<p>函数<a href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.argrelextrema.html" rel="nofollow noreferrer">^{<cd1>}</a>中存在一个bulit,它完成了此任务:</p>
<pre><code>import numpy as np
from scipy.signal import argrelextrema
a = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])
# determine the indices of the local maxima
maxInd = argrelextrema(a, np.greater)
# get the actual values using these indices
r = a[maxInd] # array([5, 3, 6])
</code></pre>
<p>这将为<code>r</code>提供所需的输出。</p>
<p>从SciPy版本1.1开始,您还可以使用<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html" rel="nofollow noreferrer">find_peaks</a>。下面是从文档本身获取的两个示例。</p>
<p>使用<code>height</code>参数,可以选择高于某个阈值的所有最大值(在本例中,所有非负最大值;如果必须处理噪声基线,这非常有用;如果要找到最小值,只需将输入乘以<code>-1</code>):</p>
<pre><code>import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np
x = electrocardiogram()[2000:4000]
peaks, _ = find_peaks(x, height=0)
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.plot(np.zeros_like(x), "--", color="gray")
plt.show()
</code></pre>
<p><a href="https://i.stack.imgur.com/2bmeW.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2bmeW.png" alt="enter image description here"/></a></p>
<p>另一个非常有用的参数是<code>distance</code>,它定义了两个峰值之间的最小距离:</p>
<pre><code>peaks, _ = find_peaks(x, distance=150)
# difference between peaks is >= 150
print(np.diff(peaks))
# prints [186 180 177 171 177 169 167 164 158 162 172]
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.show()
</code></pre>
<p><a href="https://i.stack.imgur.com/U8HBD.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/U8HBD.png" alt="enter image description here"/></a></p>