Python 中的 matplotlib . axes . axes . contains _ point()
原文:https://www . geeksforgeeks . org/matplotlib-axes-axes-contains _ point-in-python/
Matplotlib 是 Python 中的一个库，是 NumPy 库的数值-数学扩展。轴类包含了大部分的图形元素:轴、刻度、线二维、文本、多边形等。，并设置坐标系。Axes 的实例通过回调属性支持回调。
matplotlib . axes . axes . contains _ point()函数
matplotlib 库的 Axes 模块中的 Axes.contains_point()函数用于返回点(像素坐标对)是否在 axes 面片内。
语法: Axes.contains_point(自身，点)
参数:该方法只接受一个参数。
- 点:该参数为像素坐标对。
返回:该方法返回布尔值。
下面的例子说明了 matplotlib.axes . axes . contains _ point()函数在 matplotlib . axes 中的作用:
例 1:
# Implementation of matplotlib function
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
from matplotlib.path import Path
from matplotlib.patches import PathPatch
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
path = Path([[0, 1], [1, 0], [0, -1],
[-1, 0], [0, 1]])
patch = PathPatch(path, facecolor ='none')
fig, ax = plt.subplots()
ax.add_patch(patch)
im = ax.imshow(Z,
interpolation ='bilinear',
cmap = cm.gray,
origin ='lower',
extent =[-3, 3, -3, 3],
clip_path = patch,
clip_on = True)
im.set_clip_path(patch)
ax.text(-1.2, 2,
"Value Return : "+ str(path.contains_point([0, 0])),
fontweight ="bold",
fontsize = 10)
fig.suptitle('matplotlib.axes.Axes.contains_point() \
function Example', fontweight ="bold")
plt.show()
输出:
例 2:
# Implementation of matplotlib function
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
verts = [(0., 0.), (0., 1.),
(1., 1.), (1., 0.),
(0., 0.)]
codes = [Path.MOVETO, Path.LINETO,
Path.LINETO, Path.LINETO, Path.CLOSEPOLY]
path = Path(verts, codes)
fig, ax = plt.subplots()
patch = patches.PathPatch(path, facecolor ='green', lw = 2)
ax.add_patch(patch)
ax.set_xlim(-0.5, 2)
ax.set_ylim(-0.5, 2)
ax.text(0, 1.2,
"Value Return : "+ str(path.contains_point([5, 5])),
fontweight ="bold",
fontsize = 10)
fig.suptitle('matplotlib.axes.Axes.contains_point()\
function Example', fontweight ="bold")
plt.show()
输出:
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