.. _sphx_glr_gallery_lines_bars_and_markers_spectrum_demo.py: ======================== Spectrum Representations ======================== The plots show different spectrum representations of a sine signal with additive noise. A (frequency) spectrum of a discrete-time signal is calculated by utilizing the fast Fourier transform (FFT). .. image:: /gallery/lines_bars_and_markers/images/sphx_glr_spectrum_demo_001.png :align: center .. code-block:: python import matplotlib.pyplot as plt import numpy as np np.random.seed(0) dt = 0.01 # sampling interval Fs = 1 / dt # sampling frequency t = np.arange(0, 10, dt) # generate noise: nse = np.random.randn(len(t)) r = np.exp(-t / 0.05) cnse = np.convolve(nse, r) * dt cnse = cnse[:len(t)] s = 0.1 * np.sin(4 * np.pi * t) + cnse # the signal fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 7)) # plot time signal: axes[0, 0].set_title("Signal") axes[0, 0].plot(t, s, color='C0') axes[0, 0].set_xlabel("Time") axes[0, 0].set_ylabel("Amplitude") # plot different spectrum types: axes[1, 0].set_title("Magnitude Spectrum") axes[1, 0].magnitude_spectrum(s, Fs=Fs, color='C1') axes[1, 1].set_title("Log. Magnitude Spectrum") axes[1, 1].magnitude_spectrum(s, Fs=Fs, scale='dB', color='C1') axes[2, 0].set_title("Phase Spectrum ") axes[2, 0].phase_spectrum(s, Fs=Fs, color='C2') axes[2, 1].set_title("Angle Spectrum") axes[2, 1].angle_spectrum(s, Fs=Fs, color='C2') axes[0, 1].remove() # don't display empty ax fig.tight_layout() plt.show() **Total running time of the script:** ( 0 minutes 0.209 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: spectrum_demo.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: spectrum_demo.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_