Python中的 Matplotlib.pyplot.acorr()

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。 Pyplot是Matplotlib模块的基于状态的接口，它提供了一个类似 MATLAB 的接口。

matplotlib.pyplot.acorr()函数

matplotlib 库的 pyplot 模块中的acorr()函数用于绘制 x (array-like)的自相关。

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).

编程需要懂一点英语

下面的示例说明了 matplotlib.pyplot 中的 matplotlib.pyplot.acorr()函数：

示例 #1：

# Implementation of matplotlib.pyplot.acorr()
# function
  
import matplotlib.pyplot as plt
import numpy as np
  
# Time series data
geeks = 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(geeks, maxlags = 9)
  
# Add labels to autocorrelation plot
plt.title("Autocorrelation of Geeksforgeeks' Users data")
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
  
# Display the autocorrelation plot
plt.show()

输出：

示例 #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)
  
geeks = np.random.randn(51 )
  
plt.title("Autocorrelation Example")
plt.acorr(geeks, usevlines = True, 
          normed = True, maxlags = 50, 
          lw = 2)
  
plt.grid(True)
plt.show()

输出：