matplotlib.ticker.LinearLocator
The matplotlib.ticker.LinearLocator class is used to determine the tick locations. At its first call the function tries to set up the number of ticks to make a nice tick partitioning. There after the interactive navigation improves as the number of ticks get fixed. The preset parameter is used to set locs based on lom, which is a dictionary mapping of vmin, vmax -> locs.
Syntax: class matplotlib.ticker.LinearLocator(numticks=None, presets=None)
Parameters:
- numticks: Number of ticks in total.
- presets: It is used to set locs based on lom, which is a dictionary mapping of vmin, vmax -> locs.
Methods of the class:
- set_params(self, numticks=None, presets=None): It is used to set parameters within this locator.
- tick_values(self, vmin, vmax): It returns the values of located ticks between the vmin and vmax.
- view_limits(self, vmin, vmax): It is used to intelligently choose the view limits.
Example 1:
Python3
import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker xGrid = np.linspace( 1 - 1e - 14 , 1 - 1e - 16 , 30 , dtype = np.longdouble) y = np.random.rand( len (xGrid)) plt.plot(xGrid, y) plt.xlim( 1 - 1e - 14 , 1 ) loc = matplotlib.ticker.LinearLocator(numticks = 5 ) plt.gca().xaxis.set_major_locator(loc) plt.show() |
Output:
Example 2:
Python3
import numpy as np import matplotlib.pyplot as plt import matplotlib.ticker as ticker # Setup a plot such that only the bottom # spine is shown def setup(ax): ax.spines[ 'right' ].set_color( 'green' ) ax.spines[ 'left' ].set_color( 'red' ) ax.yaxis.set_major_locator(ticker.NullLocator()) ax.spines[ 'top' ].set_color( 'pink' ) ax.xaxis.set_ticks_position( 'bottom' ) ax.tick_params(which = 'major' , width = 1.00 ) ax.tick_params(which = 'major' , length = 5 ) ax.tick_params(which = 'minor' , width = 0.75 ) ax.tick_params(which = 'minor' , length = 2.5 ) ax.set_xlim( 0 , 5 ) ax.set_ylim( 0 , 1 ) ax.patch.set_alpha( 0.0 ) plt.figure(figsize = ( 8 , 6 )) n = 8 ax = plt.subplot(n, 1 , 4 ) setup(ax) ax.xaxis.set_major_locator(ticker.LinearLocator( 3 )) ax.xaxis.set_minor_locator(ticker.LinearLocator( 31 )) ax.text( 0.0 , 0.1 , "LinearLocator", fontsize = 14 , transform = ax.transAxes) plt.subplots_adjust(left = 0.05 , right = 0.95 , bottom = 0.05 , top = 1.05 ) plt.show() |
Output:
Matplotlib.ticker.LinearLocator Class in Python
Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.
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