Matplotlib.axes.Axes.xcorr() in Python - GeeksforGeeks

21 Apr, 2020
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.
The Axes.xcorr() function in axes module of matplotlib library is used to plot the cross correlation between x and y.
Syntax: Axes.xcorr(self, x, y, normed=True, detrend=, usevlines=True, maxlags=10, *, data=None, **kwargs)
Parameters: This method accept the following parameters that are described below:
x, y : These parameter are the sequence of scalar.
detrend: This parameter is an optional parameter. Its default value is mlab.detrend_none
normed: This parameter is also an optional parameter and contains the bool value. Its default value is True
usevlines: This parameter is also an optional parameter and contains the bool value. Its default value is True
maxlags: This parameter is also an optional parameter and contains the integer value. Its default value is 10
linestyle: This parameter is also an optional parameter and used for plotting the data points, only when usevlines is False.
marker: This parameter is also an optional parameter and contains the string. Its default value is ‘o’
Returns: This method returns the following:
lags:This method returns the lag vector
c:This method returns the auto correlation vector.
line : Added LineCollection if usevlines is True, otherwise add Line2D.
b: This method returns the horizontal line at 0 if usevlines is True, otherwise None.
The resultant is (lags, c, line, b).
Below examples illustrate the matplotlib.axes.Axes.xcorr() function in matplotlib.axes:
Example 1:
# Implementation of matplotlib functionimport matplotlib.pyplot as pltimport numpy as np   # Time series datageeksx = np.array([24.40, 110.25, 20.05,                   22.00, 61.90, 7.80,                    15.00, 22.80, 34.90,                    57.30])  geeksy = np.array([24.40, 110.25, 20.05,                    22.00, 61.90, 7.80,                    15.00, 22.80, 34.90,                    57.30])   # Plot autocorrelationfig, ax = plt.subplots()ax.xcorr(geeksx,  geeksy, maxlags = 9,         color ="green")   # Add labels to autocorrelation # plotax.xlabel('X-axis')ax.set_ylabel('Y-axis')ax.set_xlabel('X-axis')  ax.set_title('matplotlib.axes.Axes.xcorr() Example')  plt.show()
Output:
Example 2:
# Implementation of matplotlib functionimport matplotlib.pyplot as pltimport numpy as np     # Fixing random state for# reproducibilitynp.random.seed(10**7)geeksx = np.random.randn(100)geeksy = np.random.randn(100)  fig, ax = plt.subplots()ax.xcorr(geeksx, geeksy, usevlines = True,         normed = True, maxlags = 80,          color ="green")  ax.grid(True)  ax.set_title('matplotlib.axes.Axes.xcorr() Example')  plt.show()
Output:
Python-matplotlib
Python
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