更改子图大小以正确显示椭圆

Question

我正在尝试绘制均匀分布点的置信椭圆。在使用 Matplotlib 绘制椭圆和散点图时，我发现部分椭圆被子图剪掉了。鉴于 here, here, here and here，我尝试在 SO 上实施此处建议的其他解决方案。但无法更正显示的图。

如何更改此子图的大小以正确且完整地显示椭圆？

生成椭圆的代码：multidimensional confidence intervals

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

def eigsorted(cov):
    vals, vecs = np.linalg.eigh(cov)
    order = vals.argsort()[::-1]
    return vals[order], vecs[:,order]

nstd = 2

fig = plt.figure(1, figsize=(12, 5))
#ax = plt.subplots(111)
ax = fig.add_subplot(111, aspect='equal')
test1 = np.random.uniform(40, 60, 1000)
test2 = np.random.uniform(100, 120, 1000)
cov = np.cov(test1, test2)
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
w, h = 2 * nstd * np.sqrt(vals)
ell = Ellipse(xy=(np.mean(test1), np.mean(test2)),
              width=w, height=h,
              angle=theta, color='black')
ell.set_facecolor('none')
ax.add_artist(ell)
plt.scatter(test1, test2)
plt.show()

Answer 1

正如@ImportanceOfBeingEarnest 正确评论的那样，需要添加补丁才能正确显示椭圆。更新下面的代码以供将来其他人参考。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

def eigsorted(cov):
    vals, vecs = np.linalg.eigh(cov)
    order = vals.argsort()[::-1]
    return vals[order], vecs[:,order]

nstd = 2

fig = plt.figure(1, figsize=(12, 5))
#ax = plt.subplots(111)
ax = fig.add_subplot(111, aspect='equal')
test1 = np.random.uniform(40, 60, 1000)
test2 = np.random.uniform(100, 120, 1000)
cov = np.cov(test1, test2)
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
w, h = 2 * nstd * np.sqrt(vals)
ell = Ellipse(xy=(np.mean(test1), np.mean(test2)),
              width=w, height=h,
              angle=theta, color='black')
ell.set_facecolor('none')

#Corrected code for displaying ellipse correctly
ax.add_patch(ell)

plt.scatter(test1, test2)
plt.show()

更改子图大小以正确显示椭圆

Changing subplot size for displaying ellipse correctly

python

matplotlib

ellipse