konno_ohmachi_smoothing(spectra, frequencies, bandwidth=40, count=1, enforce_no_matrix=False, max_memory_usage=512, normalize=False)[source]

Smooths a matrix containing one spectra per row with the Konno-Ohmachi smoothing window.

All spectra need to have frequency bins corresponding to the same frequencies.

This method first will estimate the memory usage and then either use a fast and memory intensive method or a slow one with a better memory usage.

  • spectra (numpy.ndarray (float32 or float64)) One or more spectra per row. If more than one the first spectrum has to be accessible via spectra[0], the next via spectra[1], ...
  • frequencies (numpy.ndarray (float32 or float64)) Contains the frequencies for the spectra.
  • bandwidth (float) Determines the width of the smoothing peak. Lower values result in a broader peak. Must be greater than 0. Defaults to 40.
  • count (int, optional) How often the apply the filter. For very noisy spectra it is useful to apply is more than once. Defaults to 1.
  • enforce_no_matrix (bool, optional) An efficient but memory intensive matrix-multiplication algorithm is used in case more than one spectra is to be smoothed or one spectrum is to be smoothed more than once if enough memory is available. This flag disables the matrix algorithm altogether. Defaults to False
  • max_memory_usage (int, optional) Set the maximum amount of extra memory in MB for this method. Decides whether or not the matrix multiplication method is used. Defaults to 512 MB.
  • normalize (bool, optional) The Konno-Ohmachi smoothing window is normalized on a logarithmic scale. Set this parameter to True to normalize it on a normal scale. Default to False.