devmotion

using MacroTools: namify
using Base.Meta: isexpr

## Utilities

# Split struct definition (neglecting constructors)

function splitstruct(structdef)
    # Split type definition
    isexpr(structdef, :struct, 3) || error("Not a type definition:", structdef)

devmotion

using MuladdMacro, EllipsisNotation, BenchmarkTools

# In place
@generated function f_loop!(out, dt, y, k, b, idxs::Nothing, ::Type{Val{N}}) where N
    sumexpr = :(+($((:(k[$i][idx]*b[$i]) for i in 1:N)...)))

    quote
        @muladd @inbounds for idx in eachindex(out)
            out[idx] = y[idx] + dt*$sumexpr
        end

devmotion

using DelayDiffEq, DiffEqParamEstim, BlackBoxOptim, DataFrames, LsqFit
using Plots
gr()

include("importData.jl")
include("plot.jl")

# import data from the path, in which:
# pop: population data
# g1, g2: g1 and g2 data

devmotion

using Turing
using StatsPlots

using Random
using Statistics

function demo(N::Int; n::Int = 10)
    # observation noise
    σ² = 0.3

devmotion

using Turing
using StatsPlots

using Random
using Statistics

function demo(N::Int; n::Int = 10)
    # observation noise
    σ² = 0.3

devmotion

using Turing
using Turing.RandomMeasures

using Random

function stickbreaking(rpm = DirichletProcess(0.25))
    # Data
    data = [-2,2,-1.5,1.5]

    # Base distribution

devmotion

devmotion

using Distributions
using SparseArrays
using LinearAlgebra
using StatsBase

function _ot_cost_plan(c, μ::DiscreteNonParametric, ν::DiscreteNonParametric; get=:plan)
    len_μ = length(μ.p)
    len_ν = length(ν.p)
    wi = μ.p[1]
    wj = ν.p[1]

devmotion

using BenchmarkTools
using Distances
using Distributions
using StatsBase

using LinearAlgebra
using SparseArrays

function f(
    c, μ::DiscreteNonParametric, ν::DiscreteNonParametric, plan::SparseMatrixCSC

devmotion

julia> using DynamicPPL

julia> @model function demo4(n, ::Type{TV}=Vector{Float64}) where {TV}
           m ~ Normal()
           x = TV(undef, n)
           @show __varinfo__
           for i in eachindex(x)
               x[i] ~ Normal(m, 1.0)
           end
       end