Python中的 Matplotlib.pyplot.csd()

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.csd()函数

matplotlib 库的 pyplot 模块中的csd()函数用于绘制交叉光谱密度。

Syntax: matplotlib.pyplot.csd(x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=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.
• 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.
• return_line : This parameter include the line object plotted in the returned values.

Returns: This returns the following:

• Pxy:This returns the values for the cross spectrum P_{xy} before scaling.
• freqs :This returns the frequencies for the elements in Pxy.
• line :This returns the line created by this function.

The resultant is (Pxy, freqs, line)

下面的示例说明了 matplotlib.pyplot 中的 matplotlib.pyplot.csd()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.csd(s1, s2**2, 128, 1./dt)
plt.xlabel('Frequency')
plt.ylabel('CSD(db)')
  
plt.title('matplotlib.pyplot.csd() function Example',
          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)
     
plt.plot(t, s1, t, s2)
plt.xlim(0, 5)
plt.ylabel('s1 and s2')
plt.grid(True)
plt.show()
  
plt.csd(s1, s2, 256, 1./dt)
plt.ylabel('CSD(db)')
plt.xlabel('Frequency')
   
plt.title('matplotlib.pyplot.csd() function Example'
             ,fontweight="bold")
  
plt.show()

输出：