Function iswt2

pywt/_swt.py:381–537

Multilevel 2D inverse discrete stationary wavelet transform. Parameters ---------- coeffs : list Approximation and details coefficients:: [ (cA_n, (cH_n, cV_n, cD_n) ), ..., (cA

(coeffs, wavelet, norm=False, axes=(-2, -1))

def iswt2(coeffs, wavelet, norm=False, axes=(-2, -1)):
    """
    Multilevel 2D inverse discrete stationary wavelet transform.

    Parameters
    ----------
    coeffs : list
        Approximation and details coefficients::

            [
                (cA_n,
                    (cH_n, cV_n, cD_n)
                ),
                ...,
                (cA_2,
                    (cH_2, cV_2, cD_2)
                ),
                (cA_1,
                    (cH_1, cV_1, cD_1)
                )
            ]

        where cA is approximation, cH is horizontal details, cV is
        vertical details, cD is diagonal details and n is the number of
        levels.  Index 1 corresponds to ``start_level`` from ``pywt.swt2``.
    wavelet : Wavelet object or name string, or 2-tuple of wavelets
        Wavelet to use.  This can also be a 2-tuple of wavelets to apply per
        axis.
    norm : bool, optional
        Controls the normalization used by the inverse transform. This must
        be set equal to the value that was used by ``pywt.swt2`` to preserve
        the energy of a round-trip transform.

    Returns
    -------
    2D array of reconstructed data.

    Examples
    --------
    >>> import pywt
    >>> coeffs = pywt.swt2([[1,2,3,4],[5,6,7,8],
    ...                     [9,10,11,12],[13,14,15,16]],
    ...                    'db1', level=2)
    >>> pywt.iswt2(coeffs, 'db1')
    array([[  1.,   2.,   3.,   4.],
           [  5.,   6.,   7.,   8.],
           [  9.,  10.,  11.,  12.],
           [ 13.,  14.,  15.,  16.]])

    """

    # If swt was called with trim_approx=False, first element is a tuple
    trim_approx = not isinstance(coeffs[0], (tuple, list))
    cA = coeffs[0] if trim_approx else coeffs[0][0]
    if cA.ndim != 2 or axes != (-2, -1):
        # convert to swtn coefficient format and call iswtn instead
        if trim_approx:
            coeffs_nd = [cA] + [{'da': h, 'ad': v, 'dd': d}