Python 当 x 和 y 值作为 numpy 数组给出时，查找所有局部最大值和最小值
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Find all local Maxima and Minima when x and y values are given as numpy arrays
提问by prtkp
I have two arrays xand yas :
我有两个数组x和yas ：
x = np.array([6, 3, 5, 2, 1, 4, 9, 7, 8])
y = np.array([2, 1, 3, 5, 3, 9, 8, 10, 7])
I am finding index of local minima and maxima as follows:
我正在查找局部最小值和最大值的索引如下：
sortId = np.argsort(x)
x = x[sortId]
y = y[sortId]
minm = np.array([])
maxm = np.array([])
while i < y.size-1:
while(y[i+1] >= y[i]):
i = i + 1
maxm = np.insert(maxm, 0, i)
i++
while(y[i+1] <= y[i]):
i = i + 1
minm = np.insert(minm, 0, i)
i++
What is the problem in this code?
The answer should be index of minima = [2, 5, 7]and that of maxima = [1, 3, 6].
这段代码有什么问题？答案应该是minima = [2, 5, 7]和 的索引maxima = [1, 3, 6]。
采纳答案by Cleb
You do not need this whileloop at all. The code below will give you the output you want; it finds all local minima and all local maxima and stores them in minmand maxm, respectively. Please note: When you apply this to large datasets, make sure to smooth the signals first; otherwise you will end up with tons of extrema.
你根本不需要这个while循环。下面的代码会给你你想要的输出；它找到所有局部最小值和所有局部最大值，并将它们分别存储在minm和 中maxm。请注意：当您将此应用于大型数据集时，请确保先平滑信号；否则你最终会得到大量的极值。
import numpy as np
from scipy.signal import argrelextrema
import matplotlib.pyplot as plt
x = np.array([6, 3, 5, 2, 1, 4, 9, 7, 8])
y = np.array([2, 1, 3 ,5 ,3 ,9 ,8, 10, 7])
# sort the data in x and rearrange y accordingly
sortId = np.argsort(x)
x = x[sortId]
y = y[sortId]
# this way the x-axis corresponds to the index of x
plt.plot(x-1, y)
plt.show()
maxm = argrelextrema(y, np.greater) # (array([1, 3, 6]),)
minm = argrelextrema(y, np.less) # (array([2, 5, 7]),)
This should be far more efficient than the above whileloop.
这应该比上面的while循环更有效。
The plot looks like this; I shifted the x-values so that they correspond to the returned indices in minmand maxm):
情节是这样的；我移动了 x 值，使它们对应于minm和maxm) 中返回的索引：
As of SciPy version 1.1, you can also use find_peaks:
从 SciPy 1.1 版开始，您还可以使用find_peaks：
from scipy.signal import find_peaks
peaks, _ = find_peaks(y)
# this way the x-axis corresponds to the index of x
plt.plot(x-1, y)
plt.plot(peaks, y[peaks], "x")
plt.show()
That yields
这产生
The nice thing is, that you can now also easily also set a minimum peak height (e.g. 8):
好消息是，您现在还可以轻松设置最小峰高（例如 8）：
peaks, _ = find_peaks(y, height=8)
# this way the x-axis corresponds to the index of x
plt.plot(x-1, y)
plt.plot(peaks, y[peaks], "x")
plt.show()
Note that now the first peak is excluded as its height is below 8.
请注意，现在第一个峰被排除在外，因为它的高度低于 8。
Furthermore, you can set also the minimal distance between peaks (e.g. 5):
此外，您还可以设置峰值之间的最小距离（例如 5）：
peaks, _ = find_peaks(y, distance=5)
# this way the x-axis corresponds to the index of x
plt.plot(x-1, y)
plt.plot(peaks, y[peaks], "x")
plt.show()
Now the middle peak is excluded as its distance to the other two peaks is less than 5.
现在中间的峰被排除在外，因为它与其他两个峰的距离小于 5。
回答by Geeocode
This will work fine.
这将正常工作。
Python uses +=instead of ++.
Python 使用+=代替++.
Before you use iin a while loop you have to assign some value - in this case 0 - , this way initializing it to avoid error.
在使用iwhile 循环之前，您必须分配一些值 - 在本例中为 0 - ，这样初始化它以避免错误。
import numpy as np
x=np.array([6,3,5,2,1,4,9,7,8])
y=np.array([2,1,3,5,3,9,8,10,7])
sortId=np.argsort(x)
x=x[sortId]
y=y[sortId]
minm = np.array([])
maxm = np.array([])
i = 0
while i < y.size-1:
while(y[i+1] >= y[i]):
i+=1
maxm=np.insert(maxm,0,i)
i+=1
while(y[i+1] <= y[i]):
i+=1
minm=np.insert(minm,0,i)
i+=1
print minm, maxm
回答by prtkp
x=np.array([6,3,5,2,1,4,9,7,8])
y=np.array([2,1,3,5,7,9,8,10,7])
sort_idx = np.argsort(x)
y=y[sort_idx]
x=x[sort_idx]
minm=np.array([],dtype=int)
maxm=np.array([],dtype=int)
length = y.size
i=0
while i < length-1:
if i < length - 1:
while i < length-1 and y[i+1] >= y[i]:
i+=1
if i != 0 and i < length-1:
maxm = np.append(maxm,i)
i+=1
if i < length - 1:
while i < length-1 and y[i+1] <= y[i]:
i+=1
if i < length-1:
minm = np.append(minm,i)
i+=1
print minm
print maxm
minmand maxmcontain indices of minima and maxima, respectively.
minm并分别maxm包含最小值和最大值的索引。
