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Module « scipy.signal »

Fonction zoom_fft - module scipy.signal

Signature de la fonction zoom_fft

def zoom_fft(x, fn, m=None, *, fs=2, endpoint=False, axis=-1) 

Description

help(scipy.signal.zoom_fft)

Compute the DFT of `x` only for frequencies in range `fn`.

Parameters
----------
x : array
    The signal to transform.
fn : array_like
    A length-2 sequence [`f1`, `f2`] giving the frequency range, or a
    scalar, for which the range [0, `fn`] is assumed.
m : int, optional
    The number of points to evaluate.  The default is the length of `x`.
fs : float, optional
    The sampling frequency.  If ``fs=10`` represented 10 kHz, for example,
    then `f1` and `f2` would also be given in kHz.
    The default sampling frequency is 2, so `f1` and `f2` should be
    in the range [0, 1] to keep the transform below the Nyquist
    frequency.
endpoint : bool, optional
    If True, `f2` is the last sample. Otherwise, it is not included.
    Default is False.
axis : int, optional
    Axis over which to compute the FFT. If not given, the last axis is
    used.

Returns
-------
out : ndarray
    The transformed signal.  The Fourier transform will be calculated
    at the points f1, f1+df, f1+2df, ..., f2, where df=(f2-f1)/m.

See Also
--------
ZoomFFT : Class that creates a callable partial FFT function.

Notes
-----
The defaults are chosen such that ``signal.zoom_fft(x, 2)`` is equivalent
to ``fft.fft(x)`` and, if ``m > len(x)``, that ``signal.zoom_fft(x, 2, m)``
is equivalent to ``fft.fft(x, m)``.

To graph the magnitude of the resulting transform, use::

    plot(linspace(f1, f2, m, endpoint=False), abs(zoom_fft(x, [f1, f2], m)))

If the transform needs to be repeated, use `ZoomFFT` to construct
a specialized transform function which can be reused without
recomputing constants.

Examples
--------
To plot the transform results use something like the following:

>>> import numpy as np
>>> from scipy.signal import zoom_fft
>>> t = np.linspace(0, 1, 1021)
>>> x = np.cos(2*np.pi*15*t) + np.sin(2*np.pi*17*t)
>>> f1, f2 = 5, 27
>>> X = zoom_fft(x, [f1, f2], len(x), fs=1021)
>>> f = np.linspace(f1, f2, len(x))
>>> import matplotlib.pyplot as plt
>>> plt.plot(f, 20*np.log10(np.abs(X)))
>>> plt.show()

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