import numpy as np
import matplotlib.pyplot as plt

# %% Parameters of uniform():

mu_n = 0 # deg C. Sub-index n for noise.
sigma_n = 0.1  # deg C
a = mu_n - np.sqrt(3)*sigma_n
b = mu_n + np.sqrt(3)*sigma_n
sample_size = 1

# %% Time settings:

t_start = 0  # s
t_stop = 5  # s
ts = 0.05  # s
t_array = np.arange(t_start, t_stop+ts, ts)

# %% Preallocation of array for storing sim results:

N = len(t_array)
T_0 = 70  # Deg C
T_array = np.zeros(N) + T_0

# %% For loop implementing iterative simulation:

for k in range(0, N):
    temp_noise_k = np.random.uniform(a,b,sample_size)[0]
    T_k = T_0 + temp_noise_k
    T_array[k] = T_k

# %% Plotting:

plt.close('all')
fig1 = plt.figure(num=1, figsize=(30/2.54, 24/2.54))#Inch

# Plotting normal dist function:
plt.plot(t_array, T_array, 'b-')
plt.plot(t_array, T_array, 'bo')
plt.plot(t_array, np.zeros(N) + T_0, 'g')
plt.plot(t_array, np.zeros(N) + T_0 + a, '--r')
plt.plot(t_array, np.zeros(N) + T_0 + b, '--r')

plt.grid()
plt.title('')
plt.xlabel('t [s]')
plt.ylabel('Deg C')
plt.show()

# plt.savefig('plot_temp_sim_with_noise.pdf')