获取2D numpy数组中大于阈值的元素索引

Question

我有一个2D numpy数组：

x = [[  1.92043482e-04   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   2.41005634e-03   0.00000000e+00
    7.19330120e-04   0.00000000e+00   0.00000000e+00   1.42886875e-04
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   9.79279411e-05   7.88888657e-04   0.00000000e+00
    0.00000000e+00   1.40425916e-01   0.00000000e+00   1.13955893e-02
    7.36868947e-03   3.67091988e-04   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   1.72037105e-03   1.72377961e-03
    0.00000000e+00   0.00000000e+00   1.19532061e-01   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   3.37249481e-04
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   1.75111492e-03   0.00000000e+00
    0.00000000e+00   1.12639313e-02]
 [  0.00000000e+00   0.00000000e+00   1.10271735e-04   5.98736562e-04
    6.77961628e-04   7.49569659e-04   0.00000000e+00   0.00000000e+00
    2.91697850e-03   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   3.30257021e-04   2.46629275e-04
    0.00000000e+00   1.87586441e-02   6.49103144e-04   0.00000000e+00
    1.19046355e-04   0.00000000e+00   0.00000000e+00   2.69499898e-03
    1.48525386e-02   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.18803119e-03
    3.93100829e-04   0.00000000e+00   3.76245304e-04   2.79537738e-02
    0.00000000e+00   1.20738457e-03   9.74669064e-06   7.18680093e-04
    1.61546793e-02   3.49360861e-04   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00   0.00000000e+00]]

如何获得大于0.01的元素的索引？

现在，我正在做t = np.argmax(x, axis=1)来得到每个的最大值的索引，结果是： [21 35] 。 我如何实现上述目标？

Answer 1

您可以使用np.argwhere返回匹配布尔条件的数组中所有条目的索引：

>>> x = np.array([[0,0.2,0.5],[0.05,0.01,0]])

>>> np.argwhere(x > 0.01)
array([[0, 1],
       [0, 2],
       [1, 0]])