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, 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)
Below examples illustrate the matplotlib.pyplot.csd() function in matplotlib.pyplot:
Example #1:
# Implementation of matplotlib function import numpy as np import matplotlib.pyplot as plt dt = 0.01t = 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() |
Output:
Example #2:
#Implementation of matplotlib function import numpy as np import matplotlib.pyplot as plt dt = 0.01t = 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() |
Output:
