如果numpy数组元素高于特定阈值，则将它们设置为零 [英] Set numpy array elements to zero if they are above a specific threshold

问题描述

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，因此我将得到:

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]

因为我当前使用的是for循环，所以循环非常慢:

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

考虑到很多10^6元素的数组，如何以最有效的方式实现这一目标?

How can I achieve that in the most efficient way, having in mind big arrays of, say, 10^6 elements?

推荐答案

通常，列表理解比python中的for循环要快(因为python知道它不需要关心很多可能发生的事情)在常规的for循环中):

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，由于@unutbu正确地指出了，numpy允许列表索引和逐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，它通常比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.

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