Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.
matplotlib.axes.Axes.get_yminorticklabels() Function:
The Axes.get_yminorticklabels() function in axes module of matplotlib library is used to return the minor y tick labels.
Syntax: Axes.get_yminorticklabels(self)
Parameters: This method does not accepts any parameters.
Return value: This method returns List of Text instances.
Below examples illustrate the matplotlib.axes.Axes.get_yminorticklabels() function in matplotlib.axes:
Example 1:
# Implementation of matplotlib function import matplotlib.pyplot as plt import matplotlib.transforms as mtransforms fig, ax = plt.subplots() ax.plot(range(12, 24), range(12)) ax.set_yticks((2, 5, 7, 10)) ax.set_yticklabels(("Label-1", "Label-2", "Label-3", "Label-4")) w = ax.get_yminorticklabels() ax.text(15, 8, "yminorticklabels values : " + str(list(w)), fontweight ="bold") fig.suptitle('matplotlib.axes.Axes.get_yminorticklabels()\ function Example\n\n', fontweight ="bold") plt.show() |
Output:
Example 2:
# Implementation of matplotlib function import matplotlib.pyplot as plt import numpy as np import matplotlib.mlab as mlab import matplotlib.gridspec as gridspec fs = 1000t = np.linspace(0, 0.3, 301) A = np.array([2, 8]).reshape(-1, 1) f = np.array([150, 140]).reshape(-1, 1) xn = (A * np.sin(2 * np.pi * f * t)).sum(axis = 0) xn += 5 * np.random.randn(*t.shape) fig, ax = plt.subplots() yticks = [-40, -15, 10] ax.psd(xn, NFFT = 301, Fs = fs, window = mlab.window_none, pad_to = 1024, scale_by_freq = True) ax.set_yticks(yticks) ax.set_yticklabels(("Low-1", "High", "Low-2")) ax.grid(True) w = ax.get_yminorticklabels() ax.text(150, 6, "yminorticklabels values : " + str(list(w)), fontweight ="bold") fig.suptitle('matplotlib.axes.Axes.get_yminorticklabels() \ function Example\n\n', fontweight ="bold") plt.show() |
Output:
