using for loop to install conda package

.CondaPkg/env/Lib/site-packages/pywt/_mra.py

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+from functools import partial, reduce
+
+import numpy as np
+
+from ._multilevel import (_prep_axes_wavedecn, wavedec, wavedec2, wavedecn,
+                          waverec, waverec2, waverecn)
+from ._swt import iswt, iswt2, iswtn, swt, swt2, swt_max_level, swtn
+from ._utils import _modes_per_axis, _wavelets_per_axis
+
+__all__ = ["mra", "mra2", "mran", "imra", "imra2", "imran"]
+
+
+def mra(data, wavelet, level=None, axis=-1, transform='swt',
+        mode='periodization'):
+    """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
+
+    """
+    if transform == 'swt':
+        if mode != 'periodization':
+            raise ValueError(
+                "transform swt only supports mode='periodization'")
+        kwargs = dict(wavelet=wavelet, axis=axis, norm=True)
+        forward = partial(swt, level=level, trim_approx=True, **kwargs)
+        inverse = partial(iswt, **kwargs)
+        is_swt = True
+    elif transform == 'dwt':
+        kwargs = dict(wavelet=wavelet, mode=mode, axis=axis)
+        forward = partial(wavedec, level=level, **kwargs)
+        inverse = partial(waverec, **kwargs)
+        is_swt = False
+    else:
+        raise ValueError("unrecognized transform: {}".format(transform))
+
+    wav_coeffs = forward(data)
+
+    mra_coeffs = []
+    nc = len(wav_coeffs)
+
+    if is_swt:
+        # replicate same zeros array to save memory
+        z = np.zeros_like(wav_coeffs[0])
+        tmp = [z, ] * nc
+    else:
+        # zero arrays have variable size in DWT case
+        tmp = [np.zeros_like(c) for c in wav_coeffs]
+
+    for j in range(nc):
+        # tmp has arrays of zeros except for the jth entry
+        tmp[j] = wav_coeffs[j]
+
+        # reconstruct
+        rec = inverse(tmp)
+        if rec.shape != data.shape:
+            # trim any excess coefficients
+            rec = rec[tuple([slice(sz) for sz in data.shape])]
+        mra_coeffs.append(rec)
+
+        # restore zeros
+        if is_swt:
+            tmp[j] = z
+        else:
+            tmp[j] = np.zeros_like(tmp[j])
+    return mra_coeffs
+
+
+def imra(mra_coeffs):
+    """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
+    """
+    return reduce(lambda x, y: x + y, mra_coeffs)
+
+
+def mra2(data, wavelet, level=None, axes=(-2, -1), transform='swt2',
+         mode='periodization'):
+    """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
+    """
+    if transform == 'swt2':
+        if mode != 'periodization':
+            raise ValueError(
+                "transform swt only supports mode='periodization'")
+        if level is None:
+            level = min(swt_max_level(s) for s in data.shape)
+        kwargs = dict(wavelet=wavelet, axes=axes, norm=True)
+        forward = partial(swt2, level=level, trim_approx=True, **kwargs)
+        inverse = partial(iswt2, **kwargs)
+    elif transform == 'dwt2':
+        kwargs = dict(wavelet=wavelet, mode=mode, axes=axes)
+        forward = partial(wavedec2, level=level, **kwargs)
+        inverse = partial(waverec2, **kwargs)
+    else:
+        raise ValueError("unrecognized transform: {}".format(transform))
+
+    wav_coeffs = forward(data)
+
+    mra_coeffs = []
+    nc = len(wav_coeffs)
+    z = np.zeros_like(wav_coeffs[0])
+    tmp = [z]
+    for j in range(1, nc):
+        tmp.append([np.zeros_like(c) for c in wav_coeffs[j]])
+
+    # tmp has arrays of zeros except for the jth entry
+    tmp[0] = wav_coeffs[0]
+    # reconstruct
+    rec = inverse(tmp)
+    if rec.shape != data.shape:
+        # trim any excess coefficients
+        rec = rec[tuple([slice(sz) for sz in data.shape])]
+    mra_coeffs.append(rec)
+    # restore zeros
+    tmp[0] = z
+
+    for j in range(1, nc):
+        dcoeffs = []
+        for n in range(3):
+            # tmp has arrays of zeros except for the jth entry
+            z = tmp[j][n]
+            tmp[j][n] = wav_coeffs[j][n]
+            # reconstruct
+            rec = inverse(tmp)
+            if rec.shape != data.shape:
+                # trim any excess coefficients
+                rec = rec[tuple([slice(sz) for sz in data.shape])]
+            dcoeffs.append(rec)
+            # restore zeros
+            tmp[j][n] = z
+        mra_coeffs.append(tuple(dcoeffs))
+    return mra_coeffs
+
+
+def imra2(mra_coeffs):
+    """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
+    """
+    rec = mra_coeffs[0]
+    for j in range(1, len(mra_coeffs)):
+        for n in range(3):
+            rec += mra_coeffs[j][n]
+    return rec
+
+
+def mran(data, wavelet, level=None, axes=None, transform='swtn',
+         mode='periodization'):
+    """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
+    """
+    axes, axes_shapes, ndim_transform = _prep_axes_wavedecn(data.shape, axes)
+    wavelets = _wavelets_per_axis(wavelet, axes)
+
+    if transform == 'swtn':
+        if mode != 'periodization':
+            raise ValueError(
+                "transform swt only supports mode='periodization'")
+        if level is None:
+            level = min(swt_max_level(s) for s in data.shape)
+        kwargs = dict(wavelet=wavelets, axes=axes, norm=True)
+        forward = partial(swtn, level=level, trim_approx=True, **kwargs)
+        inverse = partial(iswtn, **kwargs)
+    elif transform == 'dwtn':
+        modes = _modes_per_axis(mode, axes)
+        kwargs = dict(wavelet=wavelets, mode=modes, axes=axes)
+        forward = partial(wavedecn, level=level, **kwargs)
+        inverse = partial(waverecn, **kwargs)
+    else:
+        raise ValueError("unrecognized transform: {}".format(transform))
+
+    wav_coeffs = forward(data)
+
+    mra_coeffs = []
+    nc = len(wav_coeffs)
+    z = np.zeros_like(wav_coeffs[0])
+    tmp = [z]
+    for j in range(1, nc):
+        tmp.append({k: np.zeros_like(v) for k, v in wav_coeffs[j].items()})
+
+    # tmp has arrays of zeros except for the jth entry
+    tmp[0] = wav_coeffs[0]
+    # reconstruct
+    rec = inverse(tmp)
+    if rec.shape != data.shape:
+        # trim any excess coefficients
+        rec = rec[tuple([slice(sz) for sz in data.shape])]
+    mra_coeffs.append(rec)
+    # restore zeros
+    tmp[0] = z
+
+    for j in range(1, nc):
+        dcoeffs = {}
+        dkeys = list(wav_coeffs[j].keys())
+        for k in dkeys:
+            # tmp has arrays of zeros except for the jth entry
+            z = tmp[j][k]
+            tmp[j][k] = wav_coeffs[j][k]
+            # tmp[j]['a' * len(k)] = z
+            # reconstruct
+            rec = inverse(tmp)
+            if rec.shape != data.shape:
+                # trim any excess coefficients
+                rec = rec[tuple([slice(sz) for sz in data.shape])]
+            dcoeffs[k] = rec
+            # restore zeros
+            tmp[j][k] = z
+            # tmp[j].pop('a' * len(k))
+        mra_coeffs.append(dcoeffs)
+    return mra_coeffs
+
+
+def imran(mra_coeffs):
+    """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
+    """
+    rec = mra_coeffs[0]
+    for j in range(1, len(mra_coeffs)):
+        for k, v in mra_coeffs[j].items():
+            rec += v
+    return rec