observation_adjustment.py

np.random.seed(123456)
N = 100
aim = 0
scale = 0.5
adjusted = []
for i in range(N):
    curr = np.random.randn() * scale + aim
    adjusted.append(curr)
    aim = -curr
non_adjusted = np.random.randn(N)*scale