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Summary
| Description |
English: Two power spectra calculated by the periodogram method. |
| Date | |
| Source | Own work based on code from File:Periodogram windows.png by Kirlf (talk · contribs) |
| Author | Ixfd64 |
Source code
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
w_1 = 40 # frequency of the 1st component of the signal (Hz)
w_2 = 60 # frequency of the 2nd component of the signal (Hz)
a = 0.5 # magnitude of the 1st component of the signal
b = 1.0 # magnitude of the 2nd component of the signal
t = np.array([i for i in range(1, 301)]) / 1000 # time samples (s)
fs = 1 / (t[1] - t[0]) # sampling frequency (Hz)
x = a * np.cos(2 * np.pi * w_1 * t) + b * np.sin(2 * np.pi * w_2 * t) # considered signal
n = .2 * np.random.randn(len(t)) # white Gaussian noise
y = x + n
windows = [None, 'hamming']
for window in windows:
if window is None:
label = 'rect'
else:
label = window
f, Pxx_den = signal.periodogram(y, fs=fs, scaling='spectrum', nfft=2048, window=window)
plt.semilogy(f[1:200], Pxx_den[1:200], label=label)
plt.ylabel('Spectrum')
plt.xlabel('Frequencies (Hz)')
plt.title('Periodogram')
plt.grid(True)
plt.legend()
plt.savefig("Periodogram windows.svg")
Licensing
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File history
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 21:49, 2 May 2023 | | 576 × 432 (52 KB) | Ixfd64 | Uploaded own work with UploadWizard |
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