Matplotlib.pyplot.acorr() in Python
Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.
matplotlib.pyplot.acorr() Function
The acorr() function in pyplot module of matplotlib library is used to plot the autocorrelation of x (array-like).
Syntax: matplotlib.pyplot.acorr(x, *, data=None, **kwargs)
Parameters: This method accept the following parameters that are described below:
- x: This parameter is a 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.pyplot.acorr() function in matplotlib.pyplot:
Example #1:
# Implementation of matplotlib.pyplot.acorr() # function import matplotlib.pyplot as plt import numpy as np # Time series data Beginner = np.array([ 24.40 , 110.25 , 20.05 , 22.00 , 61.90 , 7.80 , 15.00 , 22.80 , 34.90 , 57.30 ]) # Plot autocorrelation plt.acorr(Beginner, maxlags = 9 ) # Add labels to autocorrelation plot plt.title( "Autocorrelation of w3wiki' Users data" ) plt.xlabel( 'X-axis' ) plt.ylabel( 'Y-axis' ) # Display the autocorrelation plot plt.show() |
Output:
Example #2:
# Implementation of matplotlib.pyplot.acorr() # function import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed( 10 * * 7 ) Beginner = np.random.randn( 51 ) plt.title( "Autocorrelation Example" ) plt.acorr(Beginner, usevlines = True , normed = True , maxlags = 50 , lw = 2 ) plt.grid( True ) plt.show() |
Output: