Matplotlib is built on NumPy and sideby framework that’s why it is fast and efficient. It is open-source and has huge community support. It possesses the ability to work well with many operating systems and graphic backends. To get what matplotlib.pyplot.xcorr() do we need to understand Cross-Correlation.
Cross Correlation
The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables.
For example: Let us take two real valued functions f and g. g is at x is the difference along x axis. Now to calculate x ne use Cross Correlation.
matplotlib.pyplot.xcorr()
matplotlib.pyplot.xcorr() function plots cross correlation between two array lists.
Parameters :
| Parameter | Input type | Description |
|---|---|---|
| x | A vector of real or complex floating point numbers. | First variable for cross correlation. |
| y | a vector of real or complex floating point numbers. The default value is x. | Second variable for cross correlation. |
| detrend | callable | x and y are detrended by the detrend callable. This must be a function x = detrend(x) accepting and returning an numpy.array. It is optional parameter default is no normalization. |
| normed | bool | If True, input vectors are normalised to unit length. |
| usevlines | bool | If True, vertical lines are plotted from 0 to the xcorr value using Axes. It is an optional parameter |
| maxlags | int | Number of lags to show. If None, will return all 2 * len(x) – 1 lags. Optional parameter, default value is 10. |
Return :
| Parameter | Type | Description |
|---|---|---|
| lags | array (length 2*maxlags+1) | The lag vector. |
| c | array (length 2*maxlags+1) | The auto correlation vector. |
| line | Line Collection or Line2D | Artist added to the axes of the correlation: 1. Line Collection if use lines is True. 2. Line2D if use lines is False. |
| b | Line2D or None | Horizontal line at 0 if use lines is True None use lines is False. |
Example 1:
Python3
# import matplotlib libraryimport matplotlib.pyplot as pltimport numpy as np# float lists for cross # correlationx=[11.37, 14.23, 16.3, 12.36, 6.54, 4.23, 19.11, 12.13, 19.91, 11.00]y=[15.21, 12.23, 4.76, 9.89, 8.96, 19.26, 12.24, 11.54, 13.39, 18.96]# Plot graphfig = plt.figure()ax1 = fig.add_subplot(211)# cross correlation using# xcorr() functionax1.xcorr(x, y, usevlines=True, maxlags=5, normed=True, lw=2)# adding grid to the graphax1.grid(True)ax1.axhline(0, color='blue', lw=2)# show final plotted graphplt.show() |
Output :
Example 2:
Python3
# import matplotlib libraryimport matplotlib.pyplot as pltimport numpy as np# float lists for cross# correlationx, y = np.random.randn(2, 100)# Plot graphfig = plt.figure()ax1 = fig.add_subplot(211)# cross correlation using xcorr()# functionax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2)# adding grid to the graphax1.grid(True)ax1.axhline(0, color='blue', lw=2)# show final plotted graphplt.show() |
Output :
