Python/Numpy - 将切片包裹在数组末尾

发布于 2024-11-01 13:17:06 字数 576 浏览 3 评论 0原文

我有两个一维数组，一个包含一些感兴趣的值 (a)，另一个提供该数组的索引 (b)。我知道 b 中的值总是增加，除了在某一点（可能是任何地方），该点的数字会减少，因为它从数组 a 的末尾滚动到开头。下面的方法似乎有效，但我只是认为必须存在一种更干净的方法。谁能建议更好的东西吗？谢谢。

代码：

import numpy as np
a = np.arange(12)
b = np.array([5, 9, 2, 4])
#I want to generate these:
#[5,6,7,8,9]
#[9,10,11,0,1,2]
#[2,3,4]
#[4,5]

a = np.roll(a, -b[0], axis=0)
# Subtract off b[0] but ensure that all values are positive
b = (b-b[0]+len(a))%len(a)
for i, ind in enumerate(b):
   if i < len(b)-1:
      print a[b[i]:b[i+1]+1]
   else:
      print np.hstack((a[b[i]:len(a)], a[0]))

原文

I have two 1D arrays, one that has some values of interest (a) and another that provides indices into that array (b). I know that the values in b always increase, except at one point (could be anywhere) where the number decreases since it rolls from the end to the beginning of array a. The method below seems to work, but I just think that a cleaner way must exist. Can anyone suggest something better? Thanks.

Code:

import numpy as np
a = np.arange(12)
b = np.array([5, 9, 2, 4])
#I want to generate these:
#[5,6,7,8,9]
#[9,10,11,0,1,2]
#[2,3,4]
#[4,5]

a = np.roll(a, -b[0], axis=0)
# Subtract off b[0] but ensure that all values are positive
b = (b-b[0]+len(a))%len(a)
for i, ind in enumerate(b):
   if i < len(b)-1:
      print a[b[i]:b[i+1]+1]
   else:
      print np.hstack((a[b[i]:len(a)], a[0]))

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浅沫记忆 2024-11-08 13:17:07

有点短，但也许我还能做得更好......

import numpy as np

a = np.arange(12)
b = np.array([5, 9, 2, 4])
b = np.append(b, b[0])

for i in range(0, len(b)-1):
    print np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1]

A bit shorter, but maybe I can still do better...

import numpy as np

a = np.arange(12)
b = np.array([5, 9, 2, 4])
b = np.append(b, b[0])

for i in range(0, len(b)-1):
    print np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1]

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清旖 2024-11-08 13:17:07

不确定这是否有帮助，但不扰乱 a 内存的快速方法是这样的：

import numpy as np

a = np.arange(12)
b = np.array([5, 9, 2, 4])
b = np.append(b, b[0])

b2 = b.copy()

b2[(np.diff(b)<0).nonzero()[0]+1:] += a.size

print [np.take(a, np.r_[b2[i]:b2[i+1]+1], mode='wrap') for i in range(b.size-1)]

print [np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1] for i in range(b.size-1)]

%timeit [np.take(a, np.r_[b2[i]:b2[i+1]+1], mode='wrap') for i in range(b.size-1)]
# 10000 loops, best of 3: 28.6 µs per loop

%timeit [np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1] for i in range(b.size-1)]
# 10000 loops, best of 3: 77.7 µs per loop

Not sure if this helps, but a fast way without messing with the memory of a would be this:

import numpy as np

a = np.arange(12)
b = np.array([5, 9, 2, 4])
b = np.append(b, b[0])

b2 = b.copy()

b2[(np.diff(b)<0).nonzero()[0]+1:] += a.size

print [np.take(a, np.r_[b2[i]:b2[i+1]+1], mode='wrap') for i in range(b.size-1)]

print [np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1] for i in range(b.size-1)]

%timeit [np.take(a, np.r_[b2[i]:b2[i+1]+1], mode='wrap') for i in range(b.size-1)]
# 10000 loops, best of 3: 28.6 µs per loop

%timeit [np.roll(a, len(a)-b[i])[:b[i+1]-b[i]+1] for i in range(b.size-1)]
# 10000 loops, best of 3: 77.7 µs per loop

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~没有更多了~