import numpy as np

import matplotlib.pyplot as plt

##########################################

# Exemple du cours

f = np.array([1,0,3,5,1])

g = np.array([1,2,3])

h = np.convolve(f,g,'same')

print("=== Exemple ===")

print('f =',f)

print('g =',g)

print('h =',h)

##########################################

# Exemple du cours

f = np.array([1,0,3,5,1])

g = np.array([1,2,3])

h = np.convolve(f,g,'full')

print("=== Exemple ===")

print('f =',f)

print('g =',g)

print('h =',h)

# Visualisation

def affichage_convolution(f,g,mode='same'):

h = np.convolve(f,g,mode=mode)

ax = plt.subplot(2,1,1)

ax.set_title("Entrée f")

plt.plot(f)

# ax = plt.subplot(3,1,2)

# ax.set_title("fonction g")

# plt.plot(g,color='orange')

ax = plt.subplot(2,1,2)

ax.set_title("Sortie h = f*g")

plt.plot(h,color='red')

plt.subplots_adjust(top=0.9, bottom=0.1, left=0.1, right=0.95, hspace=1.0,wspace=0.5)

# plt.tight_layout()

# plt.savefig('convolution-1d-2.png')

plt.show()

return

# Exemple 1

f = np.array([4,2,1,4,5,1,3])

g = 1/3*np.array([1,1,1])

# affichage_convolution(f,g,mode='same')

# Exemple 2 : moyenne mobile et bruit

N = 100

f = np.sin(np.linspace(0,2*np.pi,N)) + 1*np.random.random(N)

g = 1/5*np.ones(5)

affichage_convolution(f,g,mode='same')