Function wavedecn

pywt/_multilevel.py:360–442

Multilevel nD Discrete Wavelet Transform. Parameters ---------- data : ndarray nD input data wavelet : Wavelet object or name string, or tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to apply along each axis in ``axes``

(data, wavelet, mode='symmetric', level=None, axes=None)

def wavedecn(data, wavelet, mode='symmetric', level=None, axes=None):
    """
    Multilevel nD Discrete Wavelet Transform.

    Parameters
    ----------
    data : ndarray
        nD input data
    wavelet : Wavelet object or name string, or tuple of wavelets
        Wavelet to use.  This can also be a tuple containing a wavelet to
        apply along each axis in ``axes``.
    mode : str or tuple of str, optional
        Signal extension mode, see :ref:`Modes <ref-modes>`. This can
        also be a tuple containing a mode to apply along each axis in ``axes``.
    level : int, optional
        Decomposition level (must be >= 0). If level is None (default) then it
        will be calculated using the ``dwt_max_level`` function.
    axes : sequence of ints, optional
        Axes over which to compute the DWT. Axes may not be repeated. The
        default is None, which means transform all axes
        (``axes = range(data.ndim)``).

    Returns
    -------
    [cAn, {details_level_n}, ... {details_level_1}] : list
        Coefficients list.  Coefficients are listed in descending order of
        decomposition level.  ``cAn`` are the approximation coefficients at
        level ``n``.  Each ``details_level_i`` element is a dictionary
        containing detail coefficients at level ``i`` of the decomposition. As
        a concrete example, a 3D decomposition would have the following set of
        keys in each ``details_level_i`` dictionary::

            {'aad', 'ada', 'daa', 'add', 'dad', 'dda', 'ddd'}

        where the order of the characters in each key map to the specified
        ``axes``.

    Examples
    --------
    >>> import numpy as np
    >>> from pywt import wavedecn, waverecn
    >>> coeffs = wavedecn(np.ones((4, 4, 4)), 'db1')
    >>> # Levels:
    >>> len(coeffs)-1
    2
    >>> waverecn(coeffs, 'db1')
    array([[[ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.]],
           [[ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.]],
           [[ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.],
            [ 1.,  1.,  1.,  1.]],