From a688cebc81cea8c0b72a5db7eda1c3472d7a6fc5 Mon Sep 17 00:00:00 2001
From: "femtocleaner[bot]" <femtocleaner[bot]@users.noreply.github.com>
Date: Sat, 21 Oct 2017 13:23:10 -0700
Subject: [PATCH] Fix deprecations (#48)

---
 src/KernelDensity.jl |  2 +-
 src/bivariate.jl     | 12 ++++--------
 src/interp.jl        |  4 ++--
 src/univariate.jl    | 12 ++++++------
 4 files changed, 13 insertions(+), 17 deletions(-)

diff --git a/src/KernelDensity.jl b/src/KernelDensity.jl
index 93a5df1c..26aca568 100644
--- a/src/KernelDensity.jl
+++ b/src/KernelDensity.jl
@@ -14,7 +14,7 @@ import Distributions: twoπ, pdf
 
 export kde, kde_lscv, UnivariateKDE, BivariateKDE, InterpKDE, pdf
 
-@compat abstract type AbstractKDE end
+abstract type AbstractKDE end
 
 include("univariate.jl")
 include("bivariate.jl")
diff --git a/src/bivariate.jl b/src/bivariate.jl
index 6a9a4ae4..407e3154 100644
--- a/src/bivariate.jl
+++ b/src/bivariate.jl
@@ -1,22 +1,18 @@
 # Store both grid and density for KDE over R2
-type BivariateKDE{Rx<:Range,Ry<:Range} <: AbstractKDE
+mutable struct BivariateKDE{Rx<:Range,Ry<:Range} <: AbstractKDE
     x::Rx
     y::Ry
     density::Matrix{Float64}
 end
 
-function kernel_dist{D<:UnivariateDistribution}(::Type{D},w::Tuple{Real,Real})
+function kernel_dist(::Type{D},w::Tuple{Real,Real}) where D<:UnivariateDistribution
     kernel_dist(D,w[1]), kernel_dist(D,w[2])
 end
-function kernel_dist{Dx<:UnivariateDistribution,Dy<:UnivariateDistribution}(::Type{Tuple{Dx, Dy}},w::Tuple{Real,Real})
+function kernel_dist(::Type{Tuple{Dx, Dy}},w::Tuple{Real,Real}) where {Dx<:UnivariateDistribution,Dy<:UnivariateDistribution}
     kernel_dist(Dx,w[1]), kernel_dist(Dy,w[2])
 end
 
-if VERSION >= v"0.6.0-dev.2123"
-    const DataTypeOrUnionAll = Union{DataType, UnionAll}
-else
-    const DataTypeOrUnionAll = DataType
-end
+const DataTypeOrUnionAll = Union{DataType, UnionAll}
 
 # this function provided for backwards compatibility, though it doesn't have the type restrictions
 # to ensure that the given tuple only contains univariate distributions
diff --git a/src/interp.jl b/src/interp.jl
index b8ae3ab0..0d5d8674 100644
--- a/src/interp.jl
+++ b/src/interp.jl
@@ -1,9 +1,9 @@
 import Interpolations: interpolate, scale
 
-type InterpKDE{K,I} <: AbstractKDE
+mutable struct InterpKDE{K,I} <: AbstractKDE
     kde::K
     itp::I
-    @compat (::Type{InterpKDE{K,I}}){K,I}(kde::K, itp::I) = new{K,I}(kde, itp)
+    InterpKDE{K,I}(kde::K, itp::I) where {K,I} = new{K,I}(kde, itp)
 end
 
 
diff --git a/src/univariate.jl b/src/univariate.jl
index 9bea4ad9..b495aa06 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -1,5 +1,5 @@
 # Store both grid and density for KDE over the real line
-type UnivariateKDE{R<:Range} <: AbstractKDE
+mutable struct UnivariateKDE{R<:Range} <: AbstractKDE
     x::R
     density::Vector{Float64}
 end
@@ -9,7 +9,7 @@ kernel_dist(::Type{Normal},w::Real) = Normal(0.0,w)
 kernel_dist(::Type{Uniform},w::Real) = (s = 1.7320508075688772*w; Uniform(-s,s))
 
 const LocationScale = Union{Laplace,Logistic,SymTriangularDist}
-kernel_dist{D}(::Type{D},w::Real) = (s = w/std(D(0.0,1.0)); D(0.0,s))
+kernel_dist(::Type{D},w::Real) where {D} = (s = w/std(D(0.0,1.0)); D(0.0,s))
 
 
 # Silverman's rule of thumb for KDE bandwidth selection
@@ -70,14 +70,14 @@ function kde_range(boundary::Tuple{Real,Real}, npoints::Int)
     lo:step:hi
 end
 
-immutable UniformWeights{N} end
+struct UniformWeights{N} end
 
 UniformWeights(n) = UniformWeights{n}()
 
 Base.sum(x::UniformWeights) = 1.
-Base.getindex{N}(x::UniformWeights{N}, i) = 1/N
+Base.getindex(x::UniformWeights{N}, i) where {N} = 1/N
 
-typealias Weights Union{UniformWeights, RealVector, WeightVec}
+const Weights = Union{UniformWeights, RealVector, WeightVec}
 
 
 # tabulate data for kde
@@ -175,7 +175,7 @@ function kde_lscv(data::RealVector, midpoints::Range;
     K = length(k.density)
     ft = rfft(k.density)
 
-    ft2 = @compat(abs2.(ft))
+    ft2 = abs2.(ft)
     c = -twoπ/(step(k.x)*K)
     hlb, hub = bandwidth_range