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"""
    fit_ellipse(x, y) → a, b, θ, x_0, y_0, p
Fit an ellipse into the 2D data defined by `(x, y)`.
Return: semi-major axis length, semi-minor axis length, ellipse rotation,
center coordinates and a parameter container for quadratic form of ellipse,
which is just `M = hcat(x.^2, x.*y, y.^2, x, y); M*p = 1`.

Code modified from:
https://www.matecdev.com/posts/julia-least-squares-qr.html
using a lot of stuff from:

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### Step 1: decide space
using Agents

space = GridSpace((10, 10); periodic = false)

### Step 2: make agent type
mutable struct SchellingAgent <: AbstractAgent
    id::Int            
    pos::NTuple{2, Int} 
    group::Int        

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# # Making animated and interactive scientific 
# # visualizations in Makie.jl

# Alrighty, in this short tutorial I'll teach you how to make
# animated scientific such as this video, or this application.

# Prerequisitives:
# * Basic familiarity with Julia (obviously)
# * Basic familiarity with Makie.jl

Datseris

using GLMakie, InteractiveDynamics, DynamicalSystems, OrdinaryDiffEq, LinearAlgebra
diffeq = (alg = Tsit5(), dtmax = 0.02, abstol = 1e-6, reltol = 1e-6)

function hassan_alkhayuon(u, p, t) # From talk of Hassan Alkhayuon
    r, c, μ, ν, α, β, χ, δ = p
    N, P = u
    common = α*N*P/(β+N)
    dN = r*N*(1 - (c/r)*N)*((N-μ)/(N+ν)) - common
    dP = χ*common - δ*P
    return SVector(dN, dP)

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using DynamicalSystems, PyPlot, LinearAlgebra, OrdinaryDiffEq
# ODE solver options:
diffeq = (alg = Vern9(), reltol = 1e-10, abstol = 1e-10)

# non-dimensionalized Rozenweig-MacArthur model
function rozenweig_macarthur(u, p, t)
    k, m, c = p
    x, y = u
    common = m*x*y/(1+x)
    xdot = x*(1-x/k) - common

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function naivehist(x::AbstractVector, ε::Real)
    mi, ma = extrema(x)
    hs = zeros(Int((ma-mi)÷ε) + 1)
    is = zeros(Int, length(x))
    for (j, e) in enumerate(x)
        xi = Int((e-mi)÷ε + 1)
        hs[xi] += 1
        is[j] = xi
    end
    return hs ./ length(x), is

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using ClimateBase, Dates
# This needs to numpy, xarray and dask installed from Conda
using PyCall
xr = pyimport("xarray")
np = pyimport("numpy")

"""
    climarray_from_xarray(xa, fieldname, name = Symbol(fieldname))
Load underlying field with given `fieldname` from the given xarray instance `xa`,
optionally providing a name for it. This `xa` can be loaded with commands like

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############### in Juno version ####################################

(AlbedoProperties) pkg> st -m
Project AlbedoProperties v0.0.0
    Status `C:\Users\m300808\ownCloud\Projects\AlbedoProperties\Manifest.toml`
  [621f4979] AbstractFFTs v0.5.0
  [85c772de] AbstractNumbers v0.2.1
  [537997a7] AbstractPlotting v0.9.17 #master (https://github.com/JuliaPlots/AbstractPlotting.jl.git)
  [27a7e980] Animations v0.3.1
  [7d9fca2a] Arpack v0.4.0

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module TraceCalls
using Cassette

mutable struct Trace
    level::Int
    cutoff::Int
end

Cassette.@context TraceCtx

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*.css