令人惊讶的是,我没有找到关于如何使用matplotlib.pyplot(请不要使用pylab)绘制圆作为输入中心(x,y)和半径r的简单描述。我尝试了一些变体:
import matplotlib.pyplot as plt
circle=plt.Circle((0,0),2)
plt.show()
…但是仍然无法正常工作。
surprisingly I didn’t find a straight-forward description on how to draw a circle with matplotlib.pyplot (please no pylab) taking as input center (x,y) and radius r. I tried some variants of this:
import matplotlib.pyplot as plt
circle=plt.Circle((0,0),2)
plt.show()
… but still didn’t get it working.
您需要将其添加到轴。A Circle
是的子类Artist
,并且axes
具有add_artist
方法。
这是执行此操作的示例:
import matplotlib.pyplot as plt
circle1 = plt.Circle((0, 0), 0.2, color='r')
circle2 = plt.Circle((0.5, 0.5), 0.2, color='blue')
circle3 = plt.Circle((1, 1), 0.2, color='g', clip_on=False)
fig, ax = plt.subplots()
ax.add_artist(circle1)
ax.add_artist(circle2)
ax.add_artist(circle3)
fig.savefig('plotcircles.png')
结果如下图:

第一个圆是原点,但默认情况下clip_on
是True
,因此,只要圆超出,就会对其进行裁剪axes
。第三个(绿色)圆圈显示了不剪切时会发生的情况Artist
。它超出轴(但不超出图形,即图形大小不会自动调整以绘制所有艺术家)。
默认情况下,x,y和半径的单位对应于数据单位。在这种情况下,我没有在轴上绘制任何内容(fig.gca()
返回当前轴),并且由于从未设置极限,因此它们的默认x和y范围为0到1。
这是该示例的继续,显示了单位的重要性:
circle1 = plt.Circle((0, 0), 2, color='r')
circle2 = plt.Circle((5, 5), 0.5, color='b', fill=False)
circle3 = plt.Circle((10, 10), 2, color='g', clip_on=False)
ax = plt.gca()
ax.cla()
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
ax.plot(range(11), 'o', color='black')
ax.plot((5), (5), 'o', color='y')
ax.add_artist(circle1)
ax.add_artist(circle2)
ax.add_artist(circle3)
fig.savefig('plotcircles2.png')
结果是:

您会看到如何将第二个圆的填充设置为False
,这对于环绕关键结果(例如我的黄色数据点)很有用。
You need to add it to an axes. A Circle
is a subclass of an Artist
, and an axes
has an add_artist
method.
Here’s an example of doing this:
import matplotlib.pyplot as plt
circle1 = plt.Circle((0, 0), 0.2, color='r')
circle2 = plt.Circle((0.5, 0.5), 0.2, color='blue')
circle3 = plt.Circle((1, 1), 0.2, color='g', clip_on=False)
fig, ax = plt.subplots()
ax.add_artist(circle1)
ax.add_artist(circle2)
ax.add_artist(circle3)
fig.savefig('plotcircles.png')
This results in the following figure:

The first circle is at the origin, but by default clip_on
is True
, so the circle is clipped when ever it extends beyond the axes
. The third (green) circle shows what happens when you don’t clip the Artist
. It extends beyond the axes (but not beyond the figure, ie the figure size is not automatically adjusted to plot all of your artists).
The units for x, y and radius correspond to data units by default. In this case, I didn’t plot anything on my axes (fig.gca()
returns the current axes), and since the limits have never been set, they defaults to an x and y range from 0 to 1.
Here’s a continuation of the example, showing how units matter:
circle1 = plt.Circle((0, 0), 2, color='r')
circle2 = plt.Circle((5, 5), 0.5, color='b', fill=False)
circle3 = plt.Circle((10, 10), 2, color='g', clip_on=False)
ax = plt.gca()
ax.cla()
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
ax.plot(range(11), 'o', color='black')
ax.plot((5), (5), 'o', color='y')
ax.add_artist(circle1)
ax.add_artist(circle2)
ax.add_artist(circle3)
fig.savefig('plotcircles2.png')
which results in:

You can see how I set the fill of the 2nd circle to False
, which is useful for encircling key results (like my yellow data point).
import matplotlib.pyplot as plt
circle1=plt.Circle((0,0),.2,color='r')
plt.gcf().gca().add_artist(circle1)
快速精简版已接受答案,可将圆快速插入现有图中。请参阅接受的答案和其他答案以了解详细信息。
顺便说说:
gcf()
表示获取当前图形
gca()
表示获取当前轴
import matplotlib.pyplot as plt
circle1=plt.Circle((0,0),.2,color='r')
plt.gcf().gca().add_artist(circle1)
A quick condensed version of the accepted answer, to quickly plug a circle into an existing plot. Refer to the accepted answer and other answers to understand the details.
By the way:
gcf()
means Get Current Figure
gca()
means Get Current Axis
如果要绘制一组圆,则可能需要查看此帖子或要点(较新)。该帖子提供了一个名为的功能circles
。
该函数的circles
工作方式类似于scatter
,但是绘制圆的大小以数据单位表示。
这是一个例子:
from pylab import *
figure(figsize=(8,8))
ax=subplot(aspect='equal')
circles(1, 0, 0.5, 'r', alpha=0.2, lw=5, edgecolor='b', transform=ax.transAxes)
a=arange(11)
out = circles(a, a, a*0.2, c=a, alpha=0.5, edgecolor='none')
colorbar(out)
xlim(0,10)
ylim(0,10)

If you want to plot a set of circles, you might want to see this post or this gist(a bit newer). The post offered a function named circles
.
The function circles
works like scatter
, but the sizes of plotted circles are in data unit.
Here’s an example:
from pylab import *
figure(figsize=(8,8))
ax=subplot(aspect='equal')
#plot one circle (the biggest one on bottom-right)
circles(1, 0, 0.5, 'r', alpha=0.2, lw=5, edgecolor='b', transform=ax.transAxes)
#plot a set of circles (circles in diagonal)
a=arange(11)
out = circles(a, a, a*0.2, c=a, alpha=0.5, edgecolor='none')
colorbar(out)
xlim(0,10)
ylim(0,10)

import matplotlib.pyplot as plt
import numpy as np
def xy(r,phi):
return r*np.cos(phi), r*np.sin(phi)
fig = plt.figure()
ax = fig.add_subplot(111,aspect='equal')
phis=np.arange(0,6.28,0.01)
r =1.
ax.plot( *xy(r,phis), c='r',ls='-' )
plt.show()
或者,如果你愿意的话,看看path
S,http://matplotlib.sourceforge.net/users/path_tutorial.html
#!/usr/bin/python
import matplotlib.pyplot as plt
import numpy as np
def xy(r,phi):
return r*np.cos(phi), r*np.sin(phi)
fig = plt.figure()
ax = fig.add_subplot(111,aspect='equal')
phis=np.arange(0,6.28,0.01)
r =1.
ax.plot( *xy(r,phis), c='r',ls='-' )
plt.show()
Or, if you prefer, look at the path
s, http://matplotlib.sourceforge.net/users/path_tutorial.html
如果无论数据坐标是什么,都希望“圆”的视觉长宽比保持为1,则可以使用scatter()方法。http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.scatter
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]
r = [100, 80, 60, 40, 20]
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y, s=r)
fig.show()

If you aim to have the “circle” maintain a visual aspect ratio of 1 no matter what the data coordinates are, you could use the scatter() method. http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.scatter
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 40, 50]
r = [100, 80, 60, 40, 20]
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y, s=r)
fig.show()

将常见问题扩展为可接受的答案。特别是:
以自然的长宽比查看圈子。
自动扩展轴限制以包括新绘制的圆。
独立的示例:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.add_patch(plt.Circle((0, 0), 0.2, color='r', alpha=0.5))
ax.add_patch(plt.Circle((1, 1), 0.5, color='#00ffff', alpha=0.5))
ax.add_artist(plt.Circle((1, 0), 0.5, color='#000033', alpha=0.5))
ax.set_aspect('equal', adjustable='datalim')
ax.plot()
plt.show()
注意两者之间ax.add_patch(..)
和的区别ax.add_artist(..)
:只有前者使自动缩放机制考虑了这个圆(参考:Discussion),因此在运行上面的代码后,我们得到:

另请参阅:set_aspect(..)
文档。
Extending the accepted answer for a common usecase. In particular:
View the circles at a natural aspect ratio.
Automatically extend the axes limits to include the newly plotted circles.
Self-contained example:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.add_patch(plt.Circle((0, 0), 0.2, color='r', alpha=0.5))
ax.add_patch(plt.Circle((1, 1), 0.5, color='#00ffff', alpha=0.5))
ax.add_artist(plt.Circle((1, 0), 0.5, color='#000033', alpha=0.5))
ax.set_aspect('equal', adjustable='datalim')
ax.plot()
plt.show()
Note the difference between ax.add_patch(..)
and ax.add_artist(..)
: of the two, only the former makes autoscaling machinery take the circle into account (reference: discussion), so after running the above code we get:

See also: set_aspect(..)
documentation.
我看到使用(.circle)的图,但是根据您可能想做的事情,您也可以尝试以下操作:
import matplotlib.pyplot as plt
import numpy as np
x = list(range(1,6))
y = list(range(10, 20, 2))
print(x, y)
for i, data in enumerate(zip(x,y)):
j, k = data
plt.scatter(j,k, marker = "o", s = ((i+1)**4)*50, alpha = 0.3)

centers = np.array([[5,18], [3,14], [7,6]])
m, n = make_blobs(n_samples=20, centers=[[5,18], [3,14], [7,6]], n_features=2,
cluster_std = 0.4)
colors = ['g', 'b', 'r', 'm']
plt.figure(num=None, figsize=(7,6), facecolor='w', edgecolor='k')
plt.scatter(m[:,0], m[:,1])
for i in range(len(centers)):
plt.scatter(centers[i,0], centers[i,1], color = colors[i], marker = 'o', s = 13000, alpha = 0.2)
plt.scatter(centers[i,0], centers[i,1], color = 'k', marker = 'x', s = 50)
plt.savefig('plot.png')

I see plots with the use of (.circle) but based on what you might want to do you can also try this out:
import matplotlib.pyplot as plt
import numpy as np
x = list(range(1,6))
y = list(range(10, 20, 2))
print(x, y)
for i, data in enumerate(zip(x,y)):
j, k = data
plt.scatter(j,k, marker = "o", s = ((i+1)**4)*50, alpha = 0.3)

centers = np.array([[5,18], [3,14], [7,6]])
m, n = make_blobs(n_samples=20, centers=[[5,18], [3,14], [7,6]], n_features=2,
cluster_std = 0.4)
colors = ['g', 'b', 'r', 'm']
plt.figure(num=None, figsize=(7,6), facecolor='w', edgecolor='k')
plt.scatter(m[:,0], m[:,1])
for i in range(len(centers)):
plt.scatter(centers[i,0], centers[i,1], color = colors[i], marker = 'o', s = 13000, alpha = 0.2)
plt.scatter(centers[i,0], centers[i,1], color = 'k', marker = 'x', s = 50)
plt.savefig('plot.png')

您好,我已经写了一个画圆的代码。这将有助于绘制各种圆圈。
该图显示了半径为1且圆心为0,0
的圆。可以选择任意中心和半径。
import matplotlib.pyplot as plt
import numpy as np
x1 = -1.5
x2 = 1.5
y1 = -1.5
y2 = 1.5
circle1 = plt.Circle((0,0),1, color = 'k', fill = False, clip_on = False)
fig, ax = plt.subplots()
ax.add_artist(circle1)
plt.axis("equal")
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.xlim(left=x1)
plt.xlim(right=x2)
plt.ylim(bottom=y1)
plt.ylim(top=y2)
plt.axhline(linewidth=2, color='k')
plt.axvline(linewidth=2, color='k')
plt.grid(color='k', linestyle='-.', linewidth=0.5)
plt.show()
祝好运
Hello I have written a code for drawing a circle.
It will help for drawing all kind of circles.
The image shows the circle with radius 1 and center at 0,0
The center and radius can be edited of any choice.
import matplotlib.pyplot as plt
import numpy as np
x1 = -1.5
x2 = 1.5
y1 = -1.5
y2 = 1.5
circle1 = plt.Circle((0,0),1, color = 'k', fill = False, clip_on = False)
fig, ax = plt.subplots()
ax.add_artist(circle1)
plt.axis("equal")
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.xlim(left=x1)
plt.xlim(right=x2)
plt.ylim(bottom=y1)
plt.ylim(top=y2)
plt.axhline(linewidth=2, color='k')
plt.axvline(linewidth=2, color='k')
plt.grid(color='k', linestyle='-.', linewidth=0.5)
plt.show()
Good luck
与散点图类似,您也可以使用具有圆线样式的法线图。使用markersize
参数可以调整圆的半径:
import matplotlib.pyplot as plt
plt.plot(200, 2, 'o', markersize=7)
Similarly to scatter plot you can also use normal plot with circle line style. Using markersize
parameter you can adjust radius of a circle:
import matplotlib.pyplot as plt
plt.plot(200, 2, 'o', markersize=7)
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