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pyplot fix; new flexible logic for DataFrames

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This commit is contained in:
Thomas Breloff 2016-06-29 16:22:16 -04:00
parent 27a68333b1
commit bf94c48225
2 changed files with 25 additions and 51 deletions
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2
src/backends/pyplot.jl
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@ -807,7 +807,7 @@ function py_add_series(plt::Plot{PyPlotBackend}, series::Series)
# TODO: this should probably be handled generically
# expand extrema... handle is a QuadMesh object
for path in handle[:properties]()[:paths]
for path in handle[:properties]()["paths"]
verts = path[:vertices]
xmin, ymin = minimum(verts, 1)
xmax, ymax = maximum(verts, 1)

74
src/recipes.jl
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@ -98,14 +98,6 @@ end
# ----------------------------------------------------------------------------------
# abstract PlotRecipe
# getRecipeXY(recipe::PlotRecipe) = Float64[], Float64[]
# getRecipeArgs(recipe::PlotRecipe) = ()
# plot(recipe::PlotRecipe, args...; kw...) = plot(getRecipeXY(recipe)..., args...; getRecipeArgs(recipe)..., kw...)
# plot!(recipe::PlotRecipe, args...; kw...) = plot!(getRecipeXY(recipe)..., args...; getRecipeArgs(recipe)..., kw...)
# plot!(plt::Plot, recipe::PlotRecipe, args...; kw...) = plot!(getRecipeXY(recipe)..., args...; getRecipeArgs(recipe)..., kw...)
num_series(x::AMat) = size(x,2)
num_series(x) = 1
@ -113,63 +105,45 @@ num_series(x) = 1
RecipesBase.apply_recipe{T}(d::KW, ::Type{T}, plt::Plot) = throw(MethodError("Unmatched plot recipe: $T"))
# # if it's not a recipe, just do nothing and return the args
# function RecipesBase.apply_recipe(d::KW, args...; issubplot=false)
# if issubplot && !isempty(args) && !haskey(d, :n) && !haskey(d, :layout)
# # put in a sensible default
# d[:n] = maximum(map(num_series, args))
# end
# args
# end
if is_installed("DataFrames")
@eval begin
import DataFrames
DFS = Union{Symbol, AbstractArray{Symbol}}
function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, dfs::DFS)
if isa(dfs, Symbol)
get!(d, Symbol(letter * "guide"), string(dfs))
collect(df[dfs])
else
get!(d, :label, reshape(dfs, 1, length(dfs)))
Any[collect(df[s]) for s in dfs]
end
# if it's one symbol, set the guide and return the column
function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, sym::Symbol)
get!(d, Symbol(letter * "guide"), string(sym))
collect(df[sym])
end
# if it's an array of symbols, set the labels and return a Vector{Any} of columns
function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, syms::AbstractArray{Symbol})
get!(d, :label, reshape(syms, 1, length(syms)))
Any[collect(df[s]) for s in syms]
end
# for anything else, no-op
function handle_dfs(df::DataFrames.AbstractDataFrame, d::KW, letter, anything)
anything
end
# handle grouping by DataFrame column
function extractGroupArgs(group::Symbol, df::DataFrames.AbstractDataFrame, args...)
extractGroupArgs(collect(df[group]))
end
function handle_group(df::DataFrames.AbstractDataFrame, d::KW)
if haskey(d, :group)
g = d[:group]
if isa(g, Symbol)
d[:group] = collect(df[g])
# if a DataFrame is the first arg, lets swap symbols out for columns
@recipe function f(df::DataFrames.AbstractDataFrame, args...)
# if any of these attributes are symbols, swap out for the df column
for k in (:fillrange, :line_z, :marker_z, :markersize, :ribbon, :weights, :xerror, :yerror)
if haskey(d, k) && isa(d[k], Symbol)
d[k] = collect(df[d[k]])
end
end
end
@recipe function f(df::DataFrames.AbstractDataFrame, sy::DFS)
handle_group(df, d)
handle_dfs(df, d, "y", sy)
end
@recipe function f(df::DataFrames.AbstractDataFrame, sx::DFS, sy::DFS)
handle_group(df, d)
x = handle_dfs(df, d, "x", sx)
y = handle_dfs(df, d, "y", sy)
x, y
end
@recipe function f(df::DataFrames.AbstractDataFrame, sx::DFS, sy::DFS, sz::DFS)
handle_group(df, d)
x = handle_dfs(df, d, "x", sx)
y = handle_dfs(df, d, "y", sy)
z = handle_dfs(df, d, "z", sz)
x, y, z
# return a list of new arguments
tuple(Any[handle_dfs(df, d, (i==1 ? "x" : i==2 ? "y" : "z"), arg) for (i,arg) in enumerate(args)]...)
end
end
end