blocks|key|898020|text|只是为了详细说明我上面的评论，matplotlib的3D绘图真的不是为等值面这样复杂的东西而设计的。它的目的是为真正简单的3D绘图生成漂亮的、出版物质量的矢量输出。它不能处理复杂的3D多边形，所以即使你自己实现行进立方体来创建等值面，它也无法正确地渲染它。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|898021|但是，您可以使用mayavi+(它的mlab+API比直接使用mayavi更方便)，它使用VTK来处理和可视化多维数据。|offset|length|898022|作为一个快速示例(从mayavi画廊示例之一修改而来)：|898023|import+numpy+as+np
from+enthought.mayavi+import+mlab

x,+y,+z+=+np.ogrid[-10:10:20j,+-10:10:20j,+-10:10:20j]
s+=+np.sin(x*y*z)/(x*y*z)

src+=+mlab.pipeline.scalar_field(s)
mlab.pipeline.iso_surface(src,+contours=[s.min()%2B0.1*s.ptp(),+],+opacity=0.3)
mlab.pipeline.iso_surface(src,+contours=[s.max()-0.1*s.ptp(),+],)

mlab.show()|code-block|syntax|javascript|898024|​|898025|📷|atomic|898026|898027|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.enthought.com/mayavi/mayavi|1|https://docs.enthought.com/mayavi/mayavi/mlab.html|2|http://www.vtk.org/|3|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/47b112aac830986d37a456534125477e.png|imageAlt^0|0|8|6|0|I|8|1|19|3|2|0|0|0|0|0|1|3|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1B|8|@]|9|@$D|1C|E|1D|1|1E]|$D|1F|E|1G|1|1H]|$D|1I|E|1J|1|1K]]|A|$]]|$1|F|3|G|5|6|7|1L|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|1M|8|@]|9|@]|A|$K|L]]|$1|M|3|N|5|6|7|1N|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1O|8|@]|9|@$D|1P|E|1Q|1|1R]]|A|$]]|$1|R|3|N|5|6|7|1S|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1T|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|V|W|X|A|$Y|11]]|12|$5|V|W|X|A|$Y|13]]|14|$5|15|W|16|A|$17|18|19|-4]]]]

Just to elaborate on my comment above, matplotlib's 3D plotting really isn't intended for something as complex as isosurfaces. It's meant to produce nice, publication-quality vector output for really simple 3D plots. It can't handle complex 3D polygons, so even if implemented marching cubes yourself to create the isosurface, it wouldn't render it properly. 

However, what you can do instead is use <a href="https://docs.enthought.com/mayavi/mayavi" rel="noreferrer">mayavi</a> (it's <a href="https://docs.enthought.com/mayavi/mayavi/mlab.html" rel="noreferrer">mlab API</a> is a bit more convenient than directly using mayavi), which uses <a href="http://www.vtk.org/" rel="noreferrer">VTK</a> to process and visualize multi-dimensional data.

As a quick example (modified from one of the mayavi gallery examples):

<pre><code>import numpy as np
from enthought.mayavi import mlab

x, y, z = np.ogrid[-10:10:20j, -10:10:20j, -10:10:20j]
s = np.sin(x*y*z)/(x*y*z)

src = mlab.pipeline.scalar_field(s)
mlab.pipeline.iso_surface(src, contours=[s.min()+0.1*s.ptp(), ], opacity=0.3)
mlab.pipeline.iso_surface(src, contours=[s.max()-0.1*s.ptp(), ],)

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

<img src="https://i.stack.imgur.com/gAHwu.png" alt="enter image description here">

blocks|key|898112|text|如果您希望将绘图保存在matplotlib中(在我看来，生成出版质量的图像比mayavi容易得多)，那么可以使用marching_cubes+function+implemented+in+skimage，然后在matplotlib中使用|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|898113|mpl_toolkits.mplot3d.art3d.Poly3DCollection|code-block|syntax|javascript|898114|如上面的链接所示。Matplotlib在渲染等值面方面做得很好。这是我用一些真实的断层扫描数据做的一个例子：|898115|​|898116|📷|atomic|898117|898118|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.marching_cubes_lewiner|1|IMAGE|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/037ac1368631cc54f24fff1f75bd6e7a.png|imageAlt^0|1K|1A|0|0|0|0|0|0|1|1|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|D|3|E|5|F|7|18|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|19|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|C|$]]|$1|M|3|N|5|O|7|1B|8|@]|9|@$A|1C|B|1D|1|1E]]|C|$]]|$1|P|3|L|5|6|7|1F|8|@]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1G|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|Z|U|10|C|$11|12|13|-4]]]]

If you want to keep your plots in matplotlib (much easier to produce publication-quality images than mayavi in my opinion), then you can use the <a href="http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.marching_cubes_lewiner" rel="nofollow noreferrer">marching_cubes function implemented in skimage</a> and then plot the results in matplotlib using

<pre><code>mpl_toolkits.mplot3d.art3d.Poly3DCollection
</code></pre>

as shown in the link above. Matplotlib does a pretty good job of rendering the isosurface. Here is an example that I made of some real tomography data:

<a href="https://i.stack.imgur.com/c8Wsa.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/c8Wsa.png" alt="enter image description here"></a>

blocks|key|681486|text|补充@DanHickstein的答案，您还可以使用trisurf来可视化在行进立方体阶段中获得的多边形。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|681487|import+numpy+as+np
from+numpy+import+sin,+cos,+pi
from+skimage+import+measure
import+matplotlib.pyplot+as+plt
from+mpl_toolkits.mplot3d+import+Axes3D
+++
+++
def+fun(x,+y,+z):
++++return+cos(x)+%2B+cos(y)+%2B+cos(z)
++++
x,+y,+z+=+pi*np.mgrid[-1:1:31j,+-1:1:31j,+-1:1:31j]
vol+=+fun(x,+y,+z)
iso_val=0.0
verts,+faces+=+measure.marching_cubes(vol,+iso_val,+spacing=(0.1,+0.1,+0.1))

fig+=+plt.figure()
ax+=+fig.add_subplot(111,+projection='3d')
ax.plot_trisurf(verts[:,+0],+verts[:,1],+faces,+verts[:,+2],
++++++++++++++++cmap='Spectral',+lw=1)
plt.show()|code-block|syntax|javascript|681488|​|681489|📷|atomic|681490|681491|更新:+2018年5月11日|681492|正如@DrBwts所提到的，现在marching_cubes返回4个值。下面的代码可以工作。|681493|import+numpy+as+np
from+numpy+import+sin,+cos,+pi
from+skimage+import+measure
import+matplotlib.pyplot+as+plt
from+mpl_toolkits.mplot3d+import+Axes3D


def+fun(x,+y,+z):
++++return+cos(x)+%2B+cos(y)+%2B+cos(z)

x,+y,+z+=+pi*np.mgrid[-1:1:31j,+-1:1:31j,+-1:1:31j]
vol+=+fun(x,+y,+z)
iso_val=0.0
verts,+faces,+_,+_+=+measure.marching_cubes(vol,+iso_val,+spacing=(0.1,+0.1,+0.1))

fig+=+plt.figure()
ax+=+fig.add_subplot(111,+projection='3d')
ax.plot_trisurf(verts[:,+0],+verts[:,1],+faces,+verts[:,+2],
++++++++++++++++cmap='Spectral',+lw=1)
plt.show()|681494|更新时间:+2020年2月2日|681495|除了我之前的回答之外，我还应该提到，从那时起，PyVista已经发布了，它使得这类任务变得比较容易。|681496|遵循与前面相同的示例。|681497|from+numpy+import+cos,+pi,+mgrid
import+pyvista+as+pv

#%25%25+Data
x,+y,+z+=+pi*mgrid[-1:1:31j,+-1:1:31j,+-1:1:31j]
vol+=+cos(x)+%2B+cos(y)+%2B+cos(z)
grid+=+pv.StructuredGrid(x,+y,+z)
grid["vol"]+=+vol.flatten()
contours+=+grid.contour([0])

#%25%25+Visualization
pv.set_plot_theme('document')
p+=+pv.Plotter()
p.add_mesh(contours,+scalars=contours.points[:,+2],+show_scalar_bar=False)
p.show()|681498|结果如下所示|681499|681500|681501|681502|更新时间:+2020年2月24日|681503|正如@HenriMenke所提到的，marching_cubes已被重命名为marching_cubes_lewiner。“新”代码片段如下所示。|681504|import+numpy+as+np
from+numpy+import+cos,+pi
from+skimage.measure+import+marching_cubes_lewiner
import+matplotlib.pyplot+as+plt
from+mpl_toolkits.mplot3d+import+Axes3D

x,+y,+z+=+pi*np.mgrid[-1:1:31j,+-1:1:31j,+-1:1:31j]
vol+=+cos(x)+%2B+cos(y)+%2B+cos(z)
iso_val=0.0
verts,+faces,+_,+_+=+marching_cubes_lewiner(vol,+iso_val,+spacing=(0.1,+0.1,+0.1))

fig+=+plt.figure()
ax+=+fig.add_subplot(111,+projection='3d')
ax.plot_trisurf(verts[:,+0],+verts[:,1],+faces,+verts[:,+2],+cmap='Spectral',
++++++++++++++++lw=1)
plt.show()|681505|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/02e8808347e4f7d6949b6d6ae2b9da18.png|imageAlt|1|LINK|MUTABLE|url|https://docs.pyvista.org/index.html|2|https://ask.qcloudimg.com/http-save/yehe-900000/437428c7bf6dab63e699465c0c90e308.png^0|P|7|0|0|0|0|1|0|0|0|0|0|0|0|N|7|1|0|0|0|0|0|0|1|2|0|0|0|I|E|12|M|0|0^^$0|@$1|2|3|4|5|6|7|1V|8|@$9|1W|A|1X|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1Y|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|20|8|@]|D|@$9|21|A|22|1|23]]|E|$]]|$1|P|3|L|5|6|7|24|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|25|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|26|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|27|8|@]|D|@]|E|$I|J]]|$1|W|3|X|5|6|7|28|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|6|7|29|8|@]|D|@$9|2A|A|2B|1|2C]]|E|$]]|$1|10|3|11|5|6|7|2D|8|@]|D|@]|E|$]]|$1|12|3|13|5|H|7|2E|8|@]|D|@]|E|$I|J]]|$1|14|3|15|5|6|7|2F|8|@]|D|@]|E|$]]|$1|16|3|L|5|6|7|2G|8|@]|D|@]|E|$]]|$1|17|3|N|5|O|7|2H|8|@]|D|@$9|2I|A|2J|1|2K]]|E|$]]|$1|18|3|L|5|6|7|2L|8|@]|D|@]|E|$]]|$1|19|3|1A|5|6|7|2M|8|@]|D|@]|E|$]]|$1|1B|3|1C|5|6|7|2N|8|@$9|2O|A|2P|B|C]|$9|2Q|A|2R|B|C]]|D|@]|E|$]]|$1|1D|3|1E|5|H|7|2S|8|@]|D|@]|E|$I|J]]|$1|1F|3|-4|5|6|7|2T|8|@]|D|@]|E|$]]]|1G|$1H|$5|1I|1J|1K|E|$1L|1M|1N|-4]]|1O|$5|1P|1J|1Q|E|$1R|1S]]|1T|$5|1I|1J|1K|E|$1L|1U|1N|-4]]]]

Complementing the answer of @DanHickstein, you can also use <code>trisurf</code> to visualize the polygons obtained in the marching cubes phase.
<pre><code>import numpy as np
from numpy import sin, cos, pi
from skimage import measure
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
 
def fun(x, y, z):
 return cos(x) + cos(y) + cos(z)
 
x, y, z = pi*np.mgrid[-1:1:31j, -1:1:31j, -1:1:31j]
vol = fun(x, y, z)
iso_val=0.0
verts, faces = measure.marching_cubes(vol, iso_val, spacing=(0.1, 0.1, 0.1))

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(verts[:, 0], verts[:,1], faces, verts[:, 2],
 cmap='Spectral', lw=1)
plt.show()
</code></pre>
<a href="https://i.stack.imgur.com/oTKQk.png" rel="noreferrer"><img src="https://i.stack.imgur.com/oTKQk.png" alt="enter image description here" /></a>
<h3>Update: May 11, 2018</h3>
As mentioned by @DrBwts, now marching_cubes return 4 values. The following code works.
<pre><code>import numpy as np
from numpy import sin, cos, pi
from skimage import measure
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D


def fun(x, y, z):
 return cos(x) + cos(y) + cos(z)

x, y, z = pi*np.mgrid[-1:1:31j, -1:1:31j, -1:1:31j]
vol = fun(x, y, z)
iso_val=0.0
verts, faces, _, _ = measure.marching_cubes(vol, iso_val, spacing=(0.1, 0.1, 0.1))

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(verts[:, 0], verts[:,1], faces, verts[:, 2],
 cmap='Spectral', lw=1)
plt.show()
</code></pre>
<h3>Update: February 2, 2020</h3>
Adding to my previous answer, I should mention that since then <a href="https://docs.pyvista.org/index.html" rel="noreferrer">PyVista</a> has been released, and it makes this
kind of tasks somewhat effortless.
Following the same example as before.
<pre class="lang-py prettyprint-override"><code>from numpy import cos, pi, mgrid
import pyvista as pv

#%% Data
x, y, z = pi*mgrid[-1:1:31j, -1:1:31j, -1:1:31j]
vol = cos(x) + cos(y) + cos(z)
grid = pv.StructuredGrid(x, y, z)
grid[&quot;vol&quot;] = vol.flatten()
contours = grid.contour([0])

#%% Visualization
pv.set_plot_theme('document')
p = pv.Plotter()
p.add_mesh(contours, scalars=contours.points[:, 2], show_scalar_bar=False)
p.show()
</code></pre>
With the following result
<a href="https://i.stack.imgur.com/jhBfJ.png" rel="noreferrer"><img src="https://i.stack.imgur.com/jhBfJ.png" alt="enter image description here" /></a>
<h3>Update: February 24, 2020</h3>
As mentioned by @HenriMenke, <code>marching_cubes</code> has been renamed to <code>marching_cubes_lewiner</code>. The &quot;new&quot; snippet is the following.
<pre class="lang-py prettyprint-override"><code>import numpy as np
from numpy import cos, pi
from skimage.measure import marching_cubes_lewiner
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

x, y, z = pi*np.mgrid[-1:1:31j, -1:1:31j, -1:1:31j]
vol = cos(x) + cos(y) + cos(z)
iso_val=0.0
verts, faces, _, _ = marching_cubes_lewiner(vol, iso_val, spacing=(0.1, 0.1, 0.1))

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(verts[:, 0], verts[:,1], faces, verts[:, 2], cmap='Spectral',
 lw=1)
plt.show()
</code></pre>

I have a 3-dimensional numpy array. I'd like to display (in matplotlib) a nice 3D plot of an isosurface of this array (or more strictly, display an isosurface of the 3D scalar field defined by interpolating between the sample points).

matplotlib's mplot3D part provides nice 3D plot support, but (so far as I can see) its API doesn't have anything which will simply take a 3D array of scalar values and display an isosurface. However, it does support displaying a collection of polygons, so presumably I could implement the marching cubes algorithm to generate such polygons.

It does seem quite likely that a scipy-friendly marching cubes has already been implemented somewhere and that I haven't found it, or that I'm missing some easy way of doing this. Alternatively I'd welcome any pointers to other tools for visualising 3D array data easily usable from the Python/numpy/scipy world.

How to display a 3D plot of a 3D array isosurface in matplotlib mplot3D or similar?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

我有一个三维的numpy数组。我想要显示(在matplotlib中)这个数组的等值面的一个漂亮的3D图(或者更严格地说，显示一个通过在采样点之间插值定义的3D标量场的等值面)。matplotlib的mplot3D部件提供了很好的3D绘图支持，但(据我所知)它的API没有任何东西可以简单地获取标量值的3D数组并显示等值面...

问如何在matplotlib mplot3D或类似工具中显示3D数组等值面的3D绘图？
EN

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何在matplotlib mplot3D或类似工具中显示3D数组等值面的3D绘图？EN

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何在matplotlib mplot3D或类似工具中显示3D数组等值面的3D绘图？
EN