zoom_fft(x, fn, m=None, *, fs=2, endpoint=False, axis=-1)
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.
The signal to transform.
A length-2 sequence [`f1`, :None:None:`f2`] giving the frequency range, or a scalar, for which the range [0, :None:None:`fn`] is assumed.
The number of points to evaluate. The default is the length of x.
The sampling frequency. If fs=10
represented 10 kHz, for example, then :None:None:`f1` and :None:None:`f2` would also be given in kHz. The default sampling frequency is 2, so :None:None:`f1` and :None:None:`f2` should be in the range [0, 1] to keep the transform below the Nyquist frequency.
If True, :None:None:`f2` is the last sample. Otherwise, it is not included. Default is False.
Axis over which to compute the FFT. If not given, the last axis is used.
The transformed signal. The Fourier transform will be calculated at the points f1, f1+df, f1+2df, ..., f2, where df=(f2-f1)/m.
Compute the DFT of x only for frequencies in range :None:None:`fn`.
ZoomFFT
Class that creates a callable partial FFT function.
To plot the transform results use something like the following:
>>> 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._czt.CZT
scipy.signal._czt.zoom_fft
scipy.signal._czt.ZoomFFT
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