Matplotlib.ticker.AutoLocator Class in Python

0 0 1 minute read

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.

matplotlib.ticker.AutoLocator

The matplotlib.ticker.AutoLocator class is a subclass of matplotlib.ticker.MaxNLocator, and has parameters nbins = ‘auto’ and steps = [1, 2, 2.5, 5, 10]. It is used to dynamically find major tick positions.

Syntax:class matplotlib.ticker.AutoLocator
Parameters:

nbins: It is either an integer or ‘auto’, where the integer value represents the maximum number of intervals; one less than max number of ticks. The number of bins gets automatically determined on the basis of the length of the axis.It is an optional argument and has a default value of 10.

steps: It is an optional parameter representing a nice number sequence that starts from 1 and ends with 10.

integer: It is an optional boolean value. If set True, the ticks accepts only integer values, provided at least min_n_ticks integers are within the view limits.

symmetric: It is an optional value. If set to True, auto-scaling will result in a range symmetric about zero.

prune: It is an optional parameter that accepts either of the four values: {‘lower’, ‘upper’, ‘both’, None}. By default it is None.

Example 1:

Python3

import matplotlib
import matplotlib.pyplot as plt
import numpy as np
 
 
fig, axes = plt.subplots(3, 4,
                         sharex = 'row',
                         sharey = 'row',
                         squeeze = False)
 
data = np.random.rand(20, 2, 10)
 
for ax in axes.flatten()[:-1]:
     
    ax.plot(*np.random.randn(2, 10), marker ="o", ls ="")
 
 
 
# Now remove axes[1, 5] from 
# the grouper for xaxis
axes[2, 3].get_shared_x_axes().remove(axes[2, 3])
 
# Create and assign new ticker
xticker = matplotlib.axis.Ticker()
axes[2, 3].xaxis.major = xticker
 
# The new ticker needs new locator 
# and formatters
xloc = matplotlib.ticker.AutoLocator()
xfmt = matplotlib.ticker.ScalarFormatter()
 
axes[2, 3].xaxis.set_major_locator(xloc)
axes[2, 3].xaxis.set_major_formatter(xfmt)
 
# Now plot to the "ungrouped" axes
axes[2, 3].plot(np.random.randn(10)*100 + 100,
                np.linspace(-3, 3, 10), 
                marker ="o", ls ="",
                color ="green")
 
plt.show()

Output:

Example 2:

Python3

import pylab as pl
from matplotlib import ticker
 
 
# helper function
def AutoLocatorInit(self):
     
    ticker.MaxNLocator.__init__(self, 
                                nbins = 4,
                                steps =[1, 2, 5, 10])
 
 
ticker.AutoLocator.__init__ = AutoLocatorInit
 
pl.plot(pl.randn(100))
pl.figure()
pl.hist(pl.randn(1000), bins = 40)
 
pl.show()