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python - 在球体表面均匀分布点的万无一失的算法?

转载 作者:行者123 更新时间:2023-12-01 00:15:10 25 4
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我一直在尝试在半径为“inner_radius”的球体表面生成点,以便它们均匀分布。该算法在半径为 1 时按预期工作,但在半径较大时生成的点少于预期。我在这里查看了类似的问题,但它们似乎是为了在整个体积内生成点,而不仅仅是在球体的表面上。

import numpy as np
PI=np.pi

def spherical_to_cartesian(pol_ang,azim_ang,radius): #This function converts given spherical coordinates (theta, phi and radius) to cartesian coordinates.
return np.array((radius*np.sin(pol_ang) * np.cos(azim_ang),
radius*np.sin(pol_ang) * np.sin(azim_ang),
radius*np.cos(pol_ang))
)

def get_electron_coordinates_list(inner_radius,electron_count):
#Algorithm used was mostly taken from https://www.cmu.edu/biolphys/deserno/pdf/sphere_equi.pdf . Explanations in code added by me.
electron_coordinate_list=[]
inner_area=4*(PI*inner_radius**2)
area_per_electron=inner_area/electron_count
pseudo_length_per_electron=np.sqrt(area_per_electron) #This is the side length of a square where the area of it is the area per electron on the sphere.
#Now, we need to get a value of angular space, such that angular space between electrons on latitude and longitude per electron is equal
#As a first step to obtaining this, we must make another value holding a whole number approximation of the ratio between PI and the pseudo_length. This will give the number of
#possible latitudes.

possible_count_of_lats=np.round(PI/pseudo_length_per_electron)

approx_length_per_electron_lat=PI/possible_count_of_lats #This is the length between electrons on a latitude
approx_length_per_electron_long=area_per_electron/approx_length_per_electron_lat #This is the length between electrons on a longitude

for electron_num_lat in range(int(possible_count_of_lats.item())): #The int(somenumpyvalue.item()) is used because Python cannot iterate over a numpy integer and it must be converted to normal int.
pol_ang=PI*(electron_num_lat+0.5)/possible_count_of_lats #The original algorithm recommended pol_ang=PI*(electron_num_lat+0.5)/possible_count_of_lats. The 0.5 appears to be added in order to get a larger number of coordinates.
#not sure if removing the 0.5 affects results. It didnt do so drastically, so what gives? Anyway, this gets the polar angle as PI*(latitudenumber)/totalnumberoflatitudes.

possible_count_of_longs=np.round(2*PI*np.sin(pol_ang)/approx_length_per_electron_long)

for electron_num_long in range(int(possible_count_of_longs.item())):

azim_ang=(2*PI)*(electron_num_long)/possible_count_of_longs #This gets the azimuthal angle as 2PI*longitudenumber/totalnumberoflongitudes

electron_coordinate=spherical_to_cartesian(pol_ang, azim_ang,inner_radius) #Converts the recieved spherical coordinates to cartesian so Manim can easily handle them.
electron_coordinate_list.append(electron_coordinate) #Add this coordinate to the electron_coordinate_list

print("Got coordinates: ",electron_coordinate) #Print the coordinate recieved.
print(len(electron_coordinate_list)," points generated.") #Print the amount of electrons will exist. Comment these two lines out if you don't need the data.

return electron_coordinate_list
get_electron_coordinates_list(1,100)
get_electron_coordinates_list(2,100)

Spherical_to_Cartesian() 只是将球面点转换为笛卡尔坐标。

对于 100 个点和半径 1,它会生成 99 个点。但是,如果半径为2,要求100点,则只能得到26点。

最佳答案

如果您可以在球体体积中均匀地生成点,那么为了在球体表面上获得均匀分布,您可以简单地对向量进行归一化,使其半径等于球体的半径。

或者,您可以使用独立同分布正态分布为 rotationally-invariant 的事实。如果从平均值为 1、标准差为 0 的 3 个正态分布中采样,然后同样对向量进行归一化,则它在球体表面上将是均匀的。这是一个例子:

import random

def sample_sphere_surface(radius=1):
x, y, z = (random.normalvariate(0, 1) for i in range(3))
scalar = radius / (x**2 + y**2 + z**2) ** 0.5
return (x * scalar, y * scalar, z * scalar)

为了绝对万无一失,我们可以处理当 xyz 都恰好为零:

def sample_sphere_surface(radius=1):
while True:
try:
x, y, z = (random.normalvariate(0, 1) for i in range(3))
scalar = radius / (x**2 + y**2 + z**2) ** 0.5
return (x * scalar, y * scalar, z * scalar)
except ZeroDivisionError:
pass

关于python - 在球体表面均匀分布点的万无一失的算法?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/59384199/

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