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Module « scipy.signal »

Fonction spectrogram - module scipy.signal

Signature de la fonction spectrogram

def spectrogram(x, fs=1.0, window=('tukey', 0.25), nperseg=None, noverlap=None, nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=-1, mode='psd') 

Description

help(scipy.signal.spectrogram)

Compute a spectrogram with consecutive Fourier transforms (legacy function).

Spectrograms can be used as a way of visualizing the change of a
nonstationary signal's frequency content over time.

.. legacy:: function

    :class:`ShortTimeFFT` is a newer STFT / ISTFT implementation with more
    features also including a :meth:`~ShortTimeFFT.spectrogram` method.
    A :ref:`comparison <tutorial_stft_legacy_stft>` between the
    implementations can be found in the :ref:`tutorial_stft` section of
    the :ref:`user_guide`.

Parameters
----------
x : array_like
    Time series of measurement values
fs : float, optional
    Sampling frequency of the `x` time series. Defaults to 1.0.
window : str or tuple or array_like, optional
    Desired window to use. If `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 `window` is array_like it will be used
    directly as the window and its length must be nperseg.
    Defaults to a Tukey window with shape parameter of 0.25.
nperseg : int, optional
    Length of each segment. Defaults to None, but if window is str or
    tuple, is set to 256, and if window is array_like, is set to the
    length of the window.
noverlap : int, optional
    Number of points to overlap between segments. If `None`,
    ``noverlap = nperseg // 8``. Defaults to `None`.
nfft : int, optional
    Length of the FFT used, if a zero padded FFT is desired. If
    `None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
    Specifies how to detrend each segment. If `detrend` is a
    string, it is passed as the `type` argument to the `detrend`
    function. If it is a function, it takes a segment and returns a
    detrended segment. If `detrend` is `False`, no detrending is
    done. Defaults to 'constant'.
return_onesided : bool, 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 `Sxx` has units of V**2/Hz and computing the power
    spectrum ('spectrum') where `Sxx` has units of V**2, if `x`
    is measured in V and `fs` is measured in Hz. Defaults to
    'density'.
axis : int, optional
    Axis along which the spectrogram is computed; the default is over
    the last axis (i.e. ``axis=-1``).
mode : str, optional
    Defines what kind of return values are expected. Options are
    ['psd', 'complex', 'magnitude', 'angle', 'phase']. 'complex' is
    equivalent to the output of `stft` with no padding or boundary
    extension. 'magnitude' returns the absolute magnitude of the
    STFT. 'angle' and 'phase' return the complex angle of the STFT,
    with and without unwrapping, respectively.

Returns
-------
f : ndarray
    Array of sample frequencies.
t : ndarray
    Array of segment times.
Sxx : ndarray
    Spectrogram of x. By default, the last axis of Sxx corresponds
    to the segment times.

See Also
--------
periodogram: Simple, optionally modified periodogram
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
welch: Power spectral density by Welch's method.
csd: Cross spectral density by Welch's method.
ShortTimeFFT: Newer STFT/ISTFT implementation providing more features,
              which also includes a :meth:`~ShortTimeFFT.spectrogram`
              method.

Notes
-----
An appropriate amount of overlap will depend on the choice of window
and on your requirements. In contrast to welch's method, where the
entire data stream is averaged over, one may wish to use a smaller
overlap (or perhaps none at all) when computing a spectrogram, to
maintain some statistical independence between individual segments.
It is for this reason that the default window is a Tukey window with
1/8th of a window's length overlap at each end.


.. versionadded:: 0.16.0

References
----------
.. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
       "Discrete-Time Signal Processing", Prentice Hall, 1999.

Examples
--------
>>> import numpy as np
>>> from scipy import signal
>>> from scipy.fft import fftshift
>>> import matplotlib.pyplot as plt
>>> rng = np.random.default_rng()

Generate a test signal, a 2 Vrms sine wave whose frequency is slowly
modulated around 3kHz, corrupted by white noise of exponentially
decreasing magnitude sampled at 10 kHz.

>>> fs = 10e3
>>> N = 1e5
>>> amp = 2 * np.sqrt(2)
>>> noise_power = 0.01 * fs / 2
>>> time = np.arange(N) / float(fs)
>>> mod = 500*np.cos(2*np.pi*0.25*time)
>>> carrier = amp * np.sin(2*np.pi*3e3*time + mod)
>>> noise = rng.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> noise *= np.exp(-time/5)
>>> x = carrier + noise

Compute and plot the spectrogram.

>>> f, t, Sxx = signal.spectrogram(x, fs)
>>> plt.pcolormesh(t, f, Sxx, shading='gouraud')
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()

Note, if using output that is not one sided, then use the following:

>>> f, t, Sxx = signal.spectrogram(x, fs, return_onesided=False)
>>> plt.pcolormesh(t, fftshift(f), fftshift(Sxx, axes=0), shading='gouraud')
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()

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