import numpy as np
import matplotlib.pyplot as plt
from KalmanFilter import KalmanFilter
timesteps = 100
#create synthetic data
pure = np.linspace(0, 10, timesteps)
noise = np.random.normal(0, 1, timesteps)
signal = pure + noise
plt.scatter(range(100), signal, label='sensor data')
plt.title("Synthetic data")
#create filter instance and run
filter = KalmanFilter(timesteps, signal)
filter.F = 1.015
filter.Q = 1
filter.R = 50
x, P = filter.runFilter()
# visualize
print(x)
print(P)
plt.plot(x, color='red', label='prediction')
plt.plot(pure, color='black', label='actual')
plt.legend()
plt.show()
