pjpetersik

using StatsBase
using Plots

nt = 500
nx = 200
ny = 200

# state
x = zeros(Int8,nx,ny,nt) 

pjpetersik

mutable struct Agent
    position::Vector{Real}
    cohere_factor::Real
    repel_factor::Real
    energy::Real
    desired_rank::Real
end

struct Model
    peloton::Vector{Agent}

pjpetersik

using StatsBase

function flocking(agent::Agent, model::Model)
    N = 0
    repel = [0.0; 0.0]
    cohere = [0.0; 0.0]
    
    for other_agent in model.peloton            
        if agent != other_agent
            N += 1

pjpetersik

function energy_usage(agent::Agent, model::Model)
    usage = 0.02
    
    for other_agent in model.peloton
        heading = other_agent.position - agent.position
        distance = sum((heading).^2)^0.5
        
        if agent != other_agent          
            if distance < 0.2 && heading[1] > 0
                angle_factor = abs(acot(heading[2] / heading[1])) / π * 2

pjpetersik

using Plots 

model = Model(50)

@gif for t ∈ 1:150            
    step!(model)
    
    postions = [ agent.position for agent in model.peloton ]
    arr = hcat(postions...)
    

pjpetersik

function update_desired_rank!(agent::Agent, position_mean)
    if rand() > agent.energy
            agent.desired_rank = agent.desired_rank - 0.01 * rand()
    else 
        if rand() > 0.99
            agent.desired_rank = agent.desired_rank + max(0, (0.5 - position_mean[1])) * rand()
        end
    end
end

pjpetersik

using StatsBase
using Plots

mutable struct Agent
    position::Vector{Real}
    cohere_factor::Real
    repel_factor::Real
    energy::Real
    desired_rank::Real
end

pjpetersik

import epipack as epk
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d

def create_growth_rate_function(r, K):
    def growth_rate(t, y):
        N = np.sum(y)
        return  r * N * (1 - N / K)
    return growth_rate