要创建一个1D数组，除了1.0值的延伸之外，所有值都为零-矩形脉冲：

import numpy as np
a = np.zeros( (1000,) )   # whatever size. initializes to zeros
a[150:180] = 1.0          # index range sets location, width of impulse

要查看绘图，请执行以下操作：

import matplotlib.pyplot as mp
mp.plot(a)
mp.show()

你也可以使用heaviside函数（在工程中也称为unit step函数），例如numpy.heaviside。脉冲是由两个heaviside序列之间的差异产生的：

n = np.arange(1-N, N)
n1, n2 = -3, 5 # pulse limits
pn = np.heaviside(n-n1, 1) - np.heaviside(n-n2, 1)

heaviside函数将输入数组的值更改为0、常量或1，这取决于它们是负的、空的还是正的。这里，输入数组包含被移位了与某个索引相对应的偏移量的索引值，并且常量是1。

完整编码

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as tck

# Sample numbers
N = 10
n = np.arange(1-N, N)

# Pulse limits
n1, n2 = -3, 5
pn = np.heaviside(n-n1, 1) - np.heaviside(n-n2, 1)

# Some signal clipped by pulse
xn = 3 * 0.8**n * pn

# Plot everything
fig, axes = plt.subplots(figsize=(6,6), nrows=4, sharex=True, layout='constrained')
axes = axes.flatten()

for ax, p, title in zip(axes[:2], [n1, n2], ['h1', 'h2']):
    ax.set_title(title)
    h = np.heaviside(n-p, 1)
    ax.stem(n, h)

for ax, s, title in zip(axes[2:], [pn, xn], ['pulse', 'signal clipped by pulse']):
    ax.set_title(title)
    ax.stem(n, s)

ax.xaxis.set_major_locator(tck.MultipleLocator())