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scipy.fft.fft — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/reference/generated/scipy.fft.fft.html
WEBscipy.fft.fft(x, n=None, axis=-1, norm=None, overwrite_x=False, workers=None, *, plan=None) [source] #. Compute the 1-D discrete Fourier Transform. This function computes the 1-D n -point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [1]. Parameters:
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Fourier Transforms (scipy.fft) — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/tutorial/fft.html
WEBFourier analysis is a method for expressing a function as a sum of periodic components, and for recovering the signal from those components. When both the function and its Fourier transform are replaced with discretized counterparts, it is called the discrete Fourier transform (DFT).
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Fourier Transforms With scipy.fft: Python Signal Processing
https://realpython.com/python-scipy-fft/
WEBIn this tutorial, you'll learn how to use the Fourier transform, a powerful tool for analyzing signals with applications ranging from audio processing to image compression. You'll explore several different transforms provided by Python's scipy.fft module.
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Discrete Fourier transforms (scipy.fft) — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/reference/fft.html
WEBCompute the 1-D discrete Fourier Transform for real input. irfft (x [, n, axis, norm, overwrite_x, ...]) Computes the inverse of rfft. rfft2 (x [, s, axes, norm, overwrite_x, ...]) Compute the 2-D FFT of a real array. irfft2 (x [, s, axes, norm, overwrite_x, ...]) Computes the inverse of rfft2.
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numpy.fft.fft — NumPy v1.26 Manual
https://numpy.org/doc/stable/reference/generated/numpy.fft.fft.html
WEBCompute the one-dimensional discrete Fourier Transform. This function computes the one-dimensional n -point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT]. Parameters: aarray_like. Input array, can be complex. nint, optional. Length of the transformed axis of the output.
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Discrete Fourier Transform (numpy.fft) — NumPy v1.26 Manual
https://numpy.org/doc/stable/reference/routines.fft.html
WEBThe SciPy module scipy.fft is a more comprehensive superset of numpy.fft, which includes only a basic set of routines. Standard FFTs # fft (a[, n, axis, norm])
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scipy.fftpack.fft — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/reference/generated/scipy.fftpack.fft.html
WEBscipy.fftpack.fft(x, n=None, axis=-1, overwrite_x=False) [source] #. Return discrete Fourier transform of real or complex sequence. The returned complex array contains y(0), y(1),..., y(n-1), where. y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum(). Parameters: xarray_like. Array to Fourier transform.
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scipy.fft.rfft — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/reference/generated/scipy.fft.rfft.html
WEBThis function computes the 1-D n -point discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT). Parameters: xarray_like. Input array. nint, optional. Number of points along transformation axis in the input to use.
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scipy.fft.fft2 — SciPy v1.13.0 Manual
https://docs.scipy.org/doc/scipy/reference/generated/scipy.fft.fft2.html
WEBscipy.fft.fft2(x, s=None, axes=(-2, -1), norm=None, overwrite_x=False, workers=None, *, plan=None) [source] #. Compute the 2-D discrete Fourier Transform. This function computes the N-D discrete Fourier Transform over any axes in an M-D array by means of the Fast Fourier Transform (FFT).
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scipy.signal.ShortTimeFFT.t — SciPy v1.13.0 Manual
https://docs.scipy.org/doc//scipy/reference/generated/scipy.signal.ShortTimeFFT.t.lower.html
WEBscipy.signal.ShortTimeFFT.t. #. Times of STFT for an input signal with n samples. Returns a 1d array with times of the stft values with the same parametrization. Note that the slices are delta_t = hop * T time units apart. Number of sample of the input signal. The first element of the range of slices to calculate.
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