periodogram#
- scipy.signal.periodogram(x, fs=1.0, window='boxcar', nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=-1)[source]#
- Estimate power spectral density using a periodogram. - Parameters:
- xarray_like
- Time series of measurement values 
- fsfloat, optional
- Sampling frequency of the x time series. Defaults to 1.0. 
- windowstr or tuple or array_like, optional
- Desired window to use. If window is a string or tuple, it is passed to - get_windowto generate the window values, which are DFT-even by default. See- get_windowfor a list of windows and required parameters. If window is array_like it will be used directly as the window and its length must be equal to the length of the axis over which the periodogram is computed. Defaults to ‘boxcar’.
- nfftint, optional
- Length of the FFT used. If None the length of x will be used. 
- detrendstr or function or False, optional
- Specifies how to detrend each segment. If - detrendis a string, it is passed as the type argument to the- detrendfunction. If it is a function, it takes a segment and returns a detrended segment. If- detrendis False, no detrending is done. Defaults to ‘constant’.
- return_onesidedbool, optional
- If True, return a one-sided spectrum for real data. If False return a two-sided spectrum. Defaults to True, but for complex data, a two-sided spectrum is always returned. 
- scaling{ ‘density’, ‘spectrum’ }, optional
- Selects between computing the power spectral density (‘density’) where Pxx has units of V**2/Hz and computing the squared magnitude spectrum (‘spectrum’) where Pxx has units of V**2, if x is measured in V and fs is measured in Hz. Defaults to ‘density’ 
- axisint, optional
- Axis along which the periodogram is computed; the default is over the last axis (i.e. - axis=-1).
 
- Returns:
- fndarray
- Array of sample frequencies. 
- Pxxndarray
- Power spectral density or power spectrum of x. 
 
 - See also - welch
- Estimate power spectral density using Welch’s method 
- lombscargle
- Lomb-Scargle periodogram for unevenly sampled data 
 - Notes - Consult the Spectral Analysis section of the SciPy User Guide for a discussion of the scalings of the power spectral density and the magnitude (squared) spectrum. - Added in version 0.12.0. - Examples - >>> import numpy as np >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> rng = np.random.default_rng() - Generate a test signal, a 2 Vrms sine wave at 1234 Hz, corrupted by 0.001 V**2/Hz of white noise sampled at 10 kHz. - >>> fs = 10e3 >>> N = 1e5 >>> amp = 2*np.sqrt(2) >>> freq = 1234.0 >>> noise_power = 0.001 * fs / 2 >>> time = np.arange(N) / fs >>> x = amp*np.sin(2*np.pi*freq*time) >>> x += rng.normal(scale=np.sqrt(noise_power), size=time.shape) - Compute and plot the power spectral density. - >>> f, Pxx_den = signal.periodogram(x, fs) >>> plt.semilogy(f, Pxx_den) >>> plt.ylim([1e-7, 1e2]) >>> plt.xlabel('frequency [Hz]') >>> plt.ylabel('PSD [V**2/Hz]') >>> plt.show()   - If we average the last half of the spectral density, to exclude the peak, we can recover the noise power on the signal. - >>> np.mean(Pxx_den[25000:]) 0.000985320699252543 - Now compute and plot the power spectrum. - >>> f, Pxx_spec = signal.periodogram(x, fs, 'flattop', scaling='spectrum') >>> plt.figure() >>> plt.semilogy(f, np.sqrt(Pxx_spec)) >>> plt.ylim([1e-4, 1e1]) >>> plt.xlabel('frequency [Hz]') >>> plt.ylabel('Linear spectrum [V RMS]') >>> plt.show()   - The peak height in the power spectrum is an estimate of the RMS amplitude. - >>> np.sqrt(Pxx_spec.max()) 2.0077340678640727