generative-AI-and-simulation-modeling/Python code for the example model at main · ali-akhavan89/generative-AI-and-simulation-modeling · GitHub

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import numpy as np
from scipy.integrate import odeint
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
import pandas as pd

data = pd.read_excel('/Models/Data.xls')

time_points = data['Time'].values
I_Data = interp1d(time_points, data['Investment Data'].values, fill_value="extrapolate")
G_Data = interp1d(time_points, data['Government Expenditure Data'].values, fill_value="extrapolate")
NX_Data = interp1d(time_points, data['Net Exports Data'].values, fill_value="extrapolate")
GDP_Data = interp1d(time_points, data['GDP Data'].values, fill_value="extrapolate")

Reference_MPC = 0.8
alpha = 0.4

def model(y, t, MPC, EFT, PAT, I, G, NX):

    EI, GDP = y
    I = I_Data(t)
    G = G_Data(t)
    NX = NX_Data(t)

    def calculate_mpc(t):
        if 2020 <= t <= 2020.25:
            return Reference_MPC * (1 - alpha)
        else:
            return Reference_MPC

    MPC = calculate_mpc(t)

    C = MPC * EI
    delta_EI = (GDP - EI) / EFT
    delta_GDP = (C + I + G + NX - GDP) / PAT

    return [delta_EI, delta_GDP]

MPC = 0.8
EFT = 2
PAT = 1
I = 10
G = 80
NX = 10

EI0 = 250
GDP0 = 250
y0 = [EI0, GDP0]

t = np.arange(2013, 2023, 0.25)

results = odeint(model, y0, t, args=(MPC, EFT, PAT, I, G, NX))

EI_results, GDP_results = results.T