[英]Layering a contourf plot and surface_plot in matplotlib
I am struggling with layering and zorder
in python.我正在为 python 中的分层和
zorder
苦苦挣扎。 I am making a 3D plot using matplotlib with three relevant elements: A surface_plot
of a planet, a surface_plot
of rings around that planet, and a contourf
image that shows the planet's shadow cast onto the rings.我正在使用matplotlib有三个相关元件的3D绘图:一个
surface_plot
一个星球,一个surface_plot
周围的行星环,和contourf
图像,显示了地球的影子投射到环。
I want the graphics to display exactly how this scenario would look in real life, with the rings going around the planet and the shadow residing across the rings in the appropriate spot.我希望图形能够准确显示该场景在现实生活中的样子,环环绕地球,阴影位于环上的适当位置。 If the shadow is behind the planet for a given POV, I want the shadow to be blocked by the planet, and vice versa if the shadow is in front of the planet for a given POV.
如果阴影在给定 POV 的行星后面,我希望阴影被行星阻挡,反之亦然,如果阴影在给定 POV 的行星前面。
To be clear, this is ONLY a layering issue.需要明确的是,这只是一个分层问题。 I have the planet, rings, and shadow all plotting correctly.
我有行星,环和阴影都正确绘制。 However, the shadow will not ever display in front of the planet.
但是,阴影永远不会显示在行星前面。 It acts as though the planet is "blocking" the shadow, even though the planet is supposed to be underneath the shadow in terms of layering.
它就像行星在“阻挡”阴影一样,即使行星在分层方面应该在阴影下方。
I have tried every single thing I can think of in terms of zorder
and rearranging which order the various plot elements are called to be drawn.我已经尝试了所有我能想到的关于
zorder
和重新排列调用各种绘图元素的顺序的事情。 The rings DO correctly display in front of the planet, but the shadow will not.环确实正确显示在行星前面,但阴影不会。
My actual code is very long.我的实际代码很长。 here are the relevant parts:
以下是相关部分:
def orthographic_proj(zfront, zback):
a = (zfront+zback)/(zfront-zback)
b = -2*(zfront*zback)/(zfront-zback)
return np.array([[1,0,0,0],
[0,1,0,0],
[0,0,a,b],
[0,0,0,zback]])
def setup_saturn_plot(ax3, elev, azim, drawz, drawxy,view):
#ax3.set_aspect('equal','box')
ax3.view_init(elev=elev, azim=azim)
if(view=="top" or view == "Top" or view == "TOP"):
ax3.dist = 5.5
if(view=="star" or view == "Star" or view == "STAR"):
ax3.dist = 5.0 #4.5 is best value
proj3d.persp_transformation = orthographic_proj
# hide grid and background
ax3.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax3.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax3.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax3.grid(False)
# hide z axis in orthographic top view, xy axes in star view
if (drawz == False):
ax3.w_zaxis.line.set_lw(0.)
ax3.set_zticks([])
if (drawz == True):
ax3.set_zlabel('Z (1000 km)',fontsize=12)
if (drawxy == False):
ax3.w_xaxis.line.set_lw(0.)
ax3.set_xticks([])
ax3.w_yaxis.line.set_lw(0.)
ax3.set_yticks([])
if (drawxy == True):
ax3.set_xlabel('X (1000 km)',fontsize=12)
ax3.set_ylabel('Y (1000 km)',fontsize=12)
def draw_saturn(ax3, elev, azim):
# Saturn dimensions
radius = 60268. / 1000.
radius_pole = 54364. / 1000.
# draw Saturn
phi, theta = np.mgrid[0.0:np.pi:100j, 0.0:2.0*np.pi:100j]
x = radius*np.sin(phi)*np.cos(theta)
y = radius*np.sin(phi)*np.sin(theta)
z = radius_pole*np.cos(phi)
line3 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=5, shade=False, lw=0.25)
#line3 = ax3.plot_wireframe(x, y, z, color="w", edgecolor='b', rstride = 5, cstride=5, lw=0.25)
ax3.tick_params(labelsize=10)
def draw_rings(ax3, elev, azim, draw_mode):
# Saturn dimensions
radius = 60268. / 1000.
# Saturn rings
dringmin = 1.110 * radius
dringmax = 1.236 * radius
cringmin = 1.239 * radius
titanringlet = 1.292 * radius
maxwellgap = 1.452 * radius
cringmax = 1.526 * radius
bringmin = 1.526 * radius
bringmax = 1.950 * radius
aringmin = 2.030 * radius
enckegap = 2.214 * radius
keelergap = 2.265 * radius
aringmax = 2.270 * radius
fringmin = 2.320 * radius
gringmin = 2.754 * radius
gringmax = 2.874 * radius
eringmin = 2.987 * radius
eringmax = 7.964 * radius
if (draw_mode == 'back'):
offset = -azim*np.pi/180. - 0.5*np.pi
if (draw_mode == 'front'):
offset = -azim*np.pi/180. + 0.5*np.pi
rad, theta = np.mgrid[dringmin:dringmax:4j, 0.0-offset:1.0*np.pi-offset:100j]
x = rad * np.cos(theta)
y = rad * np.sin(theta)
z = 0. * rad
line1 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=25, shade=False, lw=0.25,alpha=0.)
rad, theta = np.mgrid[cringmin:cringmax:4j, 0.0-offset:1.0*np.pi-offset:100j]
x = rad * np.cos(theta)
y = rad * np.sin(theta)
z = 0. * rad
line2 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=25, shade=False, lw=0.25,alpha=0.)
rad, theta = np.mgrid[bringmin:bringmax:4j, 0.0-offset:1.0*np.pi-offset:100j]
x = rad * np.cos(theta)
y = rad * np.sin(theta)
z = 0. * rad
line3 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=25, shade=False, lw=0.25,alpha=0.)
rad, theta = np.mgrid[aringmin:aringmax:4j, 0.0-offset:1.0*np.pi-offset:100j]
x = rad * np.cos(theta)
y = rad * np.sin(theta)
z = 0. * rad
line4 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=25, shade=False, lw=0.25,alpha=0.)
rad, theta = np.mgrid[fringmin:1.005*fringmin:2j, 0.0-offset:1.0*np.pi-offset:100j]
x = rad * np.cos(theta)
y = rad * np.sin(theta)
z = 0. * rad
line7 = ax3.plot_surface(x, y, z, color="w", edgecolor='b', rstride = 8, cstride=25, shade=False, lw=0.1,alpha=0.)
def draw_shadowboundary(ax3, sundir):
sqrt = np.sqrt
#azimuthal angle between x direction and direction of sun
alpha = np.arctan2(sundir[1],sundir[0])
#adjustments to keep -pi/2 < alpha < pi/2
alphaadj = 0.*np.pi/180.
if (alpha<0.):
alpha += 2.*np.pi
if ((alpha >= np.pi/2.) & (alpha <= np.pi)):
alpha += np.pi
alphaadj = np.pi
if ((alpha > np.pi) & (alpha <= 3.*np.pi/2.)):
alpha -= np.pi
alphaadj = np.pi
if (alpha>3.*np.pi/2.):
alpha-=2*np.pi
#azimuthal angle between x direction and northern summer -- found using VIMS_2005_14_OMICET and VIMS_2017_053_ALPORI to define eq. of plane of Sun's annual path in chosen coordinate system: -0.193318*x + 0.1963755*y + 0.5471502*z = 0
beta = 44.5505*np.pi/180.
#Saturn's obliquity -- from NASA fact sheet
psi = 26.73*np.pi/180.
#Saturn's oblateness -- from NASA fact sheet
obl = 0.09796
#helpful definitions for optimization
cpsic = np.cos(psi*np.cos(alpha+beta))
spsic = np.sin(psi*np.cos(alpha+beta))
calpha = np.cos(alpha)
salpha = np.sin(alpha)
#Saturn's projected shorter planetary axis as seen by the sun & ring inner edge
req = 60268. / 1000.
b = req*sqrt((1.-obl)*(1.-obl)*cpsic*cpsic + spsic*spsic)
ringstart = 1.239 * req
ringend = 2.270 * req
#shadow boundary of Saturn's rings -- can approximate using a=inf and cancelling terms
a = 9.582*1.496*10.**5
shadowline = lambda x,y : (1/a)*sqrt((req*salpha*(-a+x*calpha*cpsic+y*salpha)*(y*calpha-x*cpsic*salpha)/sqrt((y*calpha-x*cpsic*salpha)**2 + (x*spsic)**2) + calpha*(a*cpsic*(x*calpha*cpsic+y*salpha) + b*x*(a-x*calpha*cpsic-y*salpha)*spsic*spsic/sqrt((y*calpha-x*cpsic*salpha)**2 + (x*spsic)**2)))**2 + (req*calpha*(a-x*calpha*cpsic-y*salpha)*(y*calpha-x*cpsic*salpha)/sqrt((y*calpha-x*cpsic*salpha)**2 + (x*spsic)**2) + salpha*(a*cpsic*(x*calpha*cpsic+y*salpha)+b*x*(a-x*calpha*cpsic-y*salpha)*spsic*spsic/sqrt((y*calpha-x*cpsic*salpha)**2 + (x*spsic)**2)))**2)
#azimuthal radius & antisolar angle for inequalities
radius = lambda x,y : np.sqrt(x**2+y**2)
anti = lambda x,y : abs(np.arctan2(y,x)-(alpha-alphaadj))
#properties of shadow
samples=1200
d = np.linspace(-3*req,3*req,samples)
x,y = np.meshgrid(d,d)
#z = ((radius(x,y)<=shadowline(x,y)) & (ringstart<=radius(x,y)) & (np.pi/2<=anti(x,y)) & (anti(x,y)<=3.*np.pi/2)).astype(int)
z = ((radius(x,y)<=shadowline(x,y)) & (ringstart<=radius(x,y)) & (radius(x,y)<=ringend) & (np.pi/2<=anti(x,y)) & (anti(x,y)<=3.*np.pi/2)).astype(int)
cmap = matplotlib.colors.ListedColormap(["k","k"])
#add shadow to plot
ax3.contourf(x,y,z, [0.5,1.50001], cmap=cmap,alpha=0.5)
import matplotlib
import numpy
from math import *
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D # <--- This is important for 3d plotting
from mpl_toolkits.mplot3d import proj3d
def plot_results(phi, theta, sundir=[0.5, 0.5]):
#plot_names.append("occultation_track_" + starname)
fig2 = plt.figure(figsize=(9,9))
ax3 = fig2.add_subplot(111, projection='3d')
setup_saturn_plot(ax3, phi, theta, False, False, "star")
draw_saturn(ax3, phi, theta)
draw_rings(ax3, phi, theta, 'back')
draw_rings(ax3, phi, theta, 'front')
draw_shadowboundary(ax3,sundir)
ax3.set_xlim([-200, 200])
ax3.set_ylim([-200, 200])
ax3.set_zlim([-200, 200])
plot_results(phi=40, theta=50, sundir = (30,60))
The code produces an image like this:代码生成这样的图像:
The grey shadow is supposed to be residing on the rings in front of the planet.灰色阴影应该位于行星前面的环上。 However, it won't display in front of the planet, so only the little sliver of shadow to the right of the planet is actually appearing.
但是,它不会显示在行星前面,因此实际上只出现了行星右侧的一小片阴影。 The shadow displays correctly in all scenarios except when it needs to go in front the planet.
阴影在所有情况下都能正确显示,除非它需要在行星前面。
Any fixes for this?对此有任何修复吗?
I'm working on getting my head around this code at the moment, but in the meantime, at least so far, this seams to be a known issue with matplotlib3d.我目前正在努力解决这段代码,但与此同时,至少到目前为止,这似乎是 matplotlib3d 的一个已知问题。
As @TheImportanceOfBeingErnest pointed out a long time ago, this issue appears in the mpl3d faq正如@TheImportanceOfBeingErnest 很久以前指出的那样,这个问题出现在mpl3d faq 中
My 3D plot doesn't look right at certain viewing angles
我的 3D 绘图在某些视角下看起来不正确
This is probably the most commonly reported issue with mplot3d.
这可能是 mplot3d 最常报告的问题。 The problem is that – from some viewing angles – a 3D object would appear in front of another object, even though it is physically behind it.
问题在于——从某些视角来看——一个 3D 对象会出现在另一个对象的前面,即使它在物理上位于它的后面。 This can result in plots that do not look “physically correct.”
这可能导致绘图看起来“物理上不正确”。
Unfortunately, while some work is being done to reduce the occurrence of this artifact, it is currently an intractable problem, and can not be fully solved until matplotlib supports 3D graphics rendering at its core.
不幸的是,虽然正在做一些工作来减少这种伪影的发生,但它目前是一个棘手的问题,在 matplotlib 支持其核心的 3D 图形渲染之前无法完全解决。
The problem occurs due to the reduction of 3D data down to 2D + z-order scalar.
问题的发生是由于 3D 数据减少到 2D + z 阶标量。 A single value represents the 3rd dimension for all parts of 3D objects in a collection.
单个值表示集合中 3D 对象所有部分的第 3 维。 Therefore, when the bounding boxes of two collections intersect, it becomes possible for this artifact to occur.
因此,当两个集合的边界框相交时,就有可能出现这种伪影。 Furthermore, the intersection of two 3D objects (such as polygons or patches) can not be rendered properly in matplotlib's 2D rendering engine.
此外,在 matplotlib 的 2D 渲染引擎中无法正确渲染两个 3D 对象(例如多边形或补丁)的交集。
This problem will likely not be solved until OpenGL support is added to all of the backends (patches are greatly welcomed).
在向所有后端添加 OpenGL 支持之前,这个问题可能无法解决(非常欢迎补丁)。 Until then, if you need complex 3D scenes, we recommend using MayaVi.
在此之前,如果您需要复杂的 3D 场景,我们建议使用 MayaVi。
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