blocks|key|28695|text|问题是matplotlib不是光线跟踪器，它并不是真正设计成一个3D绘图库。因此，它与2D空间中的层系统一起工作，对象可以位于更前面或更后面的层中。这可以通过大多数绘图函数的zorder关键字参数来设置。然而，matplotlib没有意识到一个对象在3D空间中是在另一个对象的前面还是后面。因此，您既可以让完整的线可见(在球体前面)，也可以隐藏(在球体后面)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|28696|解决方案是计算你自己应该是可见的点，我在这里说的是点，因为一条线将“穿过”球体连接可见的点，这是不需要的。因此，我将自己局限于绘制点-但如果您有足够的点，它们看起来就像一条线:-)。或者，可以通过在不连接的点之间使用额外的nan坐标来隐藏直线；在这里，我将自己限制在点上，以避免使解决方案比需要的更复杂。|28697|对于一个完美的球体来说，计算哪些点应该是可见的并不是太难，想法如下：|28698|28699|从中获得3D+plot|ordered-list-item|28700|的视角，在view.|28701|Calculate方向的数据坐标中计算到视觉平面的法向量这个法向量(在下面的代码中称为X+)和线点之间的标量积，以便使用这个标量积作为是否显示点的条件。如果标量积小于条件，则相应的点在观察者所看到的观察平面的另一侧，因此不应通过条件来shown.|28702|Filter+0点。|28703|28704|另一个可选任务是调整所显示的点，以适应用户旋转视图时的情况。这是通过将motion_notify_event连接到基于新设置的视角使用上述过程更新数据的功能来实现的。|28705|有关如何实现这一点，请参阅下面的代码。|28706|import+matplotlib
import+matplotlib.pyplot+as+plt
from+mpl_toolkits.mplot3d+import+Axes3D
import+numpy+as+np


NPoints_Phi+++++++++=+30
NPoints_Theta+++++++=+30

phi_array+++++++++++=+((np.linspace(0,+1,+NPoints_Phi))**1)+*+2*np.pi
theta_array+++++++++=+(np.linspace(0,+1,+NPoints_Theta)+**1)+*+np.pi

radius=1
phi,+theta++++++++++=+np.meshgrid(phi_array,+theta_array)+

x_coord+++++++++++++=+radius*np.sin(theta)*np.cos(phi)
y_coord+++++++++++++=+radius*np.sin(theta)*np.sin(phi)
z_coord+++++++++++++=+radius*np.cos(theta)

#Make+colormap+the+fourth+dimension
color_dimension+++++=+x_coord+
minn,+maxx++++++++++=+color_dimension.min(),+color_dimension.max()
norm++++++++++++++++=+matplotlib.colors.Normalize(minn,+maxx)
m+++++++++++++++++++=+plt.cm.ScalarMappable(norm=norm,+cmap='jet')
m.set_array([])
fcolors+++++++++++++=+m.to_rgba(color_dimension)

theta2++++++++++++++=+np.linspace(-np.pi,++0,+1000)
phi2++++++++++++++++=+np.linspace(+0,+5+*+2*np.pi+,+1000)

x_coord_2+++++++++++=+radius+*+np.sin(theta2)+*+np.cos(phi2)
y_coord_2+++++++++++=+radius+*+np.sin(theta2)+*+np.sin(phi2)
z_coord_2+++++++++++=+radius+*+np.cos(theta2)

#+plot
fig+=+plt.figure()

ax+=+fig.gca(projection='3d')
#+plot+empty+plot,+with+points+(without+a+line)
points,+=+ax.plot([],[],[],'k.',+markersize=5,+alpha=0.9)
#set+initial+viewing+angles
azimuth,+elev+=+75,+21
ax.view_init(elev,+azimuth+)

def+plot_visible(azimuth,+elev):
++++#transform+viewing+angle+to+normal+vector+in+data+coordinates
++++a+=+azimuth*np.pi/180.+-np.pi
++++e+=+elev*np.pi/180.+-+np.pi/2.
++++X+=+[+np.sin(e)+*+np.cos(a),np.sin(e)+*+np.sin(a),np.cos(e)]++
++++#+concatenate+coordinates
++++Z+=+np.c_[x_coord_2,+y_coord_2,+z_coord_2]
++++#+calculate+dot+product+
++++#+the+points+where+this+is+positive+are+to+be+shown
++++cond+=+(np.dot(Z,X)+>=+0)
++++#+filter+points+by+the+above+condition
++++x_c+=+x_coord_2[cond]
++++y_c+=+y_coord_2[cond]
++++z_c+=+z_coord_2[cond]
++++#+set+the+new+data+points
++++points.set_data(x_c,+y_c)
++++points.set_3d_properties(z_c,+zdir="z")
++++fig.canvas.draw_idle()

plot_visible(azimuth,+elev)
ax.plot_surface(x_coord,y_coord,z_coord,+rstride=1,+cstride=1,+
++++++++++++facecolors=fcolors,+vmin=minn,+vmax=maxx,+shade=False)

#+in+order+to+always+show+the+correct+points+on+the+sphere,+
#+the+points+to+be+shown+must+be+recalculated+one+the+viewing+angle+changes
#+when+the+user+rotates+the+plot
def+rotate(event):
++++if+event.inaxes+==+ax:
++++++++plot_visible(ax.azim,+ax.elev)

c1+=+fig.canvas.mpl_connect('motion_notify_event',+rotate)

plt.show()|code-block|syntax|javascript|28707|​|28708|📷|atomic|28709|28710|在最后，人们可能不得不玩一点markersize，alpha和点数，以获得最具视觉吸引力的结果。|28711|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/45e24cc60859a4bb9d603956a9846dee.png|imageAlt^0|2F|6|0|33|3|0|0|0|0|0|17|1|0|7|1|0|0|Z|J|0|0|0|0|0|1|0|0|0|E|A|P|5|0^^$0|@$1|2|3|4|5|6|7|1K|8|@$9|1L|A|1M|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1N|8|@$9|1O|A|1P|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1Q|8|@]|D|@]|E|$]]|$1|J|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|1S|8|@]|D|@]|E|$]]|$1|N|3|O|5|M|7|1T|8|@]|D|@]|E|$]]|$1|P|3|Q|5|M|7|1U|8|@$9|1V|A|1W|B|C]]|D|@]|E|$]]|$1|R|3|S|5|M|7|1X|8|@$9|1Y|A|1Z|B|C]]|D|@]|E|$]]|$1|T|3|-4|5|6|7|20|8|@]|D|@]|E|$]]|$1|U|3|V|5|6|7|21|8|@$9|22|A|23|B|C]]|D|@]|E|$]]|$1|W|3|X|5|6|7|24|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|10|7|25|8|@]|D|@]|E|$11|12]]|$1|13|3|14|5|6|7|26|8|@]|D|@]|E|$]]|$1|15|3|16|5|17|7|27|8|@]|D|@$9|28|A|29|1|2A]]|E|$]]|$1|18|3|14|5|6|7|2B|8|@]|D|@]|E|$]]|$1|19|3|1A|5|6|7|2C|8|@$9|2D|A|2E|B|C]|$9|2F|A|2G|B|C]]|D|@]|E|$]]|$1|1B|3|-4|5|6|7|2H|8|@]|D|@]|E|$]]]|1C|$1D|$5|1E|1F|1G|E|$1H|1I|1J|-4]]]]

The problem is that matplotlib is no ray tracer and it's not really designed to be a 3D capable plotting library. As such it works with a system of layers in 2D space, and objects can be in a layer more in front or more to the back. This can be set with the <code>zorder</code> keyword argument to most plotting functions. However there is no awareness in matplotlib about whether an object is in front or behind another object in 3D space. Therefore you can either have the complete line visible (in front of the sphere) or hidden (behind it). 

The solution would be to calculate the points that should be visible by yourself. I'm talking about points here because a line would be connecting visible points "through" the sphere, which is unwanted. I therefore restrict myself to plotting points - but if you have enough of them, they look like a line :-). Alternatively lines can be hidden by using an additional <code>nan</code> coordinate in between points that are not to be connected; I'm restricting myself to points here not to make the solution more complicated than it needs to be.

The calculation of which points should be visible is not too hard for a perfect sphere, and the idea is the following: 

<ol>
<li>Obtain the viewing angle of the 3D plot</li>
<li>From that, calculate the normal vector to the plane of vision in data coordinates in direction of the view.</li>
<li>Calculate the scalar product between this normal vector (called <code>X</code> in the code below) and the line points in order to use this scalar product as a condition on whether to show the points or not. If the scalar product is smaller than <code>0</code> then the respective point is on the other side of the viewing plane as seen from the observer and should therefore not be shown.</li>
<li>Filter the points by the condition.</li>
</ol>

One further optional task is then to adapt the points shown for the case when the user rotates the view. This is accomplished by connecting the <code>motion_notify_event</code> to a function that updates the data using the procedure from above, based on the newly set viewing angle.

See the code below on how to implement this. 

<pre><code>import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np


NPoints_Phi = 30
NPoints_Theta = 30

phi_array = ((np.linspace(0, 1, NPoints_Phi))**1) * 2*np.pi
theta_array = (np.linspace(0, 1, NPoints_Theta) **1) * np.pi

radius=1
phi, theta = np.meshgrid(phi_array, theta_array) 

x_coord = radius*np.sin(theta)*np.cos(phi)
y_coord = radius*np.sin(theta)*np.sin(phi)
z_coord = radius*np.cos(theta)

#Make colormap the fourth dimension
color_dimension = x_coord 
minn, maxx = color_dimension.min(), color_dimension.max()
norm = matplotlib.colors.Normalize(minn, maxx)
m = plt.cm.ScalarMappable(norm=norm, cmap='jet')
m.set_array([])
fcolors = m.to_rgba(color_dimension)

theta2 = np.linspace(-np.pi, 0, 1000)
phi2 = np.linspace( 0, 5 * 2*np.pi , 1000)

x_coord_2 = radius * np.sin(theta2) * np.cos(phi2)
y_coord_2 = radius * np.sin(theta2) * np.sin(phi2)
z_coord_2 = radius * np.cos(theta2)

# plot
fig = plt.figure()

ax = fig.gca(projection='3d')
# plot empty plot, with points (without a line)
points, = ax.plot([],[],[],'k.', markersize=5, alpha=0.9)
#set initial viewing angles
azimuth, elev = 75, 21
ax.view_init(elev, azimuth )

def plot_visible(azimuth, elev):
 #transform viewing angle to normal vector in data coordinates
 a = azimuth*np.pi/180. -np.pi
 e = elev*np.pi/180. - np.pi/2.
 X = [ np.sin(e) * np.cos(a),np.sin(e) * np.sin(a),np.cos(e)] 
 # concatenate coordinates
 Z = np.c_[x_coord_2, y_coord_2, z_coord_2]
 # calculate dot product 
 # the points where this is positive are to be shown
 cond = (np.dot(Z,X) &gt;= 0)
 # filter points by the above condition
 x_c = x_coord_2[cond]
 y_c = y_coord_2[cond]
 z_c = z_coord_2[cond]
 # set the new data points
 points.set_data(x_c, y_c)
 points.set_3d_properties(z_c, zdir="z")
 fig.canvas.draw_idle()

plot_visible(azimuth, elev)
ax.plot_surface(x_coord,y_coord,z_coord, rstride=1, cstride=1, 
 facecolors=fcolors, vmin=minn, vmax=maxx, shade=False)

# in order to always show the correct points on the sphere, 
# the points to be shown must be recalculated one the viewing angle changes
# when the user rotates the plot
def rotate(event):
 if event.inaxes == ax:
 plot_visible(ax.azim, ax.elev)

c1 = fig.canvas.mpl_connect('motion_notify_event', rotate)

plt.show()
</code></pre>

<a href="https://i.stack.imgur.com/u7j32.png" rel="noreferrer"><img src="https://i.stack.imgur.com/u7j32.png" alt="enter image description here"></a>

At the end one may have to play a bit with the <code>markersize</code>, <code>alpha</code> and the number of points in order to get the most visually appealing result out of this.

I want to plot data using Matplotlib via a colormap on the surface of a sphere. Additionally, I would like to add a 3D line plot. The code I have so far is this:

<pre><code>import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np


NPoints_Phi = 30
NPoints_Theta = 30

radius = 1
pi = np.pi
cos = np.cos
sin = np.sin

phi_array = ((np.linspace(0, 1, NPoints_Phi))**1) * 2*pi
theta_array = (np.linspace(0, 1, NPoints_Theta) **1) * pi


phi, theta = np.meshgrid(phi_array, theta_array) 


x_coord = radius*sin(theta)*cos(phi)
y_coord = radius*sin(theta)*sin(phi)
z_coord = radius*cos(theta)


#Make colormap the fourth dimension
color_dimension = x_coord 
minn, maxx = color_dimension.min(), color_dimension.max()
norm = matplotlib.colors.Normalize(minn, maxx)
m = plt.cm.ScalarMappable(norm=norm, cmap='jet')
m.set_array([])
fcolors = m.to_rgba(color_dimension)



theta2 = np.linspace(-np.pi, 0, 1000)
phi2 = np.linspace( 0 , 5 * 2*np.pi , 1000)


x_coord_2 = radius * np.sin(theta2) * np.cos(phi2)
y_coord_2 = radius * np.sin(theta2) * np.sin(phi2)
z_coord_2 = radius * np.cos(theta2)

# plot
fig = plt.figure()

ax = fig.gca(projection='3d')
ax.plot(x_coord_2, y_coord_2, z_coord_2,'k|-', linewidth=1 )
ax.plot_surface(x_coord,y_coord,z_coord, rstride=1, cstride=1, facecolors=fcolors, vmin=minn, vmax=maxx, shade=False)
fig.show()
</code></pre>

This code produces an image that looks like this: <a href="https://i.stack.imgur.com/medST.png" rel="noreferrer"><img src="https://i.stack.imgur.com/medST.png" alt="this"></a> which is ALMOST what I want. However, the black line should be obscured by the surface plot when it is in the background and visible when it is in the foreground. In other words, the black line should not "shine through" the sphere. 

Can this be done in Matplotlib and without the use of Mayavi?

How to obscure a line behind a surface plot in matplotlib?

我想使用Matplotlib通过球体表面的颜色映射来绘制数据。另外，我想添加一个3D线条图。到目前为止，我拥有的代码是：import matplotlibimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport numpy ...

问如何在matplotlib中遮挡曲面图后面的一条线？
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