我想用python在给定的边界内生成2000个三维立方体空间的随机点。要怎么去做呢?
import random
xrange = (-1000.0, 1000.0)
yrange = (-1000.0, 1000.0)
zrange = (-1000.0, 1000.0)
points = []
[ points.append((random.uniform(*xrange), random.uniform(*yrange), random.uniform(*zrange))) for i in range(2000) ]
print(points)
一个numpy版本,"axis_serie = scale_factor * rand_serie + shifted_location"。
import numpy as np
n = 2000
x1, x2 = 20, 40
y1, y2 = 10, 20
z1, z2 = 25, 50
xs = (x2 - x1)*np.random.rand(n) + x1
ys = (y2 - y1)*np.random.rand(n) + y1
zs = (z2 - z1)*np.random.rand(n) + z1
注:正如文档中指出的,在[0,1)上使用统一分布。"1 "从未达到。我不知道这对你是否重要。
如果你想要一个图 。
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs, ys, zs)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
的 uniform
numpy随机生成器类的方法接受数组作为输入,并将其广播,所以你可以在一次调用中生成值。
下面是一个例子。 首先,导入numpy并创建一个随机数生成器的实例。(我使用的是在numpy 1.17.0中引入的 "新 "numpy random API。)
In [65]: import numpy as np
In [66]: rng = np.random.default_rng()
设置区域的边界。
In [67]: x1, x2 = 1, 2
...: y1, y2 = 10, 12
...: z1, z2 = -5, 0
生成 n
样本。
In [68]: n = 10
In [69]: sample = rng.uniform([x1, y1, z1], [x2, y2, z2], size=(n, 3))
In [70]: sample
Out[70]:
array([[ 1.99165561, 10.95293326, -1.44300776],
[ 1.26473083, 11.46700288, -4.76642593],
[ 1.50086835, 10.16910997, -4.12962459],
[ 1.40330536, 10.16069764, -2.32614375],
[ 1.33484647, 11.12465768, -4.41986844],
[ 1.51458061, 10.67661873, -1.20121699],
[ 1.48522136, 10.82256589, -4.76048685],
[ 1.47682586, 10.94448464, -3.33623395],
[ 1.30821543, 11.67045336, -3.40941982],
[ 1.37784727, 11.66706056, -0.09819484]])
这也适用于传统的随机API。
In [71]: np.random.uniform([x1, y1, z1], [x2, y2, z2], size=(n, 3))
Out[71]:
array([[ 1.68445394, 11.59105704, -4.64697128],
[ 1.61346095, 10.70280999, -2.43062441],
[ 1.73148392, 11.23600717, -2.66405039],
[ 1.31235329, 11.23210203, -2.79144212],
[ 1.07450983, 10.98469372, -4.81962085],
[ 1.40672198, 11.71311779, -3.52870319],
[ 1.61392178, 10.5307566 , -2.51603141],
[ 1.92398626, 10.15939042, -3.11646383],
[ 1.85797376, 11.88704914, -0.3134136 ],
[ 1.91229518, 10.23955732, -1.18727606]])