<p>您可以使用<a href="https://numpy.org/doc/stable/reference/generated/numpy.isclose.html" rel="nofollow noreferrer">numpy.isclose</a>来比较行,然后使用<code>.all(axis=2)</code>来查找最后所有行都相同的位置。Numpy将广播每一行以适应<code>xy</code>形状</p>
<pre><code>import numpy as np
xy = np.array([[[ 0.30539728, 49.82845203],
[ 0.67235022, 49.95042185],
[ 0.268982 , 49.95195348]],
[[ 0.268982 , 49.95195348],
[ 0.67235022, 49.95042185],
[ 0.27000135, 50.16334035]],
[[ 1.00647459, 50.25958169],
[ 0.79479121, 50.3010079 ],
[ 0.67235022, 49.95042185]],
[[ 0.79479121, 50.3010079 ],
[ 0.6886783 , 50.25867683],
[ 0.67235022, 49.95042185]]])
xy_start = xy.copy()
to_replace = np.array([[ 1.08267406, 49.88690993],
[ 1.1028248 , 50.01440407],
# [ 0.74114309, 49.73183549],
[ 0.6886783 , 50.25867683],
[ 1.08267406, 49.88690993]])
replace_by = np.array([ 0.87751214, 49.91866589, 0.88758751, 49.98241296, 0.70674665, 49.84112867, 0.87751214, 49.91866589])
replace_by_reshaped = replace_by.reshape(-1, 2)
for i, row in enumerate(to_replace):
xy[np.isclose(xy, row).all(axis=2)] = replace_by_reshaped[i]
print(xy_start)
# [[[ 0.30539728 49.82845203]
# [ 0.67235022 49.95042185]
# [ 0.268982 49.95195348]]
# [[ 0.268982 49.95195348]
# [ 0.67235022 49.95042185]
# [ 0.27000135 50.16334035]]
# [[ 1.00647459 50.25958169]
# [ 0.79479121 50.3010079 ]
# [ 0.67235022 49.95042185]]
# [[ 0.79479121 50.3010079 ]
# [ 0.6886783 50.25867683]
# [ 0.67235022 49.95042185]]]
print(xy)
# [[[ 0.30539728 49.82845203]
# [ 0.67235022 49.95042185]
# [ 0.268982 49.95195348]]
# [[ 0.268982 49.95195348]
# [ 0.67235022 49.95042185]
# [ 0.27000135 50.16334035]]
# [[ 1.00647459 50.25958169]
# [ 0.79479121 50.3010079 ]
# [ 0.67235022 49.95042185]]
# [[ 0.79479121 50.3010079 ]
# [ 0.70674665 49.84112867]
# [ 0.67235022 49.95042185]]]
</code></pre>
<p><strong>编辑</strong></p>
<p><code>.all(axis=2)</code>如果轴=2上的所有值都是<code>True</code>和<code>False</code>,则将轴=2收缩到<code>True</code>。我认为这个2d小例子清楚地说明了这里发生了什么</p>
<pre><code>>>> import numpy as np
>>> a = np.array([[0, 1], [0, 2], [3, 4]])
>>> a
array([[0, 1],
[0, 2],
[3, 4]])
>>> np.isclose(a, [0, 1])
array([[ True, True],
[ True, False],
[False, False]])
>>> np.isclose(a, [0, 1]).all(axis=1)
array([ True, False, False])
>>> a[np.isclose(a, [0, 1]).all(axis=1)]
array([[0, 1]])
>>> a[np.isclose(a, [0, 1]).all(axis=1)] = [12, 14]
>>> a
array([[12, 14],
[ 0, 2],
[ 3, 4]])
</code></pre>