Function wavedec2

pywt/_multilevel.py:179–253

Multilevel 2D Discrete Wavelet Transform. Parameters ---------- data : ndarray 2D input data wavelet : Wavelet object or name string, or 2-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=(-2, -1))

def wavedec2(data, wavelet, mode='symmetric', level=None, axes=(-2, -1)):
    """
    Multilevel 2D Discrete Wavelet Transform.

    Parameters
    ----------
    data : ndarray
        2D input data
    wavelet : Wavelet object or name string, or 2-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 2-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 : 2-tuple of ints, optional
        Axes over which to compute the DWT. Repeated elements are not allowed.

    Returns
    -------
    [cAn, (cHn, cVn, cDn), ... (cH1, cV1, cD1)] : list
        Coefficients list.  For user-specified ``axes``, ``cH*``
        corresponds to ``axes[0]`` while ``cV*`` corresponds to ``axes[1]``.
        The first element returned is the approximation coefficients for the
        nth level of decomposition.  Remaining elements are tuples of detail
        coefficients in descending order of decomposition level.
        (i.e. ``cH1`` are the horizontal detail coefficients at the first
        level)

    Examples
    --------
    >>> import pywt
    >>> import numpy as np
    >>> coeffs = pywt.wavedec2(np.ones((4,4)), 'db1')
    >>> # Levels:
    >>> len(coeffs)-1
    2
    >>> pywt.waverec2(coeffs, 'db1')
    array([[ 1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.],
           [ 1.,  1.,  1.,  1.]])
    """
    data = np.asarray(data)
    if data.ndim < 2:
        raise ValueError("Expected input data to have at least 2 dimensions.")

    axes = tuple(axes)
    if len(axes) != 2:
        raise ValueError("Expected 2 axes")
    if len(axes) != len(set(axes)):
        raise ValueError("The axes passed to wavedec2 must be unique.")
    try:
        axes_sizes = [data.shape[ax] for ax in axes]
    except IndexError:
        raise AxisError("Axis greater than data dimensions")