import random
import csv
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
import pandas as pd
import numpy as np


time = np.arange(0, 150, 1)
amplitude = np.sin(time)
amplitude_mirror = []
for x in amplitude:
    amplitude_mirror.append(x+random.uniform(-0.5,0.5))
plt.scatter(time, amplitude_mirror)
kryterium1 = 0
kryterium2 = 0
kryterium3 = 0
kryterium4 = 0
model = ARIMA(amplitude_mirror, order=(1, 0, 0))
model2 = ARIMA(amplitude_mirror, order=(0, 0, 1))
model3 = ARIMA(amplitude_mirror, order=(4, 0, 2))
model4 = ARIMA(amplitude_mirror, order=(4, 1, 2))
results_ARIMA = model.fit(disp=-1)
results_ARIMA2 = model2.fit(disp=-1)
results_ARIMA3 = model3.fit(disp=-1)
results_ARIMA4 = model4.fit(disp=-1)
#plt.plot(results_ARIMA.fittedvalues, color='green')
#plt.plot(results_ARIMA2.fittedvalues, color='blue')
plt.plot(results_ARIMA3.fittedvalues, color='red')
plt.plot(results_ARIMA4.fittedvalues, color='grey')
for i in range(0,len(results_ARIMA.fittedvalues)-1):
    kryterium1 += (results_ARIMA.fittedvalues[i]-amplitude_mirror[i])**2
    kryterium2 += (results_ARIMA2.fittedvalues[i] - amplitude_mirror[i])**2
    kryterium3 += (results_ARIMA3.fittedvalues[i] - amplitude_mirror[i])**2
    kryterium4 += (results_ARIMA4.fittedvalues[i] - amplitude_mirror[i]) ** 2
print(kryterium1)
print(kryterium2)
print(kryterium3)
print(kryterium4)
plt.show()