From a2ff1649d3606cd33f0354a976d72c3eb6105760 Mon Sep 17 00:00:00 2001
From: Christopher Rackauckas <accounts@chrisrackauckas.com>
Date: Wed, 6 Jan 2021 10:20:57 -0500
Subject: [PATCH] Give UnivariateKDE distribution-like overloads

Upstreams https://github.com/SciML/DiffEqUncertainty.jl/pull/41 . Allows for it to be "distribution-like". Still missing the random sampling.
---
 src/univariate.jl | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/univariate.jl b/src/univariate.jl
index 825626fb..92a17faf 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -20,6 +20,10 @@ mutable struct UnivariateKDE{R<:AbstractRange} <: AbstractKDE
     density::Vector{Float64}
 end
 
+Base.eltype(K::UnivariateKDE)  = eltype(K.density)
+Base.minimum(K::UnivariateKDE) = minimum(K.x)
+Base.maximum(K::UnivariateKDE) = maximum(K.x)
+
 # construct kernel from bandwidth
 kernel_dist(::Type{Normal},w::Real) = Normal(0.0,w)
 kernel_dist(::Type{Uniform},w::Real) = (s = 1.7320508075688772*w; Uniform(-s,s))