Python中的 Matplotlib.axes.Axes.set_gid()
Matplotlib是Python中的一个库,它是 NumPy 库的数值数学扩展。 Axes 类包含大部分图形元素:Axis、Tick、Line2D、Text、Polygon 等,并设置坐标系。 Axes 的实例通过回调属性支持回调。
matplotlib.axes.Axes.set_gid()函数
matplotlib 库的 axes 模块中的Axes.set_gid()函数用于设置艺术家的(组)ID。
Syntax: Axes.set_gid(self, gid)
Parameters: This method accepts only one parameters.
- gid: This parameter is the string given as gid.
Returns: This method does not return any value.
下面的示例说明了 matplotlib.axes 中的 matplotlib.axes.Axes.set_gid()函数:
示例 1:
# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
y, x = np.mgrid[:5, 1:6]
poly_coords = [
(0.25, 2.75), (3.25, 2.75),
(2.25, 0.75), (0.25, 0.75)
]
fig, ax = plt.subplots()
cells = ax.plot(x, y, x + y, color ='green')
ax.add_patch(
plt.Polygon(poly_coords,
color ='forestgreen',
alpha = 0.5)
)
ax.margins(x = 0.1, y = 0.05)
ax.set_aspect('equal')
for i, t in enumerate(ax.patches):
t.set_gid('patch_% d' % i)
fig.suptitle('matplotlib.axes.Axes.set_gid() \
function Example\n\n', fontweight ="bold")
plt.show()
输出:
示例 2:
# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
circle = plt.Circle((0, 0), 5, fc ='blue')
rect = plt.Rectangle((-5, 10), 10, 5, fc ='green')
ax.add_patch(circle)
ax.add_patch(rect)
circle_tip = ax.annotate('This is a blue circle.',
xy =(0, 0),
xytext =(30, -30),
textcoords ='offset points',
color ='w',
ha ='left',
bbox = dict(boxstyle ='round, pad =.5',
fc =(.1, .1, .1, .92),
ec =(1., 1., 1.),
lw = 1,
zorder = 1),
)
rect_tip = ax.annotate('This is a green rectangle.',
xy =(-5, 10),
xytext =(30, 40),
textcoords ='offset points',
color ='w',
ha ='left',
bbox = dict(boxstyle ='round, pad =.5',
fc =(.1, .1, .1, .92),
ec =(1., 1., 1.),
lw = 1,
zorder = 1),
)
for i, t in enumerate(ax.patches):
t.set_gid('patch_% d'% i)
for i, t in enumerate(ax.texts):
t.set_gid('tooltip_% d'% i)
ax.set_xlim(-30, 30)
ax.set_ylim(-30, 30)
ax.set_aspect('equal')
fig.suptitle('matplotlib.axes.Axes.set_gid() function \
Example\n\n', fontweight ="bold")
plt.show()
输出: