improve heatmap_edges performance

src/axes.jl

@@ -126,7 +126,7 @@ function optimal_ticks_and_labels(ticks, alims, scale, formatter)
     amin, amax = alims
 
     # scale the limits
-    sf = RecipesPipeline.scale_func(scale)
+    sf, invsf, noop = scale_inverse_scale_func(scale)
 
     # If the axis input was a Date or DateTime use a special logic to find
     # "round" Date(Time)s as ticks
@@ -135,7 +135,8 @@ function optimal_ticks_and_labels(ticks, alims, scale, formatter)
     # or DateTime) is chosen based on the time span between amin and amax
     # rather than on the input format
     # TODO: maybe: non-trivial scale (:ln, :log2, :log10) for date/datetime
-    if ticks === nothing && scale === :identity
+
+    if ticks === nothing && noop
         if formatter == RecipesPipeline.dateformatter
             # optimize_datetime_ticks returns ticks and labels(!) based on
             # integers/floats corresponding to the DateTime type. Thus, the axes
@@ -159,7 +160,7 @@ function optimal_ticks_and_labels(ticks, alims, scale, formatter)
             sf(amax);
             k_min = scale ∈ _logScales ? 2 : 4, # minimum number of ticks
             k_max = 8, # maximum number of ticks
-            scale = scale,
+            scale,
         ) |> first
     elseif typeof(ticks) <: Int
         optimize_ticks(
@@ -171,12 +172,12 @@ function optimal_ticks_and_labels(ticks, alims, scale, formatter)
             # `strict_span = false` rewards cases where the span of the
             # chosen  ticks is not too much bigger than amin - amax:
             strict_span = false,
-            scale = scale,
+            scale,
         ) |> first
     else
         map(sf, filter(t -> amin ≤ t ≤ amax, ticks))
     end
-    unscaled_ticks = map(RecipesPipeline.inverse_scale_func(scale), scaled_ticks)
+    unscaled_ticks = noop ? scaled_ticks : map(invsf, scaled_ticks)
 
     labels::Vector{String} = if any(isfinite, unscaled_ticks)
         if formatter in (:auto, :plain, :scientific, :engineering)
@@ -520,9 +521,14 @@ function scale_lims(from, to, factor)
     mid .+ (-span, span) .* factor
 end
 
-function scale_lims(from, to, factor, scale)
-    f, invf = RecipesPipeline.scale_func(scale), RecipesPipeline.inverse_scale_func(scale)
+_scale_lims(::Val{true}, ::Function, ::Function, from, to, factor) =
+    scale_lims(from, to, factor)
+_scale_lims(::Val{false}, f::Function, invf::Function, from, to, factor) =
     invf.(scale_lims(f(from), f(to), factor))
+
+function scale_lims(from, to, factor, scale)
+    f, invf, noop = scale_inverse_scale_func(scale)
+    _scale_lims(Val(noop), f, invf, from, to, factor)
 end
 
 """
@@ -534,9 +540,8 @@ If `letter` is omitted, all axes are affected.
 """
 function scale_lims!(sp::Subplot, letter, factor)
     axis = Plots.get_axis(sp, letter)
-    scale = axis[:scale]
     from, to = Plots.get_sp_lims(sp, letter)
-    axis[:lims] = scale_lims(from, to, factor, scale)
+    axis[:lims] = scale_lims(from, to, factor, axis[:scale])
 end
 function scale_lims!(plt::Plot, letter, factor)
     foreach(sp -> scale_lims!(sp, letter, factor), plt.subplots)
@@ -770,8 +775,7 @@ function add_major_or_minor_segments_2d(
 )
     ticks === nothing && return
     if cond
-        f = RecipesPipeline.scale_func(oax[:scale])
-        invf = RecipesPipeline.inverse_scale_func(oax[:scale])
+        f, invf = scale_inverse_scale_func(oax[:scale])
         tick_start, tick_stop = if sp[:framestyle] === :origin
             oamin, oamax = oamM
             t = invf(f(0) + factor * (f(oamax) - f(oamin)))
@@ -904,8 +908,7 @@ function add_major_or_minor_segments_3d(
 )
     ticks === nothing && return
     if cond
-        f = RecipesPipeline.scale_func(nax[:scale])
-        invf = RecipesPipeline.inverse_scale_func(nax[:scale])
+        f, invf = scale_inverse_scale_func(nax[:scale])
         tick_start, tick_stop = if sp[:framestyle] === :origin
             namin, namax = namM
             t = invf(f(0) + factor * (f(namax) - f(namin)))

src/backends/plotly.jl

@@ -478,7 +478,10 @@ plotly_data(v::AbstractArray{R}) where {R<:Rational} = float(v)
 plotly_native_data(axis::Axis, a::Surface) = Surface(plotly_native_data(axis, a.surf))
 plotly_native_data(axis::Axis, data::AbstractArray) =
     if !isempty(axis[:discrete_values])
-        construct_categorical_data(data, axis)
+        map(
+            xi -> axis[:discrete_values][searchsortedfirst(axis[:continuous_values], xi)],
+            data,
+        )
     elseif axis[:formatter] in (datetimeformatter, dateformatter, timeformatter)
         plotly_convert_to_datetime(data, axis[:formatter])
     else

src/utils.jl

@@ -219,47 +219,64 @@ replaceAlias!(plotattributes::AKW, k::Symbol, aliases::Dict{Symbol,Symbol}) =
 replaceAliases!(plotattributes::AKW, aliases::Dict{Symbol,Symbol}) =
     foreach(k -> replaceAlias!(plotattributes, k, aliases), collect(keys(plotattributes)))
 
-function _heatmap_edges(v::AVec, isedges::Bool = false, ispolar::Bool = false)
-    length(v) == 1 && return v[1] .+ [ispolar ? max(-v[1], -0.5) : -0.5, 0.5]
+scale_inverse_scale_func(scale::Symbol) = (
+    RecipesPipeline.scale_func(scale),
+    RecipesPipeline.inverse_scale_func(scale),
+    scale === :identity,
+)
+
+function __heatmap_edges(v::AVec, isedges::Bool, ispolar::Bool)
+    (n = length(v)) == 1 && return v[1] .+ [ispolar ? max(-v[1], -0.5) : -0.5, 0.5]
     isedges && return v
     # `isedges = true` means that v is a vector which already describes edges
     # and does not need to be extended.
     vmin, vmax = ignorenan_extrema(v)
     extra_min = ispolar ? min(v[1], 0.5(v[2] - v[1])) : 0.5(v[2] - v[1])
-    extra_max = 0.5(v[end] - v[end - 1])
-    vcat(vmin - extra_min, 0.5(v[1:(end - 1)] + v[2:end]), vmax + extra_max)
+    extra_max = 0.5(v[n] - v[n - 1])
+    vcat(vmin - extra_min, 0.5(v[1:(n - 1)] + v[2:n]), vmax + extra_max)
+end
+
+_heatmap_edges(::Val{true}, v::AVec, ::Symbol, isedges::Bool, ispolar::Bool) =
+    __heatmap_edges(v, isedges, ispolar)
+
+function _heatmap_edges(::Val{false}, v::AVec, scale::Symbol, isedges::Bool, ispolar::Bool)
+    f, invf = scale_inverse_scale_func(scale)
+    invf.(__heatmap_edges(f.(v), isedges, ispolar))
 end
 
 "create an (n+1) list of the outsides of heatmap rectangles"
-function heatmap_edges(
+heatmap_edges(
     v::AVec,
     scale::Symbol = :identity,
     isedges::Bool = false,
     ispolar::Bool = false,
-)
-    f, invf = RecipesPipeline.scale_func(scale), RecipesPipeline.inverse_scale_func(scale)
-    map(invf, _heatmap_edges(map(f, v), isedges, ispolar))
-end
+) = _heatmap_edges(Val(scale === :identity), v, scale, isedges, ispolar)
 
 function heatmap_edges(
     x::AVec,
     xscale::Symbol,
     y::AVec,
     yscale::Symbol,
-    z_size::Tuple{Int,Int},
+    z_size::NTuple{2,Int},
     ispolar::Bool = false,
 )
     nx, ny = length(x), length(y)
     # ismidpoints = z_size == (ny, nx) # This fails some tests, but would actually be
     # the correct check, since (4, 3) != (3, 4) and a missleading plot is produced.
     ismidpoints = prod(z_size) == (ny * nx)
     isedges = z_size == (ny - 1, nx - 1)
-    if !ismidpoints && !isedges
-        error("""Length of x & y does not match the size of z.
-              Must be either `size(z) == (length(y), length(x))` (x & y define midpoints)
-              or `size(z) == (length(y)+1, length(x)+1))` (x & y define edges).""")
-    end
-    heatmap_edges(x, xscale, isedges), heatmap_edges(y, yscale, isedges, ispolar) # special handle for `r` in polar plots
+    (ismidpoints || isedges) ||
+        """
+        Length of x & y does not match the size of z.
+        Must be either `size(z) == (length(y), length(x))` (x & y define midpoints)
+        or `size(z) == (length(y)+1, length(x)+1))` (x & y define edges).
+        """ |>
+        ArgumentError |>
+        throw
+    (
+        _heatmap_edges(Val(xscale === :identity), x, xscale, isedges, false),
+        _heatmap_edges(Val(yscale === :identity), y, yscale, isedges, ispolar),  # special handle for `r` in polar plots
+    )
 end
 
 is_uniformly_spaced(v; tol = 1e-6) =
@@ -934,45 +951,7 @@ function convert_sci_unicode(label::AbstractString)
     label
 end
 
-function straightline_data(series, expansion_factor = 1)
-    sp = series[:subplot]
-    xl, yl = isvertical(series) ? (xlims(sp), ylims(sp)) : (ylims(sp), xlims(sp))
-
-    # handle axes scales
-    xscale = sp[:xaxis][:scale]
-    xf = RecipesPipeline.scale_func(xscale)
-    xinvf = RecipesPipeline.inverse_scale_func(xscale)
-    yscale = sp[:yaxis][:scale]
-    yf = RecipesPipeline.scale_func(yscale)
-    yinvf = RecipesPipeline.inverse_scale_func(yscale)
-
-    xl, yl = xf.(xl), yf.(yl)
-    x, y = xf.(series[:x]), yf.(series[:y])
-    n = length(x)
-
-    xdata, ydata = if n == 2
-        straightline_data(xl, yl, x, y, expansion_factor)
-    else
-        k, r = divrem(n, 3)
-        if r == 0
-            xdata, ydata = fill(NaN, n), fill(NaN, n)
-            for i in 1:k
-                inds = (3 * i - 2):(3 * i - 1)
-                xdata[inds], ydata[inds] =
-                    straightline_data(xl, yl, x[inds], y[inds], expansion_factor)
-            end
-            xdata, ydata
-        else
-            error(
-                "Misformed data. `straightline_data` either accepts vectors of length 2 or 3k. The provided series has length $n",
-            )
-        end
-    end
-
-    xinvf.(xdata), yinvf.(ydata)
-end
-
-function straightline_data(xl, yl, x, y, expansion_factor = 1)
+function ___straightline_data(xl, yl, x, y, exp_fact)
     x_vals, y_vals = if y[1] == y[2]
         if x[1] == x[2]
             error("Two identical points cannot be used to describe a straight line.")
@@ -996,45 +975,105 @@ function straightline_data(xl, yl, x, y, expansion_factor = 1)
     end
     # expand the data outside the axis limits, by a certain factor too improve
     # plotly(js) and interactive behaviour
-    x_vals = x_vals .+ (x_vals[2] - x_vals[1]) .* expansion_factor .* [-1, 1]
-    y_vals = y_vals .+ (y_vals[2] - y_vals[1]) .* expansion_factor .* [-1, 1]
-    x_vals, y_vals
+    (
+        x_vals .+ (x_vals[2] - x_vals[1]) .* exp_fact,
+        y_vals .+ (y_vals[2] - y_vals[1]) .* exp_fact,
+    )
 end
 
-function shape_data(series, expansion_factor = 1)
+__straightline_data(xl, yl, x, y, exp_fact) =
+    if (n = length(x)) == 2
+        ___straightline_data(xl, yl, x, y, exp_fact)
+    else
+        k, r = divrem(n, 3)
+        @assert r == 0 "Misformed data. `straightline_data` either accepts vectors of length 2 or 3k. The provided series has length $n"
+        xdata, ydata = fill(NaN, n), fill(NaN, n)
+        for i in 1:k
+            inds = (3i - 2):(3i - 1)
+            xdata[inds], ydata[inds] =
+                ___straightline_data(xl, yl, x[inds], y[inds], exp_fact)
+        end
+        xdata, ydata
+    end
+
+_straightline_data(::Val{true}, ::Function, ::Function, ::Function, ::Function, args...) =
+    __straightline_data(args...)
+
+function _straightline_data(
+    ::Val{false},
+    xf::Function,
+    xinvf::Function,
+    yf::Function,
+    yinvf::Function,
+    xl,
+    yl,
+    x,
+    y,
+    exp_fact,
+)
+    xdata, ydata = __straightline_data(xf.(xl), yf.(yl), xf.(x), yf.(y), exp_fact)
+    xinvf.(xdata), yinvf.(ydata)
+end
+
+function straightline_data(series, expansion_factor = 1)
     sp = series[:subplot]
     xl, yl = isvertical(series) ? (xlims(sp), ylims(sp)) : (ylims(sp), xlims(sp))
 
     # handle axes scales
-    xscale = sp[:xaxis][:scale]
-    xf = RecipesPipeline.scale_func(xscale)
-    xinvf = RecipesPipeline.inverse_scale_func(xscale)
-    yscale = sp[:yaxis][:scale]
-    yf = RecipesPipeline.scale_func(yscale)
-    yinvf = RecipesPipeline.inverse_scale_func(yscale)
-
-    x, y = copy(series[:x]), copy(series[:y])
-    for i in eachindex(x)
+    xf, xinvf, xnoop = scale_inverse_scale_func(sp[:xaxis][:scale])
+    yf, yinvf, ynoop = scale_inverse_scale_func(sp[:yaxis][:scale])
+
+    _straightline_data(
+        Val(xnoop && ynoop),
+        xf,
+        xinvf,
+        yf,
+        yinvf,
+        xl,
+        yl,
+        series[:x],
+        series[:y],
+        [-expansion_factor, +expansion_factor],
+    )
+end
+
+function _shape_data!(::Val{false}, xf::Function, xinvf::Function, x, xl, exp_fact)
+    @inbounds for i in eachindex(x)
         if x[i] == -Inf
-            x[i] = xinvf(xf(xl[1]) - expansion_factor * (xf(xl[2]) - xf(xl[1])))
-        elseif x[i] == Inf
-            x[i] = xinvf(xf(xl[2]) + expansion_factor * (xf(xl[2]) - xf(xl[1])))
+            x[i] = xinvf(xf(xl[1]) - exp_fact * (xf(xl[2]) - xf(xl[1])))
+        elseif x[i] == +Inf
+            x[i] = xinvf(xf(xl[2]) + exp_fact * (xf(xl[2]) - xf(xl[1])))
         end
     end
-    for i in eachindex(y)
-        if y[i] == -Inf
-            y[i] = yinvf(yf(yl[1]) - expansion_factor * (yf(yl[2]) - yf(yl[1])))
-        elseif y[i] == Inf
-            y[i] = yinvf(yf(yl[2]) + expansion_factor * (yf(yl[2]) - yf(yl[1])))
+    x
+end
+
+function _shape_data!(::Val{true}, ::Function, ::Function, x, xl, exp_fact)
+    @inbounds for i in eachindex(x)
+        if x[i] == -Inf
+            x[i] = xl[1] - exp_fact * (xl[2] - xl[1])
+        elseif x[i] == +Inf
+            x[i] = xl[2] + exp_fact * (xl[2] - xl[1])
         end
     end
-    x, y
+    x
 end
 
-construct_categorical_data(x::AbstractArray, axis::Axis) =
-    (map(xi -> axis[:discrete_values][searchsortedfirst(axis[:continuous_values], xi)], x))
+function shape_data(series, expansion_factor = 1)
+    sp = series[:subplot]
+    xl, yl = isvertical(series) ? (xlims(sp), ylims(sp)) : (ylims(sp), xlims(sp))
+
+    # handle axes scales
+    xf, xinvf, xnoop = scale_inverse_scale_func(sp[:xaxis][:scale])
+    yf, yinvf, ynoop = scale_inverse_scale_func(sp[:yaxis][:scale])
+
+    (
+        _shape_data!(Val(xnoop), xf, xinvf, copy(series[:x]), xl, expansion_factor),
+        _shape_data!(Val(ynoop), yf, yinvf, copy(series[:y]), yl, expansion_factor),
+    )
+end
 
-function add_triangle!(I::Int, i::Int, j::Int, k::Int, x, y, z, X, Y, Z)
+function _add_triangle!(I::Int, i::Int, j::Int, k::Int, x, y, z, X, Y, Z)
     m = 4(I - 1) + 1
     n = m + 1
     o = m + 2
@@ -1059,7 +1098,7 @@ function mesh3d_triangles(x, y, z, cns::Tuple{Array,Array,Array})
     Y = zeros(eltype(y), 4length(cj))
     Z = zeros(eltype(z), 4length(ck))
     @inbounds for I in eachindex(ci)  # connections are 0-based
-        add_triangle!(I, ci[I] + 1, cj[I] + 1, ck[I] + 1, x, y, z, X, Y, Z)
+        _add_triangle!(I, ci[I] + 1, cj[I] + 1, ck[I] + 1, x, y, z, X, Y, Z)
     end
     X, Y, Z
 end
@@ -1069,7 +1108,7 @@ function mesh3d_triangles(x, y, z, cns::AbstractVector{NTuple{3,Int}})
     Y = zeros(eltype(y), 4length(cns))
     Z = zeros(eltype(z), 4length(cns))
     @inbounds for I in eachindex(cns)  # connections are 1-based
-        add_triangle!(I, cns[I]..., x, y, z, X, Y, Z)
+        _add_triangle!(I, cns[I]..., x, y, z, X, Y, Z)
     end
     X, Y, Z
 end

test/test_utils.jl

@@ -194,3 +194,13 @@ end
     # `CSV` produces `SentinelArrays` data
     @test scatter(ChainedVector([[1, 2], [3, 4]]), 1:4) isa Plot
 end
+
+@testset "performance" begin
+    with(:gr) do
+        pl = heatmap(rand(10, 10); xscale = :log10, yscale = :log10)
+        @test show(devnull, pl) isa Nothing
+
+        pl = plot(Shape([(1, 1), (2, 1), (2, 2), (1, 2)]); xscale = :log10, yscale = :log10)
+        @test show(devnull, pl) isa Nothing
+    end
+end