Python 如何通过索引列表过滤numpy数组？
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How to filter numpy array by list of indices?
提问by Barbarossa
I am relatively new to python and have been trying to learn how to use numpy and scipy. I have a numpy array comprised of LAS data [x, y, z, intensity, classification]. I have created a cKDTree of points and have found nearest neighbors using query_ball_point. I would like to find standard deviation of the z values for the neighbors returned by query_ball_point, which returns a list of indices for the point and its neighbors.
我对 python 比较陌生，一直在尝试学习如何使用 numpy 和 scipy。我有一个由 LAS 数据 [x、y、z、强度、分类] 组成的 numpy 数组。我创建了一个 cKDTree 点，并使用query_ball_point找到了最近的邻居。我想找到 query_ball_point 返回的邻居的 z 值的标准偏差，它返回该点及其邻居的索引列表。
Is there a way to filter filtered__rows to create an array of only points whose index is in the list returned by query_ball_point? See code below. I can append the values to a list and calculate std dev from that, but I think it would be easier to use numpy to calculate std dev on a single axis. Thanks in advance.
有没有办法过滤filtered__rows以创建一个仅包含索引位于query_ball_point返回的列表中的点的数组？请参阅下面的代码。我可以将值附加到列表中并从中计算 std dev，但我认为使用 numpy 在单个轴上计算 std dev 会更容易。提前致谢。
# Import modules
from liblas import file
import numpy as np
import scipy.spatial
if __name__=="__main__":
'''Read LAS file and create an array to hold X, Y, Z values'''
# Get file
las_file = r"E:\Testing\kd-tree_testing\LE_K20_clipped.las"
# Read file
f = file.File(las_file, mode='r')
# Get number of points from header
num_points = int(f.__len__())
# Create empty numpy array
PointsXYZIC = np.empty(shape=(num_points, 5))
# Load all LAS points into numpy array
counter = 0
for p in f:
newrow = [p.x, p.y, p.z, p.intensity, p.classification]
PointsXYZIC[counter] = newrow
counter += 1
'''Filter array to include classes 1 and 2'''
# the values to filter against
unclassified = 1
ground = 2
# Create an array of booleans
filter_array = np.any([PointsXYZIC[:, 4] == 1, PointsXYZIC[:, 4] == 2], axis=0)
# Use the booleans to index the original array
filtered_rows = PointsXYZIC[filter_array]
'''Create a KD tree structure and segment the point cloud'''
tree = scipy.spatial.cKDTree(filtered_rows, leafsize=10)
'''For each point in the point cloud use the KD tree to identify nearest neighbors,
with a K radius'''
k = 5 #meters
for pntIndex in range(len(filtered_rows)):
neighbor_list = tree.query_ball_point(filtered_rows[pntIndex], k)
zList = []
for neighbor in neighbor_list:
neighbor_z = filtered_rows[neighbor, 2]
zList.append(neighbor_z)
采纳答案by Joran Beasley
ummmm Its hard to tell whats being asked (thats quite the wall of text)
嗯，很难说出被问到的问题（那是一堆文字）
filter_indices = [1,3,5]
print numpy.array([11,13,155,22,0xff,32,56,88])[filter_indices]
may be what you are asking
可能是你要问的
回答by Leaderchicken
Do you know how that translates for multi-dimensional arrays?
你知道多维数组是如何转换的吗？
It can be expanded to multi dimensional arrays by giving a 1d array for every index so for a 2d array
filter_indices=np.array([[1,0],[0,1]])
array=np.array([[0,1],[1,2]])
print(array[filter_indices[:,0],filter_indices[:,1])
通过为每个索引提供一维数组，它可以扩展为多维数组，因此对于二维数组
filter_indices=np.array([[1,0],[0,1]])
array=np.array([[0,1],[1,2]])
print(array[filter_indices[:,0],filter_indices[:,1])
will give you : [1,1]
会给你：[1,1]
Scipy has an explanation on what will happen if you call:
print(array[filter_indices])
Scipy 有一个解释，如果你打电话会发生什么：
print(array[filter_indices])
https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
回答by gnuchoi
numpy.takecan be useful and works well for multimensional arrays.
numpy.take可能很有用，并且适用于多维度阵列。
import numpy as np
filter_indices = [1, 2]
axis = 0
array = np.array([[1, 2, 3, 4, 5],
[10, 20, 30, 40, 50],
[100, 200, 300, 400, 500]])
print(np.take(array, filter_indices, axis))
# [[ 10 20 30 40 50]
# [100 200 300 400 500]]
axis = 1
print(np.take(array, filter_indices, axis))
# [[ 2 3]
# [ 20 30]
# [200 300]]
