Python中的 matplotlib.pyplot.cohere()

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。在 Pyplot 中可以使用各种图，包括线图、等高线图、直方图、散点图、3D 图等。

matplotlib.pyplot.cohere()函数：

matplotlib 库的 pyplot 模块中的 cohere ()函数用于绘制 x 和 y 之间的相干性。相干性是归一化的交叉谱密度。

Syntax: matplotlib.pyplot.cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=, window=, noverlap=0, pad_to=None, sides=’default’, scale_by_freq=None, *, data=None, **kwargs)

Parameters: This method accept the following parameters that are described below:

• x, y: These parameter are the sequence of data.
• Fs : This parameter is a scalar. Its default value is 2.
• window: This parameter take a data segment as an argument and return the windowed version of the segment. Its default value is window_hanning()
• sides: This parameter specifies which sides of the spectrum to return. This can have following values : ‘default’, ‘onesided’ and ‘twosided’.
• pad_to : This parameter contains the integer value to which the data segment is padded.
• Fc: This parameter is also contains the integer value to offsets the x extents of the plot to reflect the frequency range. Its default value is 0
• NFFT : This parameter contains the number of data points used in each block for the FFT.
• detrend : This parameter contains the function applied to each segment before fft-ing, designed to remove the mean or linear trend {‘none’, ‘mean’, ‘linear’}.
• scale_by_freq : This parameter is allows for integration over the returned frequency values.
• noverlap : This parameter is the number of points of overlap between blocks.
• Fc : This parameter is the center frequency of x.

Returns: This returns the following:

• Cxy:This returns the coherence vector..
• freqs :This returns the frequencies for the elements in Cxy.

The resultant is (Cxy, freqs)

下面的示例说明了 matplotlib.axes 中的 matplotlib.pyplot.figure()函数：
示例 #1：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
   
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t))
nse2 = np.random.randn(len(t))
   
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + nse1
s2 = np.cos(np.pi * t) + nse2
   
plt.cohere(s1, s2**2, 128, 1./dt)
plt.xlabel('time')
plt.ylabel('coherence')
plt.title('matplotlib.pyplot.cohere() Example\n', 
               fontsize = 14, fontweight ='bold')
plt.show()

输出：

示例 2：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
   
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t))
nse2 = np.random.randn(len(t))
r = np.exp(-t / 0.05)
   
cnse1 = np.convolve(nse1, r, mode ='same')*dt
cnse2 = np.convolve(nse2, r, mode ='same')*dt
   
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + cnse1
s2 = np.cos(np.pi * t) + cnse2 + np.sin(2 * np.pi * 10 * t)
  
fig, [ax1, ax2] = plt.subplots(2, 1)
ax1.set_title('matplotlib.pyplot.cohere() Example\n', 
                    fontsize = 14, fontweight ='bold')
  
ax1.plot(t, s1, t, s2)
ax1.set_xlim(0, 5)
ax1.set_xlabel('time')
ax1.set_ylabel('s1 and s2')
ax1.grid(True)
   
ax2.cohere(s1, s2, 256, 1./dt)
ax2.set_ylabel('coherence')
plt.show()

输出：