skued.baseline_dwt

skued.baseline_dwt(array, max_iter, level=None, wavelet='sym6', background_regions=None, mask=None, mode='constant', axis=-1)

Iterative method of baseline determination, based on the discrete wavelet transform.

Parameters:
  • array (~numpy.ndarray) – Data with background.

  • max_iter (int) – Number of iterations to perform.

  • level (int or None, optional) – Decomposition level. A higher level will result in a coarser approximation of the input signal (read: a lower frequency baseline). If None (default), the maximum level possible is used.

  • wavelet (PyWavelet.Wavelet object or str, optional) – Wavelet with which to perform the algorithm. See PyWavelet documentation for available values. Default is ‘sym6’.

  • background_regions (iterable or None, optional) –

    Indices of the array values that are known to be purely background. Depending on the dimensions of array, the format is different:

    • 1D signal: background_regions is a list of ints (indices) or slices, e.g. [0, 7, slice(534, 1000)].

    • 2D signal: background_regions is a list of tuples of ints (indices) or tuples of slices, e.g. [(14, 19), (slice(59, 82), slice(81,23))].

    Default is empty list.

  • mask (~numpy.ndarray, dtype bool, optional) – Mask array that evaluates to True for pixels that are invalid. Useful to determine which pixels are masked by a beam block.

  • mode (str, optional) – Signal extension mode, see pywt.Modes.

  • axis (int or tuple, optional) – Axis over which to compute the wavelet transform. Can also be a 2-tuple of ints for 2D baseline

Returns:

baseline – Baseline of the input array.

Return type:

~numpy.ndarray, shape (M,N)

See also

baseline_dt

Baseline-removal based on the dual-tree complex wavelet transform

References