9. Noise models

[1]:

import matplotlib.pyplot as plt
%matplotlib inline

[2]:

import numpy

[7]:

signal = numpy.random.randn(100)

[4]:

plt.plot(signal)

[4]:

[<matplotlib.lines.Line2D at 0x11cbac940>]

../../_images/2_Control_6_Discrete_control_and_analysis_Noise_models_4_1.png

[5]:

import scipy.signal

[6]:

result = []

[8]:

previous = 0

[9]:

alpha = 0.95

[10]:

for s in signal:
    news = previous*(1-alpha) + alpha*s
    previous = news
    result.append(news)

[11]:

plt.plot(result)

[11]:

[<matplotlib.lines.Line2D at 0x1c1eb4aa20>]

../../_images/2_Control_6_Discrete_control_and_analysis_Noise_models_10_1.png

[13]:

news = 0

[18]:

result = numpy.cumsum(signal)

[19]:

plt.plot(result)

[19]:

[<matplotlib.lines.Line2D at 0x1c1ecd37b8>]

../../_images/2_Control_6_Discrete_control_and_analysis_Noise_models_13_1.png

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