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Matplotlib.pyplot.psd() in Python

  • Last Updated : 21 Apr, 2020
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Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.

matplotlib.pyplot.csd() Function

The csd() function in pyplot module of matplotlib library is used to plot the cross-spectral density.

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: This parameter is a 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:



  • Pxx:This returns the values for the power spectrum P_{xx} before scaling.
  • freqs :This returns the frequencies for the elements in Pxx.
  • line :This returns the line created by this function.

The resultant is (Pxx, freqs, line)

Below examples illustrate the matplotlib.pyplot.psd() function in matplotlib.pyplot:

Example #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))
    
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + nse1 + np.cos(np.pi * t)
    
plt.psd(s1**2, 512, 1./dt, color ="green")
plt.xlabel('Frequency')
plt.ylabel('PSD(db)')
  
plt.suptitle('matplotlib.pyplot.psd() function \
Example', fontweight ="bold")
  
plt.show()

Output:

Example #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))
r = np.exp(-t / 0.05)
     
cnse1 = np.convolve(nse1, r, mode ='same')*dt
     
s1 = np.cos(np.pi * t) + cnse1 + np.sin(2 * np.pi * 10 * t) 
     
     
plt.psd(s1, 2**14, dt)
plt.ylabel('PSD(db)')
plt.xlabel('Frequency')
plt.title('matplotlib.pyplot.psd() Example\n',
          fontsize = 14, fontweight ='bold')
  
plt.show()

Output:

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