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.xcorr() Function
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 function import matplotlib.pyplot as plt import numpy as np # Time series data neveropenx = np.array([24.40, 110.25, 20.05, 22.00, 61.90, 7.80, 15.00, 22.80, 34.90, 57.30]) neveropeny = np.array([24.40, 110.25, 20.05, 22.00, 61.90, 7.80, 15.00, 22.80, 34.90, 57.30]) # Plot autocorrelation fig, ax = plt.subplots() ax.xcorr(neveropenx, neveropeny, 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 function import matplotlib.pyplot as plt import numpy as np # Fixing random state for # reproducibility np.random.seed(10**7) neveropenx = np.random.randn(100) neveropeny = np.random.randn(100) fig, ax = plt.subplots() ax.xcorr(neveropenx,neveropeny, usevlines = True, normed = True, maxlags = 80, color ="green") ax.grid(True) ax.set_title('matplotlib.axes.Axes.xcorr() Example') plt.show() |
Output:
