在 Python 中定义白噪声过程
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Defining a white noise process in Python
提问by dbliss
I need to draw samples from a white noise process in order to implement a particular integral numerically.
我需要从白噪声过程中抽取样本，以便在数值上实现特定的积分。
How do I generate this with Python (i.e., numpy, scipy, etc.)?
如何使用 Python（即 numpy、scipy 等）生成它？
采纳答案by Sam
You can achieve this through the numpy.random.normalfunction, which draws a given number of samples from a Gaussian distribution.
您可以通过numpy.random.normal函数实现这一点，该函数从高斯分布中抽取给定数量的样本。
import numpy
import matplotlib.pyplot as plt
mean = 0
std = 1
num_samples = 1000
samples = numpy.random.normal(mean, std, size=num_samples)
plt.plot(samples)
plt.show()
回答by user8866568
Short answer is numpy.random.random(). Numpy site description
简短的回答是numpy.random.random()。Numpy 站点描述
But since I find more and more answers to similar questions written as numpy.random.normal, I suspect a little description is needed. If I do understand Wikipedia (and a few lessons at the University) correctly, Gauss and White Noise are two separate things. White noise has Uniform distribution, not Normal (Gaussian).
但由于我发现越来越多的类似问题的答案写成numpy.random.normal，我怀疑需要一些描述。如果我确实正确理解了维基百科（以及大学的一些课程），那么高斯和白噪声是两个独立的事物。白噪声具有均匀分布，而不是正态（高斯）。
import numpy.random as nprnd
import matplotlib.pyplot as plt
num_samples = 10000
num_bins = 200
samples = numpy.random.random(size=num_samples)
plt.hist(samples, num_bins)
plt.show()
This is my first answer, so if you correct mistakes possibly made by me here, I'll gladly update it. Thanks =)
这是我的第一个答案，所以如果您纠正我在这里可能犯的错误，我会很乐意更新。谢谢=)
回答by Ryan Yarahmadian
Create random samples with normal distribution (Gaussian) with numpy.random.normal:
创建具有正态分布（高斯）的随机样本numpy.random.normal：
import numpy as np
import seaborn as sns
mu, sigma = 0, 1 # mean and standard deviation
s = np.random.normal(mu, sigma, size=1000) # 1000 samples with normal distribution
# seaborn histogram with Kernel Density Estimation
sns.distplot(s, bins=40, hist_kws={'edgecolor':'black'})
