periodogram(x, fs=1.0, window='boxcar', nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=-1)
Time series of measurement values
Sampling frequency of the x time series. Defaults to 1.0.
Desired window to use. If :None:None:`window` is a string or tuple, it is passed to get_window
to generate the window values, which are DFT-even by default. See get_window
for a list of windows and required parameters. If :None:None:`window` is array_like it will be used directly as the window and its length must be nperseg. Defaults to 'boxcar'.
Length of the FFT used. If :None:None:`None` the length of x will be used.
Specifies how to detrend each segment. If detrend
is a string, it is passed as the :None:None:`type` argument to the detrend
function. If it is a function, it takes a segment and returns a detrended segment. If detrend
is :None:None:`False`, no detrending is done. Defaults to 'constant'.
If :None:None:`True`, return a one-sided spectrum for real data. If :None:None:`False` return a two-sided spectrum. Defaults to :None:None:`True`, but for complex data, a two-sided spectrum is always returned.
Selects between computing the power spectral density ('density') where :None:None:`Pxx` has units of V**2/Hz and computing the power spectrum ('spectrum') where :None:None:`Pxx` has units of V**2, if x is measured in V and :None:None:`fs` is measured in Hz. Defaults to 'density'
Axis along which the periodogram is computed; the default is over the last axis (i.e. axis=-1
).
Array of sample frequencies.
Power spectral density or power spectrum of x.
Estimate power spectral density using a periodogram.
lombscargle
Lomb-Scargle periodogram for unevenly sampled data
welch
Estimate power spectral density using Welch's method
>>> 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()) 2.0077340678640727See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._spectral_py.welch
scipy.signal._spectral_py.coherence
scipy.signal._spectral_py.csd
scipy.signal._spectral_py.periodogram
scipy.signal._spectral_py.spectrogram
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