对于我在大学从事的一个项目的一部分,我正在尝试使用 Python 重新创建地球,并使用它来绘制表面上的特定位置并绘制不同方向的圆圈,以便它们与我必须提供的卫星数据对齐数据集中给定时间飞机位置的表示。
我首先简单地绘制了一个线框,并在线框上绘制了我需要的点(全部按照地球及其地理区域的比例)。
我遇到的问题是,当我在球形物体上绘制点并在其上覆盖地球图像时,当球体旋转超过某个点时,这些点就会消失。所以,最初的问题是:我怎样才能阻止它们消失?
其次;我似乎找不到任何方法来绘制以球体为中心的圆 - 例如,围绕赤道的圆,然后操纵相同的想法在球体表面上绘制圆,以给出如下所示的图像:
我知道这是来自谷歌地图,但我很好奇这是否可以在Python中完成(我假设是这样)。
我当前的代码是:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from itertools import product, combinations
import PIL
#Plot the Earth
f = plt.figure(1, figsize=(13,13))
ax = f.add_subplot(111, projection='3d')
u, v = np.mgrid[0:2*np.pi:30j, 0:np.pi:20j]
x=6371*np.cos(u)*np.sin(v)
y=6371*np.sin(u)*np.sin(v)
z=6371*np.cos(v)
ax.plot_wireframe(x, y, z, color="b")
#GES ground station @ Perth & AES @ KLIA
ax.scatter([-2368.8],[4881.1],[-3342.0],color="r",s=100)
ax.scatter([-1293.0],[6238.3],[303.5],color="k",s=100)
#Load earthmap with PIL
bm = PIL.Image.open('earthmap.jpg')
#It's big, so I'll rescale it, convert to array, and divide by 256 to get RGB values that matplotlib accept
bm = np.array(bm.resize([d/3 for d in bm.size]))/256.
#d/1 is normal size, anything else is smaller - faster loading time on Uni HPC
#Coordinates of the image - don't know if this is entirely accurate, but probably close
lons = np.linspace(-180, 180, bm.shape[1]) * np.pi/180
lats = np.linspace(-90, 90, bm.shape[0])[::-1] * np.pi/180
#Repeat code specifying face colours
x = np.outer(6371*np.cos(lons), np.cos(lats)).T
y = np.outer(6371*np.sin(lons), np.cos(lats)).T
z = np.outer(6371*np.ones(np.size(lons)), np.sin(lats)).T
ax.plot_surface(x, y, z, rstride=4, cstride=4, facecolors = bm)
plt.show()
如果有什么方法可以让这些点停止消失,甚至在赤道上绘制一个圆圈,那就太好了!
谢谢!
我相信您问题的第一部分已经大部分得到解决。关于你问题的第二部分,你一般可以尝试以下方法,
%matplotlib ipympl
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Creating an arbitrary sphere
f = plt.figure(1, figsize=(10, 10))
ax = f.add_subplot(111, projection='3d')
u, v = np.mgrid[0:2*np.pi:100j, 0:np.pi:100j]
x=np.cos(u)*np.sin(v)
y=np.sin(u)*np.sin(v)
z=np.cos(v)
ax.plot_surface(x, y, z, color="b", alpha=0.3) #low alpha value allows for better visualization of scatter points
def draw_circle_on_sphere(p:float, a:float, v:float):
'''
Parametric equation determined by the radius and angular positions (both polar and azimuthal relative to the z-axis) of the circle on the spherical surface
Parameters:
p (float): polar angle
a (float): azimuthal angle
v (float): radius is controlled by sin(v)
Returns:
Circular scatter points on a spherical surface
'''
u = np.mgrid[0:2*np.pi:30j]
x = np.sin(v)*np.cos(p)*np.cos(a)*np.cos(u) + np.cos(v)*np.sin(p)*np.cos(a) - np.sin(v)*np.sin(a)*np.sin(u)
y = np.sin(v)*np.cos(p)*np.sin(a)*np.cos(u) + np.cos(v)*np.sin(p)*np.sin(a) + np.sin(v)*np.cos(a)*np.sin(u)
z = -np.sin(v)*np.sin(p)*np.cos(u) + np.cos(v)*np.cos(p)
return ax.scatter(x, y, z, color="r")
_ = draw_circle_on_sphere(3*np.pi/2, np.pi/4, np.pi/4) # example