fft with 16GB memory,cause memory exhausted.
print(data_size) freqs, times, spec_arr = signal.spectrogram(data, fs=samp_rate,nfft=1024,return_onesided=False,axis=0,scaling='spectrum',mode='magnitude')
Output as below:
How to calculate fft of large size data ,with existing python package?
A more general solution is to do that yourself. 1D FFTs can be split in smaller ones thanks to the well-known Cooley–Tukey FFT algorithm and multidimentional decomposition. For more information about this strategy, please read The Design and Implementation of FFTW3. You can do the operation in virtually mapped memory so to do that more easily. Some library/package like the FFTW enable you to relatively-easily perform fast in-place FFTs. You may need to write your own Python package or to use Cython so not to allocate additional memory that is not memory mapped.
One alternative solution is to save your data in HDF5 (for example using h5py, and then use out_of_core_fft and then read again the file. But, be aware that this package is a bit old and appear not to be maintained anymore.
Answered By – Jérôme Richard
Answer Checked By – Gilberto Lyons (BugsFixing Admin)