Python中的 Matplotlib.axis.Tick.set_rasterized()函数
Matplotlib是Python中的一个库,它是 NumPy 库的数值数学扩展。它是Python中用于二维数组图的惊人可视化库,用于处理更广泛的 SciPy 堆栈。
Matplotlib.axis.Tick.set_rasterized()函数
matplotlib 库的轴模块中的Tick.set_rasterized()函数用于强制在矢量后端输出中进行光栅化(位图)绘图。
Syntax: Tick.set_rasterized(self, rasterized)
Parameters: This method accepts the following parameters.
- rasterized: This parameter is the boolean value.
Return value: This method does not return any value.
以下示例说明了 matplotlib.axis 中的 matplotlib.axis.Tick.set_rasterized()函数:
示例 1:
Python3
# Implementation of matplotlib function
from matplotlib.axis import Tick
import numpy as np
import matplotlib.pyplot as plt
d = np.arange(16).reshape(4,4)
xx, yy = np.meshgrid(np.arange(5), np.arange(5))
fig, ax = plt.subplots()
ax.set_aspect(1)
m = ax.pcolormesh(xx, yy, d)
Tick.set_rasterized(m, True)
fig.suptitle('matplotlib.axis.Tick.set_rasterized() \
function Example', fontweight ="bold")
plt.show()
Python3
# Implementation of matplotlib function
from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.gridspec as gridspec
import numpy as np
arr = np.arange(20).reshape((4, 5))
norm = mcolors.Normalize(vmin = 0., vmax = 20.)
pc_kwargs = {'cmap': 'BuGn', 'norm': norm}
fig, ax = plt.subplots( )
im = ax.pcolormesh(arr, **pc_kwargs)
fig.colorbar(im, ax = ax, shrink = 0.7)
Tick.set_rasterized(im, False)
fig.suptitle('matplotlib.axis.Tick.set_rasterized() \
function Example', fontweight ="bold")
plt.show()
输出:
示例 2:
Python3
# Implementation of matplotlib function
from matplotlib.axis import Tick
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.gridspec as gridspec
import numpy as np
arr = np.arange(20).reshape((4, 5))
norm = mcolors.Normalize(vmin = 0., vmax = 20.)
pc_kwargs = {'cmap': 'BuGn', 'norm': norm}
fig, ax = plt.subplots( )
im = ax.pcolormesh(arr, **pc_kwargs)
fig.colorbar(im, ax = ax, shrink = 0.7)
Tick.set_rasterized(im, False)
fig.suptitle('matplotlib.axis.Tick.set_rasterized() \
function Example', fontweight ="bold")
plt.show()
输出: