Python中的 Matplotlib.axes.Axes.acorr()

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。 Axes 类包含大部分图形元素：Axis、Tick、Line2D、Text、Polygon 等，并设置坐标系。 Axes 的实例通过回调属性支持回调。

matplotlib.axes.Axes.acorr()函数

matplotlib 库的 axes 模块中的Axes.acorr()函数用于绘制 x 的自相关。

Syntax: Axes.acorr(self, 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.axes 中的 matplotlib.axes.Axes.acorr()函数：

示例 1：

# Implementation of matplotlib 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
fig, ax = plt.subplots()
ax.acorr(geeks, maxlags = 9)
   
# Add labels to autocorrelation
# plotax.xlabel('X-axis')
ax.set_ylabel('Y-axis')
  
ax.set_title('matplotlib.axes.Axes.acorr() Example')
  
plt.show()

输出：

示例 2：

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
   
   
# Fixing random state for reproducibility
np.random.seed(10**7)
geeks = np.random.randn(100)
  
fig, ax = plt.subplots()
ax.acorr(geeks, usevlines = True, normed = True,
         maxlags = 80, lw = 3)
ax.grid(True)
  
ax.set_title('matplotlib.axes.Axes.acorr() Example')
  
plt.show()

输出：