用 pyplot 绘制一个圆圈 [英] plot a circle with pyplot

问题描述

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)以输入中心 (x,y) 和半径 r 绘制圆的直接描述.我尝试了一些变体:

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)
# here must be something like circle.plot() or not?
plt.show()

...但仍然没有让它工作.

... but still didn't get it working.

推荐答案

您需要将其添加到坐标区.Circle 是一个Patch 的子类，并且一个 axes 有一个 add_patch 方法.(您也可以使用 add_artist 但不推荐.)

You need to add it to an axes. A Circle is a subclass of an Patch, and an axes has an add_patch method. (You can also use add_artist but it's not recommended.)

以下是执行此操作的示例:

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() # note we must use plt.subplots, not plt.subplot
# (or if you have an existing figure)
# fig = plt.gcf()
# ax = fig.gca()

ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)

fig.savefig('plotcircles.png')

结果如下图:

第一个圆位于原点，但默认情况下 clip_on 为 True，因此当圆超出 轴.第三个(绿色)圆圈显示当您不剪辑 Artist 时会发生什么.它超出轴(但不超出图形，即图形大小不会自动调整以绘制您的所有艺术家).

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).

x、y 和半径的单位默认对应于数据单位.在这种情况下，我没有在我的轴上绘制任何内容(fig.gca() 返回当前轴)，并且由于从未设置过限制，它们默认为 x 和 y 范围从0 到 1.

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.

下面是这个例子的延续，展示了单位的重要性:

Here's a continuation of the example, showing how units matter:

circle1 = plt.Circle((0, 0), 2, color='r')
# now make a circle with no fill, which is good for hi-lighting key results
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() # clear things for fresh plot

# change default range so that new circles will work
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
# some data
ax.plot(range(11), 'o', color='black')
# key data point that we are encircling
ax.plot((5), (5), 'o', color='y')
    
ax.add_patch(circle1)
ax.add_patch(circle2)
ax.add_patch(circle3)
fig.savefig('plotcircles2.png')

导致:

您可以看到我如何将第二个圆圈的填充设置为 False，这对于包围关键结果(例如我的黄色数据点)很有用.

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).

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