`scipy.signal._czt:zoom_fft`

source: /scipy/signal/_czt.py :508

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.

Array API Standard Support

zoom_fft has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ⛔                     ⛔                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

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()

✓

Aliases

scipy.signal.zoom_fft