Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. It is an amazing visualization library in Python for 2D plots of arrays and used for working with the broader SciPy stack.
Matplotlib.axis.Tick.set_rasterized() Function
The Tick.set_rasterized() function in axis module of matplotlib library is used to force rasterized (bitmap) drawing in vector backend output.
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.
Below examples illustrate the matplotlib.axis.Tick.set_rasterized() function in matplotlib.axis:
Example 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() |
Output:
Example 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() |
Output:
