Fix deprecations

deps/find_attr_groups.jl

@@ -35,11 +35,11 @@ function main()
     _SRC_ATTRS = collect(filter(x -> endswith(string(x), "src"), nms))
 
     open(joinpath(@__DIR__, "src_attrs.csv"), "w") do f
-        writecsv(f, map(string, _SRC_ATTRS))
+        writedlm(f, map(string, _SRC_ATTRS))
     end
 
     open(joinpath(@__DIR__, "underscore_attrs.csv"), "w") do f
-        writecsv(f, map(string, _UNDERSCORE_ATTRS))
+        writedlm(f, map(string, _UNDERSCORE_ATTRS))
     end
 
     _UNDERSCORE_ATTRS, _SRC_ATTRS

docs/basics.md

@@ -29,19 +29,22 @@ There are three core types for representing a visualization (not counting the
 two abstract types):
 
 ```julia
-abstract AbstractTrace
-abstract AbstractLayout
+abstract type AbstractTrace 
+end
+abstract type AbstractLayout 
+
+end
 
-type GenericTrace{T<:AbstractDict{Symbol,Any}} <: AbstractTrace
+mutable struct GenericTrace{T<:AbstractDict{Symbol,Any}} <: AbstractTrace
     kind::ASCIIString
     fields::T
 end
 
-type Layout{T<:AbstractDict{Symbol,Any}} <: AbstractLayout
+mutable struct Layout{T<:AbstractDict{Symbol,Any}} <: AbstractLayout
     fields::T
 end
 
-type Plot{TT<:AbstractTrace}
+mutable struct Plot{TT<:AbstractTrace}
     data::Vector{TT}
     layout::AbstractLayout
     divid::Base.Random.UUID

docs/building_traces_layouts.md

@@ -9,7 +9,7 @@ A `Plot` instance will have a vector of `trace`s. These should each be a subtype
 PlotlyJS.jl defines one such subtype:
 
 ```julia
-type GenericTrace{T<:AbstractDict{Symbol,Any}} <: AbstractTrace
+mutable struct GenericTrace{T<:AbstractDict{Symbol,Any}} <: AbstractTrace
     kind::ASCIIString
     fields::T
 end
@@ -239,7 +239,7 @@ julia> println(JSON.json(t1, 2))
 The `Layout` type is defined as
 
 ```julia
-type Layout{T<:AbstractDict{Symbol,Any}} <: AbstractLayout
+mutable struct Layout{T<:AbstractDict{Symbol,Any}} <: AbstractLayout
     fields::T
 end
 ```

docs/contributing.md

@@ -49,7 +49,7 @@ For example, if we wanted to add an example of a scatter plot of the sin functio
 
 ```julia
 function sin_scatter()
-    x = linspace(0, 2*pi, 50)
+    x = range(0, stop=2*pi, length=50)
     y = sin(x)
     plot(scatter(x=x, y=y, marker_symbol="line-nw", mode="markers+symbols"))
 end

docs/examples/3d.md

@@ -328,7 +328,7 @@ function meshcube()
         i=[7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2],
         j=[3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3],
         k=[0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6],
-        intensity=linspace(0, 1, 8),
+        intensity=range(0, stop=1, length=8),
         colorscale=[
             [0, "rgb(255, 0, 255)"],
             [0.5, "rgb(0, 255, 0)"],

docs/examples/box_plots.md

@@ -338,7 +338,7 @@ box9()
 ```julia
 function box10()
     n_box = 30
-    colors = ["hsl($i, 50%, 50%)" for i in linspace(0, 360, n_box)]
+    colors = ["hsl($i, 50%, 50%)" for i in range(0, stop=360, length=n_box)]
 
     gen_y_data(i) =
         3.5*sin(pi*i/n_box) + i/n_box + (1.5+0.5*cos(pi*i/n_box)).*rand(10)

docs/syncplots.md

@@ -7,7 +7,7 @@ We'll also discuss how to integrate with various frontends
 Recall that the definition of the `Plot` object is
 
 ```julia
-type Plot{TT<:AbstractTrace}
+mutable struct Plot{TT<:AbstractTrace}
     data::Vector{TT}
     layout::AbstractLayout
     divid::Base.Random.UUID
@@ -124,7 +124,9 @@ To do this in a simple and reliable way we introduce the concept of a
 `SyncPlot`, which is defined as:
 
 ```julia
-abstract AbstractPlotlyDisplay
+abstract type AbstractPlotlyDisplay 
+
+end
 
 struct SyncPlot{TD<:AbstractPlotlyDisplay}
     plot::Plot
@@ -179,7 +181,7 @@ A convenient typealias has been defined for `SyncPlot`s with the
 `ElectronDisplay` frontend:
 
 ```julia
-typealias ElectronPlot SyncPlot{ElectronDisplay}
+const ElectronPlot = SyncPlot{ElectronDisplay}
 ```
 
 Please note that the Electron frontend also allows PlotlyJS.jl figures to be
@@ -201,7 +203,7 @@ for the plot as well as registering Julia functions as callbacks.
 A `SyncPlot` with a `JupyterDisplay` also has a typealias:
 
 ```julia
-typealias JupyterPlot SyncPlot{JupyterDisplay}
+const JupyterPlot = SyncPlot{JupyterDisplay}
 ```
 
 Please note that the `JupyterPlot` frontend also allows PlotlyJS.jl figures to

examples/3d.jl

@@ -234,7 +234,7 @@ function meshcube()
         i=[7, 0, 0, 0, 4, 4, 6, 6, 4, 0, 3, 2],
         j=[3, 4, 1, 2, 5, 6, 5, 2, 0, 1, 6, 3],
         k=[0, 7, 2, 3, 6, 7, 1, 1, 5, 5, 7, 6],
-        intensity=linspace(0, 1, 8),
+        intensity=range(0, stop=1, length=8),
         colorscale=[
             [0, "rgb(255, 0, 255)"],
             [0.5, "rgb(0, 255, 0)"],

examples/box_plots.jl

@@ -201,7 +201,7 @@ end
 
 function box10()
     n_box = 30
-    colors = ["hsl($i, 50%, 50%)" for i in linspace(0, 360, n_box)]
+    colors = ["hsl($i, 50%, 50%)" for i in range(0, stop=360, length=n_box)]
 
     gen_y_data(i) =
         3.5*sin(pi*i/n_box) + i/n_box + (1.5+0.5*cos(pi*i/n_box)).*rand(10)

examples/heatmaps.jl

@@ -1,5 +1,5 @@
 using PlotlyJS
-srand(42)
+Random.seed!(42)
 
 function heatmap1()
     plot(heatmap(z=[1 20 30; 20 1 60; 30 60 1]))

src/PlotlyJS.jl

@@ -1,5 +1,3 @@
-__precompile__()
-
 module PlotlyJS
 
 using Reexport

src/util.jl

@@ -4,10 +4,10 @@ JSON.lower(sp::SyncPlot) = JSON.lower(sp.plot)
 PlotlyBase._is3d(p::SyncPlot) = _is3d(p.plot)
 
 # subplot methods on syncplot
-Base.hcat{TP<:SyncPlot}(sps::TP...) = TP(hcat([sp.plot for sp in sps]...))
-Base.vcat{TP<:SyncPlot}(sps::TP...) = TP(vcat([sp.plot for sp in sps]...))
-Base.vect{TP<:SyncPlot}(sps::TP...) = vcat(sps...)
-Base.hvcat{TP<:SyncPlot}(rows::Tuple{Vararg{Int}}, sps::TP...) =
+Base.hcat(sps::TP...) where {TP<:SyncPlot} = TP(hcat([sp.plot for sp in sps]...))
+Base.vcat(sps::TP...) where {TP<:SyncPlot} = TP(vcat([sp.plot for sp in sps]...))
+Base.vect(sps::TP...) where {TP<:SyncPlot} = vcat(sps...)
+Base.hvcat(rows::Tuple{Vararg{Int}}, sps::TP...) where {TP<:SyncPlot} =
     TP(hvcat(rows, [sp.plot for sp in sps]...))
 
 

test/runtests.jl

@@ -1,7 +1,7 @@
 module PlotlyJSTest
 using TestSetExtensions
 
-using Base.Test
+using Test
 
 using PlotlyJS
 const M = PlotlyJS