问题描述
比如说,我有一个由 10
元素组成的 numpy 数组,例如:
Say, I have a numpy array consists of 10
elements, for example:
a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])
现在我想有效地将所有高于 10
的 a
值设置为 0
,所以我会得到:
Now I want to efficiently set all a
values higher than 10
to 0
, so I'll get:
[2, 0, 0, 7, 9, 0, 0, 0, 5, 3]
因为我目前使用的是 for
循环,它非常慢:
Because I currently use a for
loop, which is very slow:
# Zero values below "threshold value".
def flat_values(sig, tv):
"""
:param sig: signal.
:param tv: threshold value.
:return:
"""
for i in np.arange(np.size(sig)):
if sig[i] < tv:
sig[i] = 0
return sig
我怎样才能以最有效的方式实现这一点,考虑到 10^6
元素的大数组?
How can I achieve that in the most efficient way, having in mind big arrays of, say, 10^6
elements?
推荐答案
一般情况下,列表推导比 Python 中的 for
循环更快(因为 Python 知道它不需要关心很多在常规 for
循环中可能发生的事情):
Generally, list comprehensions are faster than for
loops in python (because python knows that it doesn't need to care for a lot of things that might happen in a regular for
loop):
a = [0 if a_ > thresh else a_ for a_ in a]
但是,正如@unutbu 正确指出,numpy 允许列表索引,并且元素比较为您提供索引列表,所以:
but, as @unutbu correctly pointed out, numpy allows list indexing, and element-wise comparison giving you index lists, so:
super_threshold_indices = a > thresh
a[super_threshold_indices] = 0
会更快.
通常,在对数据向量应用方法时,请查看 numpy.ufuncs
,它的性能通常比您使用任何本机机制映射的 Python 函数要好得多.
Generally, when applying methods on vectors of data, have a look at numpy.ufuncs
, which often perform much better than python functions that you map using any native mechanism.
这篇关于如果 numpy 数组元素高于特定阈值,则将它们设置为零的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!