tpoisot

using Zygote
using Distributions

lm(x, m, b) = m .* x .+ b

x = rand(Uniform(-2, 2), 100)
ε = rand(Normal(0.0, 0.05), length(x))
p₀ = [-1.2, 0.05]

y = lm(x + ε, p₀...)

tpoisot

Longitude,Latitude,Presence,Annual Mean Temperature,Mean Diurnal Range,Isothermality,Temperature Seasonality,Max Temperature of Warmest Month,Min Temperature of Coldest Month,Temperature Annual Range,Mean Temperature of Wettest Quarter,Mean Temperature of Driest Quarter,Mean Temperature of Warmest Quarter,Mean Temperature of Coldest Quarter,Annual Precipitation,Precipitation of Wettest Month,Precipitation of Driest Month,Precipitation Seasonality,Precipitation of Wettest Quarter,Precipitation of Driest Quarter,Precipitation of Warmest Quarter,Precipitation of Coldest Quarter
4.645833333333333,52.354166666666664,0,9.8115,6.8223333,32.80599,519.0232,21.28,0.484,20.796001,10.71,8.669333,16.204666,3.662,835.0,99.0,45.0,25.123003,280.0,156.0,202.0,197.0
4.645833333333333,52.979166666666664,0,9.9,4.922222,26.727,493.6818,19.9,1.4833333,18.416666,11.4388895,10.944445,16.116667,4.2055554,813.0,103.0,46.0,29.996643,295.0,149.0,212.0,175.0
4.854166666666667,61.0625,0,7.3601193,3.9666667,24.604933,464.8009,16.164286,0.0

tpoisot

using DataFrames
using DataFramesMeta
using CSV: CSV
using CairoMakie
using AlgebraOfGraphics
using Dates
using Downloads

CairoMakie.activate!(; px_per_unit=2)

tpoisot

using Pkg
Pkg.activate(".")
using Colors
using Mustache
using Plots
using Clustering

luma(color) = luma(convert(RGB, color))
luma(color::T) where {T <: RGB} = 0.299color.r + 0.587color.g + 0.114color.b

tpoisot

import Pkg
Pkg.activate(".")

using Turing
using StatsPlots
using StatsBase
import CSV

d_orig = CSV.read("ls.csv")
d = d_orig[d_orig.P .> 0,:]

tpoisot

# http://faculty.washington.edu/joelross/courses/archive/s13/cs261/lab/k/

using EcologicalNetworks
using Plots

N = simplify(nz_stream_foodweb()[1])

mutable struct NodePosition
  x::Float64
  y::Float64

tpoisot

cbfp = Dict(
    :orange => "#e69f00",
    :sky_blue => "#56b4e9",
    :bluish_green => "#009e73",
    :yellow => "#f0e442",
    :blue => "#0072b2",
    :vermillion => "#d55e00",
    :reddish_purple => "#cc79a7"
    )

tpoisot

using Plots

λ = 1.07
K = 50.0
a = 0.06
n = 1.0

ΔN(Nt,Pt) = λ*Nt*exp((1-Nt/K)-a*Pt)
ΔP(Nt,Pt) = n*Nt*(1-exp(-a*Pt))

tpoisot

using Plots
using Iterators
pyplot()

function dn(n1, n2; α=[1.0 0.0; 0.0 1.0], r1=1.0, r2=1.0)
    dn1 = n1*(r1-α[1,1]*n1-α[1,2]*n2)
    dn2 = n2*(r2-α[2,2]*n2-α[2,1]*n1)
    return dn1, dn2
end

tpoisot

using Plots
pyplot()

"""
Derivatives
prey, predator, growth, comp, conversion, predation, mortality
"""
function ṅ(x, y, r, q, α, β, δ)
   ẋ = r*x - q*x^2 - β*y*x
   ẏ = α*β*y*x - δ*y