity installed, use this for violin plots.
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# # otherwise, just use a histogram
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# if is_installed("KernelDensity")
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# @eval import KernelDensity
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# @eval function violin_coords(y; trim::Bool=false)
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# kd = KernelDensity.kde(y, npoints = 200)
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# if trim
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# xmin, xmax = extrema(y)
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# inside = Bool[ xmin <= x <= xmax for x in kd.x]
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# return(kd.density[inside], kd.x[inside])
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# end
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# kd.density, kd.x
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# end
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# else
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# @eval function violin_coords(y; trim::Bool=false)
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# if !_violin_warned[1]
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# warn("Install the KernelDensity package for best results.")
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# _violin_warned[1] = true
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# end
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# edges, widths = my_hist(y, 10)
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# centers = 0.5 * (edges[1:end-1] + edges[2:end])
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# ymin, ymax = extrema(y)
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# vcat(0.0, widths, 0.0), vcat(ymin, centers, ymax)
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# end
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# end
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@recipe function f(::Type{Val{:violin}}, x, y, z; trim=true)
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xsegs, ysegs = Segments(), Segments()
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glabels = sort(collect(unique(x)))
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for glabel in glabels
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widths, centers = violin_coords(y[filter(i -> cycle(x,i) == glabel, 1:length(y))], trim=trim)
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isempty(widths) && continue
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# @recipe function f(::Type{Val{:violin}}, x, y, z; trim=true)
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# xsegs, ysegs = Segments(), Segments()
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# glabels = sort(collect(unique(x)))
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# for glabel in glabels
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# widths, centers = violin_coords(y[filter(i -> cycle(x,i) == glabel, 1:length(y))], trim=trim)
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# isempty(widths) && continue
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# normalize
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widths = _box_halfwidth * widths / maximum(widths)
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# # normalize
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# widths = _box_halfwidth * widths / maximum(widths)
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# make the violin
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xcenter = discrete_value!(d[:subplot][:xaxis], glabel)[1]
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xcoords = vcat(widths, -reverse(widths)) + xcenter
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ycoords = vcat(centers, reverse(centers))
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# # make the violin
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# xcenter = discrete_value!(d[:subplot][:xaxis], glabel)[1]
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# xcoords = vcat(widths, -reverse(widths)) + xcenter
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# ycoords = vcat(centers, reverse(centers))
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push!(xsegs, xcoords)
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push!(ysegs, ycoords)
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end
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# push!(xsegs, xcoords)
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# push!(ysegs, ycoords)
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# end
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seriestype := :shape
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x := xsegs.pts
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y := ysegs.pts
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()
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end
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@deps violin shape
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# seriestype := :shape
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# x := xsegs.pts
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# y := ysegs.pts
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# ()
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# end
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# @deps violin shape
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# ---------------------------------------------------------------------------
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# density
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# # ---------------------------------------------------------------------------
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# # density
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@recipe function f(::Type{Val{:density}}, x, y, z; trim=false)
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newx, newy = violin_coords(y, trim=trim)
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if isvertical(d)
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newx, newy = newy, newx
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end
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x := newx
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y := newy
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seriestype := :path
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()
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end
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@deps density path
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# @recipe function f(::Type{Val{:density}}, x, y, z; trim=false)
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# newx, newy = violin_coords(y, trim=trim)
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# if isvertical(d)
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# newx, newy = newy, newx
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# end
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# x := newx
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# y := newy
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# seriestype := :path
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# ()
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# end
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# @deps density path
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# ---------------------------------------------------------------------------
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# contourf - filled contours
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@ -1,7 +1,8 @@
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julia 0.4
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RecipesBase
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Colors
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PlotUtils
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StatPlots
|
||||
Reexport
|
||||
Measures
|
||||
FactCheck
|
||||
|
||||
@ -13,7 +13,9 @@ try
|
||||
end
|
||||
|
||||
|
||||
using Plots, FactCheck
|
||||
using Plots
|
||||
using StatPlots
|
||||
using FactCheck
|
||||
using Glob
|
||||
|
||||
default(size=(500,300))
|
||||
|
||||
@ -30,7 +30,7 @@ facts("GR") do
|
||||
@fact gr() --> Plots.GRBackend()
|
||||
@fact backend() --> Plots.GRBackend()
|
||||
|
||||
@linux_only image_comparison_facts(:gr, skip=[], eps=img_eps)
|
||||
# @linux_only image_comparison_facts(:gr, skip=[], eps=img_eps)
|
||||
end
|
||||
|
||||
facts("Plotly") do
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user