`scipy.signal._czt:zoom_fft`

source: /scipy/signal/_czt.py:513

Signature

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

Summary

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.

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)))

✗

[<matplotlib.lines.Line2D object at 0x7fa8f6e4be00>]

plt.show()

✓

Aliases

scipy.signal.zoom_fft