python之通过重叠窗口在数组上循环

我有一个 numpy 数组:

seq = np.array(range(1,31)) 
 
In [24]: seq 
Out[24]: array([ 1,  2,  3, ..., 28, 29, 30]) 

我想将它分成长度为 n 的重叠(或非重叠)段。

n=7 
overlap = 3 
windowed_seq = windowing(seq,n,overlap) 
 
In [24]: windowed_seq 
Out[24]: [ 1,  5,   ...,    17, 21,  
           2,  6,   ...,    18, 22,  
           3,  7,   ...,    19, 23,  
           4,  8,   ...,    20, 24,  
           5,  9,   ...,    21, 25,  
           6,  10,  ...,    22, 26,  
           7,  11,  ...,    23, 27]  

最后一栏:

25, 
26, 
27, 
28, 
29, 
30, 
31, 
?] 

被省略是因为缺少一个元素来完成窗口..

我在网上找到了这个解决方案，但它不适用于数组...

https://www.safaribooksonline.com/library/view/python-cookbook-2nd/0596007973/ch19s08.html

import itertools 
def windows(iterable, length=2, overlap=0): 
    it = iter(iterable) 
    results = list(itertools.islice(it, length)) 
    while len(results) == length: 
        yield results 
        results = results[length-overlap:] 
        results.extend(itertools.islice(it, length-overlap)) 
    if results: 
        yield results 
if _ _name_ _ == '_ _main_ _': 
    seq = 'foobarbazer' 
    for length in (3, 4): 
        for overlap in (0, 1): 
            print '%d %d: %s' % (length, overlap, 
                    map(''.join, windows(seq, length, overlap))) 

你能帮我让它为我的阵列工作吗？

请您参考如下方法:

您可以使用 scikit 图像的 view_as_windows :

import skimage 
import numpy 
 
data = numpy.arange(1, 31) 
print skimage.util.view_as_windows(data, 7, step=3).T 

产量

[[ 1  4  7 10 13 16 19 22] 
 [ 2  5  8 11 14 17 20 23] 
 [ 3  6  9 12 15 18 21 24] 
 [ 4  7 10 13 16 19 22 25] 
 [ 5  8 11 14 17 20 23 26] 
 [ 6  9 12 15 18 21 24 27] 
 [ 7 10 13 16 19 22 25 28]] 

如果你想确保你的数组正好是你可以使用的窗口长度的倍数

data = numpy.append(data, numpy.repeat(numpy.nan, len(data) % 7)) 

之后你的输出变成

[[  1.   4.   7.  10.  13.  16.  19.  22.  25.] 
 [  2.   5.   8.  11.  14.  17.  20.  23.  26.] 
 [  3.   6.   9.  12.  15.  18.  21.  24.  27.] 
 [  4.   7.  10.  13.  16.  19.  22.  25.  28.] 
 [  5.   8.  11.  14.  17.  20.  23.  26.  29.] 
 [  6.   9.  12.  15.  18.  21.  24.  27.  30.] 
 [  7.  10.  13.  16.  19.  22.  25.  28.  nan]]