NoFishLikeIan

### A Pluto.jl notebook ###
# v0.20.3

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    #! format: off
    quote

NoFishLikeIan

import numpy as np
import numpy.typing as npt


def simulate(P:npt.NDArray, T:int, s0:int = 0) -> np.ndarray:
    'Simulates with transition matrix P for T steps, starting at s0 = 0.'

    u = np.random.uniform(0., 1., T)
    S = np.empty(T, dtype = int)
    S[0] = s0

NoFishLikeIan

### A Pluto.jl notebook ###
# v0.19.22

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end

NoFishLikeIan

import numpy as np
import matplotlib.pyplot as plt

"""
Compute the trajectory of a function f: (R^n x R) -> R^n, where x_{t + 1} = f(x_t, beta). 
"""
def trajectory(f, beta, x0: np.ndarray, T: int) -> np.ndarray:

    n = x0.shape[0]
    X = np.zeros((T + 1, n), dtype = np.float64)

NoFishLikeIan

import numpy as np
import matplotlib.pyplot as plt

# This function takes previous estimations of s(t) and generates a new RUNGE-KUTTA estimation based upon given timepoints


def NextApproximation(f, t, s, h):

    k1 = f(t, s)
    k2 = f(t + h/2, s + h*(k1/2))

NoFishLikeIan

"Pair"
function ↑(a::String, b::String)
    a * '0' * b * a * '1' * b
end

"""
Unpair one step
"""
function ↓(p::String)
    mid = length(p) ÷ 2    

NoFishLikeIan

#!/bin/bash
filename=$1
echo "timeout --foreground 2h python3 $filename" | at 2300

NoFishLikeIan

function plotattractors(basins, attractors, x_space; kwargs...)

	lims = extrema(x_space)
	
	attractors_keys = keys(attractors) |> collect |> sort
	isdivergent = -1 ∈ basins # Check whether there is a divergent region
	isunique = length(unique(basins)) == 1  # Check whether the basin is uniform
	
	levels = isdivergent ? [-1, attractors_keys..., Inf] : [attractors_keys..., Inf]
	

NoFishLikeIan

### A Pluto.jl notebook ###
# v0.16.4

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local el = $(esc(element))

NoFishLikeIan

begin
	function plotvectorfield(xs, ys, g::Function; plotkwargs...)
	    fig = plot()
	    plotvectorfield!(fig, xs, ys, g; plotkwargs...)
	    return fig
	end
	
	function plotvectorfield!(figure, xs, ys, g::Function; rescale = 1, plotkwargs...)
	
		xlims = extrema(xs)