# How does the retina respond to stimuli with different sparseness?

This stimulus is generated simply using the Motion Clouds library by defining a sparse draw of events:

import numpy as np
import MotionClouds as mc
import matplotlib.pyplot as plt
# PARAMETERS
seed = 2042
np.random.seed(seed=seed)
N_sparse = 5
sparse_base = 2.e5
sparseness =  np.logspace(-1, 0, N_sparse, base=sparse_base, endpoint=True)
print(sparseness)
# TEXTON
N_X, N_Y, N_frame = 256, 256, 1
fx, fy, ft = mc.get_grids(N_X, N_Y, 1)
mc_i = mc.envelope_gabor(fx, fy, ft, sf_0=0.05, B_sf=0.025, B_theta=np.inf)
values = np.random.randn(N_X, N_Y, N_frame)
chance = np.argsort(-np.abs(values.ravel()))
chance = np.array(chance, dtype=np.float)
chance /= chance.max()
chance = chance.reshape((N_X, N_Y, N_frame))
fig, axs = plt.subplots(1, N_sparse, figsize=(fig_width, fig_width/N_sparse))
for i_ax, l0_norm in enumerate(sparseness):
threshold = 1 - l0_norm
#axs[i_ax].text(9, 80, r'$n=%.0f\%%$' % (noise*100), color='white', fontsize=10)
axs[i_ax].text(4, 40, r'$\epsilon=%.0e$' % l0_norm, color='white', fontsize=8)