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    e9                     @   s   d dl mZmZ d dlZddlmZmZmZm	Z	m
Z
mZmZ ddlmZmZmZmZmZmZmZ ddlmZmZ g dZ			
dddZdd Z		
dddZdd Z		
dddZdd ZdS )    )partialreduceN   )_prep_axes_wavedecnwavedecwavedec2wavedecnwaverecwaverec2waverecn)iswtiswt2iswtnswtswt2swt_max_levelswtn)_modes_per_axis_wavelets_per_axis)mramra2mranimraimra2imranr   periodizationc                 C   sT  |dkr)|dkrt dt||dd}ttf|dd|}ttfi |}d}	n'|dkrIt|||d}ttfd	|i|}ttfi |}d
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d }|g| }ndd |
D }t|D ]3}|
| ||< ||}|j| jkr|tdd | jD  }|| |	r|||< qtt	
|| ||< qt|S )a  Forward 1D multiresolution analysis.

    It is a projection onto the wavelet subspaces.

    Parameters
    ----------
    data: array_like
        Input data
    wavelet : Wavelet object or name string
        Wavelet to use
    level : int, optional
        Decomposition level (must be >= 0). If level is None (default) then it
        will be calculated using the `dwt_max_level` function.
    axis: int, optional
        Axis over which to compute the DWT. If not given, the last axis is
        used. Currently only available when ``transform='dwt'``.
    transform : {'dwt', 'swt'}
        Whether to use the DWT or SWT for the transforms.
    mode : str, optional
        Signal extension mode, see `Modes` (default: 'symmetric'). This option
        is only used when transform='dwt'.

    Returns
    -------
    [cAn, {details_level_n}, ... {details_level_1}] : list
        For more information, see the detailed description in `wavedec`

    See Also
    --------
    imra, swt

    Notes
    -----
    This is sometimes referred to as an additive decomposition because the
    inverse transform (``imra``) is just the sum of the coefficient arrays
    [1]_. The decomposition using ``transform='dwt'`` corresponds to section
    2.2 while that using an undecimated transform (``transform='swt'``) is
    described in section 3.2 and appendix A.

    This transform does not share the variance partition property of ``swt``
    with `norm=True`. It does however, result in coefficients that are
    temporally aligned regardless of the symmetry of the wavelet used.

    The redundancy of this transform is ``(level + 1)``.

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551

    r   r   0transform swt only supports mode='periodization'T)waveletaxisnormlevelZtrim_approxZdwt)r   moder   r"   Funrecognized transform: {}r   c                 S      g | ]}t |qS  np
zeros_like.0cr&   r&   9D:\Projects\ConvertPro\env\Lib\site-packages\pywt/_mra.py
<listcomp>_       zmra.<locals>.<listcomp>c                 S      g | ]}t |qS r&   slicer+   szr&   r&   r-   r.   i       )
ValueErrordictr   r   r   r   r	   formatlenr(   r)   rangeshapetupleappend)datar   r"   r   	transformr#   kwargsforwardinverseZis_swt
wav_coeffs
mra_coeffsncztmpjrecr&   r&   r-   r      s@   7

r   c                 C   s   t dd | S )aW  Inverse 1D multiresolution analysis via summation.

    Parameters
    ----------
    mra_coeffs : list of ndarray
        Multiresolution analysis coefficients as returned by `mra`.

    Returns
    -------
    rec : ndarray
        The reconstructed signal.

    See Also
    --------
    mra

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551
    c                 S   s   | | S Nr&   )xyr&   r&   r-   <lambda>   s    zimra.<locals>.<lambda>)r   )rD   r&   r&   r-   r   t   s   r   r   r   c                 C   s  |dkr5|dkrt d|du rtdd | jD }t||dd}ttf|dd	|}ttfi |}n%|d
krSt|||d}ttfd|i|}ttfi |}nt d	||| }	g }
t
|	}t|	d }|g}td|D ]}|dd |	| D  qs|	d |d< ||}|j| jkr|tdd | jD  }|
| ||d< td|D ]C}g }tdD ]3}|| | }|	| | || |< ||}|j| jkr|tdd | jD  }|| ||| |< q|
t| q|
S )a  Forward 2D multiresolution analysis.

    It is a projection onto wavelet subspaces.

    Parameters
    ----------
    data: array_like
        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`.
    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.
        Currently only available when ``transform='dwt2'``.
    transform : {'dwt2', 'swt2'}
        Whether to use the DWT or SWT for the transforms.
    mode : str or 2-tuple of str, optional
        Signal extension mode, see `Modes` (default: 'symmetric'). This option
        is only used when transform='dwt2'.

    Returns
    -------
    coeffs : list
        For more information, see the detailed description in `wavedec2`

    Notes
    -----
    This is sometimes referred to as an additive decomposition because the
    inverse transform (``imra2``) is just the sum of the coefficient arrays
    [1]_. The decomposition using ``transform='dwt'`` corresponds to section
    2.2 while that using an undecimated transform (``transform='swt'``) is
    described in section 3.2 and appendix A.

    This transform does not share the variance partition property of ``swt2``
    with `norm=True`. It does however, result in coefficients that are
    temporally aligned regardless of the symmetry of the wavelet used.

    The redundancy of this transform is ``3 * level + 1``.

    See Also
    --------
    imra2, swt2

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551
    r   r   r   Nc                 s       | ]}t |V  qd S rJ   r   r+   sr&   r&   r-   	<genexpr>       zmra2.<locals>.<genexpr>Tr   axesr    r!   Zdwt2r   r#   rW   r"   r$   r   r   c                 S   r%   r&   r'   r*   r&   r&   r-   r.      r/   zmra2.<locals>.<listcomp>c                 S   r0   r&   r1   r3   r&   r&   r-   r.      r5      c                 S   r0   r&   r1   r3   r&   r&   r-   r.      r5   )r6   minr;   r7   r   r   r   r   r
   r8   r9   r(   r)   r:   r=   r<   )r>   r   r"   rW   r?   r#   r@   rA   rB   rC   rD   rE   rF   rG   rH   rI   dcoeffsnr&   r&   r-   r      sP   7

r   c                 C   s>   | d }t dt| D ]}t dD ]
}|| | | 7 }qq|S )aN  Inverse 2D multiresolution analysis via summation.

    Parameters
    ----------
    mra_coeffs : list
        Multiresolution analysis coefficients as returned by `mra2`.

    Returns
    -------
    rec : ndarray
        The reconstructed signal.

    See Also
    --------
    mra2

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551
    r   r   rY   )r:   r9   )rD   rI   rH   r\   r&   r&   r-   r      s   r   r   c                 C   s  t | j|\}}}t||}|dkrC|dkrtd|du r(tdd | jD }t||dd}	ttf|dd	|	}
ttfi |	}n*|d
krft	||}t|||d}	tt
fd|i|	}
ttfi |	}ntd||
| }g }t|}t|d }|g}td|D ]}|dd ||  D  q|d |d< ||}|j| jkr|tdd | jD  }|| ||d< td|D ]F}i }t||  }|D ]2}|| | }|| | || |< ||}|j| jkr|tdd | jD  }|||< ||| |< q|| q|S )a  Forward nD multiresolution analysis.

    It is a projection onto the wavelet subspaces.

    Parameters
    ----------
    data: array_like
        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`.
    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 : tuple of ints, optional
        Axes over which to compute the DWT. Repeated elements are not allowed.
    transform : {'dwtn', 'swtn'}
        Whether to use the DWT or SWT for the transforms.
    mode : str or tuple of str, optional
        Signal extension mode, see `Modes` (default: 'symmetric'). This option
        is only used when transform='dwtn'.

    Returns
    -------
    coeffs : list
        For more information, see the detailed description in `wavedecn`.

    See Also
    --------
    imran, swtn

    Notes
    -----
    This is sometimes referred to as an additive decomposition because the
    inverse transform (``imran``) is just the sum of the coefficient arrays
    [1]_. The decomposition using ``transform='dwt'`` corresponds to section
    2.2 while that using an undecimated transform (``transform='swt'``) is
    described in section 3.2 and appendix A.

    This transform does not share the variance partition property of ``swtn``
    with `norm=True`. It does however, result in coefficients that are
    temporally aligned regardless of the symmetry of the wavelet used.

    The redundancy of this transform is ``(2**n - 1) * level + 1`` where ``n``
    corresponds to the number of axes transformed.

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551
    r   r   r   Nc                 s   rP   rJ   rQ   rR   r&   r&   r-   rT   Z  rU   zmran.<locals>.<genexpr>TrV   r!   ZdwtnrX   r"   r$   r   r   c                 S   s   i | ]
\}}|t |qS r&   r'   )r+   kvr&   r&   r-   
<dictcomp>m  s    zmran.<locals>.<dictcomp>c                 S   r0   r&   r1   r3   r&   r&   r-   r.   u  r5   zmran.<locals>.<listcomp>c                 S   r0   r&   r1   r3   r&   r&   r-   r.     r5   )r   r;   r   r6   rZ   r7   r   r   r   r   r   r   r8   r9   r(   r)   r:   r=   itemsr<   listkeys)r>   r   r"   rW   r?   r#   Zaxes_shapesZndim_transformZwaveletsr@   rA   rB   modesrC   rD   rE   rF   rG   rH   rI   r[   Zdkeysr]   r&   r&   r-   r     sX   7


r   c                 C   s>   | d }t dt| D ]}| |  D ]\}}||7 }qq|S )aN  Inverse nD multiresolution analysis via summation.

    Parameters
    ----------
    mra_coeffs : list
        Multiresolution analysis coefficients as returned by `mra2`.

    Returns
    -------
    rec : ndarray
        The reconstructed signal.

    See Also
    --------
    mran

    References
    ----------
    .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal
        Coastal Sea Level Fluctuations Using Wavelets. Journal of the American
        Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880.
        https://doi.org/10.2307/2965551
    r   r   )r:   r9   r`   )rD   rI   rH   r]   r^   r&   r&   r-   r     s   
r   )Nr   r   r   )NrN   r   r   )NNr   r   )	functoolsr   r   numpyr(   Z_multilevelr   r   r   r   r	   r
   r   Z_swtr   r   r   r   r   r   r   _utilsr   r   __all__r   r   r   r   r   r   r&   r&   r&   r-   <module>   s$    $$
g
m
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