Matplotlib.pyplot.psd() in Python
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:
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()
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Output:
Example #2:
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()
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Output:
Last Updated :
21 Apr, 2020
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