feature: added DTCWT operator

docs/source/api/index.rst

@@ -103,6 +103,7 @@ Signal processing
     DWT
     DWT2D
     DCT
+    DTCWT
     Seislet
     Radon2D
     Radon3D

docs/source/installation.rst

@@ -318,9 +318,20 @@ of GPUs should install it prior to installing PyLops as described in :ref:`Optio
 In alphabetic order:
 
 
+dtcwt
+-----
+`dtcwt <https://dtcwt.readthedocs.io/en/0.12.0/>`_ is a library used to implement the DT-CWT operators.
+
+Install it via ``pip`` with:
+
+.. code-block:: bash
+
+   >> pip install dtcwt
+
+
 Devito
 ------
-`Devito <https://github.com/devitocodes/devito>`_ is library used to solve PDEs via
+`Devito <https://github.com/devitocodes/devito>`_ is a library used to solve PDEs via
 the finite-difference method. It is used in PyLops to compute wavefields
 :py:class:`pylops.waveeqprocessing.AcousticWave2D`
 

environment-dev.yml

@@ -26,6 +26,7 @@ dependencies:
   - black
   - pip:
     - devito
+    - dtcwt
     - scikit-fmm
     - spgl1
     - pytest-runner

examples/plot_dtcwt.py

@@ -0,0 +1,82 @@
+"""
+Dual-Tree Complex Wavelet Transform
+===================================
+This example shows how to use the :py:class:`pylops.signalprocessing.DTCWT` operator to perform the
+1D Dual-Tree Complex Wavelet Transform on a (single or multi-dimensional) input array. Such a transform
+provides advantages over the DWT which lacks shift invariance in 1-D and directional sensitivity in N-D.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pywt
+
+import pylops
+
+plt.close("all")
+
+###############################################################################
+# To begin with, let's define two 1D arrays with a spike at slightly different location
+
+n = 128
+x = np.zeros(n)
+x1 = np.zeros(n)
+
+x[59] = 1
+x1[63] = 1
+
+###############################################################################
+# We now create the DTCWT operator with the shape of our input array. The DTCWT transform
+# provides a Pyramid object that is internally flattened out into a vector. Here we re-obtain
+# the Pyramid object such that we can visualize the different scales indipendently.
+
+level = 3
+DCOp = pylops.signalprocessing.DTCWT(dims=n, level=level)
+Xc = DCOp.get_pyramid(DCOp @ x)
+Xc1 = DCOp.get_pyramid(DCOp @ x1)
+
+###############################################################################
+# To prove the superiority of the DTCWT transform over the DWT in shift-invariance,
+# let's also compute the DWT transform of these two signals and compare the coefficents
+# of both transform at level 3. As you will see, the coefficients change completely for
+# the DWT despite the two input signals are very similar; this is not the case for the
+# DCWT transform.
+
+DOp = pylops.signalprocessing.DWT(dims=n, level=level, wavelet="sym7")
+X = pywt.array_to_coeffs(DOp @ x, DOp.sl, output_format="wavedecn")
+X1 = pywt.array_to_coeffs(DOp @ x1, DOp.sl, output_format="wavedecn")
+
+fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 5))
+axs[0, 0].stem(np.abs(X[1]["d"]), linefmt="k", markerfmt=".k", basefmt="k")
+axs[0, 0].set_title(f"DWT (Norm={np.linalg.norm(np.abs(X[1]['d']))**2:.3f})")
+axs[0, 1].stem(np.abs(X1[1]["d"]), linefmt="k", markerfmt=".k", basefmt="k")
+axs[0, 1].set_title(f"DWT (Norm={np.linalg.norm(np.abs(X1[1]['d']))**2:.3f})")
+axs[1, 0].stem(np.abs(Xc.highpasses[2]), linefmt="k", markerfmt=".k", basefmt="k")
+axs[1, 0].set_title(f"DCWT (Norm={np.linalg.norm(np.abs(Xc.highpasses[2]))**2:.3f})")
+axs[1, 1].stem(np.abs(Xc1.highpasses[2]), linefmt="k", markerfmt=".k", basefmt="k")
+axs[1, 1].set_title(f"DCWT (Norm={np.linalg.norm(np.abs(Xc1.highpasses[2]))**2:.3f})")
+plt.tight_layout()
+
+###################################################################################
+# The DTCWT can also be performed on multi-dimension arrays, where the parameter
+# ``axis`` is used to define the axis over which the transform is performed. Let's
+# just replicate our input signal over the second axis and see how the transform
+# will produce the same series of coefficients for all replicas.
+
+nrepeat = 10
+x = np.repeat(np.random.rand(n, 1), 10, axis=1).T
+
+level = 3
+DCOp = pylops.signalprocessing.DTCWT(dims=(nrepeat, n), level=level, axis=1)
+X = DCOp @ x
+
+fig, axs = plt.subplots(1, 2, sharey=True, figsize=(10, 3))
+axs[0].imshow(X[0])
+axs[0].axis("tight")
+axs[0].set_xlabel("Coeffs")
+axs[0].set_ylabel("Replicas")
+axs[0].set_title("DTCWT Real")
+axs[1].imshow(X[1])
+axs[1].axis("tight")
+axs[1].set_xlabel("Coeffs")
+axs[1].set_title("DTCWT Imag")
+plt.tight_layout()

pylops/signalprocessing/__init__.py

@@ -24,7 +24,7 @@
     DWT                             One dimensional Wavelet operator.
     DWT2D                           Two dimensional Wavelet operator.
     DCT                             Discrete Cosine Transform.
-    Seislet                         Two dimensional Seislet operator.
+    DTCWT                           Dual-Tree Complex Wavelet TransformsSeislet                         Two dimensional Seislet operator.
     Radon2D	                        Two dimensional Radon transform.
     Radon3D	                        Three dimensional Radon transform.
     Sliding1D                       1D Sliding transform operator.
@@ -62,6 +62,8 @@
 from .dwt2d import *
 from .seislet import *
 from .dct import *
+from .dtcwt import *
+
 
 __all__ = [
     "FFT",
@@ -92,4 +94,5 @@
     "DWT2D",
     "Seislet",
     "DCT",
+    "DTCWT",
 ]

pylops/signalprocessing/dct.py

@@ -29,7 +29,7 @@ class DCT(LinearOperator):
     axes : :obj:`int` or :obj:`list`, optional
         Axes over which the DCT is computed. If ``None``, the transform is applied
         over all axes.
-    workers :obj:`int`, optional
+    workers : :obj:`int`, optional
         Maximum number of workers to use for parallel computation. If negative,
         the value wraps around from os.cpu_count().
     dtype : :obj:`DTypeLike`, optional

pylops/signalprocessing/dtcwt.py

@@ -0,0 +1,182 @@
+__all__ = ["DTCWT"]
+
+from typing import Union
+
+import numpy as np
+
+from pylops import LinearOperator
+from pylops.utils import deps
+from pylops.utils._internal import _value_or_sized_to_tuple
+from pylops.utils.decorators import reshaped
+from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray
+
+dtcwt_message = deps.devito_import("the dtcwt module")
+
+if dtcwt_message is None:
+    import dtcwt
+
+
+class DTCWT(LinearOperator):
+    r"""Dual-Tree Complex Wavelet Transform
+
+    Perform 1D Dual-Tree Complex Wavelet Transform along an ``axis`` of a
+    multi-dimensional array of size ``dims``.
+
+    Note that the DTCWT operator is an overload of the ``dtcwt``
+    implementation of the DT-CWT transform. Refer to
+    https://dtcwt.readthedocs.io for a detailed description of the
+    input parameters.
+
+    Parameters
+    ----------
+    dims : :obj:`int` or :obj:`tuple`
+        Number of samples for each dimension.
+    birot : :obj:`str`, optional
+        Level 1 wavelets to use. See :py:func:`dtcwt.coeffs.birot`. Default is `"near_sym_a"`.
+    qshift : :obj:`str`, optional
+        Level >= 2 wavelets to use. See :py:func:`dtcwt.coeffs.qshift`. Default is `"qshift_a"`
+    level : :obj:`int`, optional
+        Number of levels of wavelet decomposition. Default is 3.
+    include_scale : :obj:`bool`, optional
+        Include scales in pyramid. See :py:class:`dtcwt.Pyramid`. Default is False.
+    axis : :obj:`int`, optional
+        Axis on which the transform is performed.
+    dtype : :obj:`DTypeLike`, optional
+        Type of elements in input array.
+    name : :obj:`str`, optional
+        Name of operator (to be used by :func:`pylops.utils.describe.describe`)
+
+    Notes
+    -----
+    The DTCWT operator applies the dual-tree complex wavelet transform
+    in forward mode and the dual-tree complex inverse wavelet transform in adjoint mode
+    from the ``dtcwt`` library.
+
+    The ``dtcwt`` library uses a Pyramid object to represent the signal in the transformed domain,
+    which is composed of:
+        - `lowpass` (coarsest scale lowpass signal);
+        - `highpasses` (complex subband coefficients for corresponding scales);
+        - `scales` (lowpass signal for corresponding scales finest to coarsest).
+
+    To make the dtcwt forward() and inverse() functions compatible with PyLops, in forward model
+    the Pyramid object is flattened out and all coefficients (high-pass and low pass coefficients)
+    are appended into one array using the `_coeff_to_array` method.
+
+    In adjoint mode, the input array is transformed back into a Pyramid object using the `_array_to_coeff`
+    method and then the inverse transform is performed.
+
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, InputDimsLike],
+        biort: str = "near_sym_a",
+        qshift: str = "qshift_a",
+        level: int = 3,
+        include_scale: bool = False,
+        axis: int = -1,
+        dtype: DTypeLike = "float64",
+        name: str = "C",
+    ) -> None:
+        if dtcwt_message is not None:
+            raise NotImplementedError(dtcwt_message)
+
+        dims = _value_or_sized_to_tuple(dims)
+        self.ndim = len(dims)
+        self.axis = axis
+
+        self.otherdims = int(np.prod(dims) / dims[self.axis])
+        self.dims_swapped = list(dims)
+        self.dims_swapped[0], self.dims_swapped[self.axis] = (
+            self.dims_swapped[self.axis],
+            self.dims_swapped[0],
+        )
+        self.dims_swapped = tuple(self.dims_swapped)
+        self.level = level
+        self.include_scale = include_scale
+
+        # dry-run of transform to find dimensions of coefficients at different levels
+        self._transform = dtcwt.Transform1d(biort=biort, qshift=qshift)
+        self._interpret_coeffs(dims, self.axis)
+
+        dimsd = list(dims)
+        dimsd[self.axis] = self.coeff_array_size
+        self.dimsd_swapped = list(dimsd)
+        self.dimsd_swapped[0], self.dimsd_swapped[self.axis] = (
+            self.dimsd_swapped[self.axis],
+            self.dimsd_swapped[0],
+        )
+        self.dimsd_swapped = tuple(self.dimsd_swapped)
+        dimsd = tuple(
+            [
+                2,
+            ]
+            + dimsd
+        )
+
+        super().__init__(
+            dtype=np.dtype(dtype),
+            clinear=False,
+            dims=dims,
+            dimsd=dimsd,
+            name=name,
+        )
+
+    def _interpret_coeffs(self, dims, axis):
+        x = np.ones(dims[axis])
+        pyr = self._transform.forward(
+            x, nlevels=self.level, include_scale=self.include_scale
+        )
+        self.lowpass_size = pyr.lowpass.size
+        self.coeff_array_size = self.lowpass_size
+        self.highpass_sizes = []
+        for _h in pyr.highpasses:
+            self.highpass_sizes.append(_h.size)
+            self.coeff_array_size += _h.size
+
+    def _nd_to_2d(self, arr_nd):
+        arr_2d = arr_nd.reshape(self.dims[self.axis], -1).squeeze()
+        return arr_2d
+
+    def _coeff_to_array(self, pyr: dtcwt.Pyramid) -> NDArray:
+        highpass_coeffs = np.vstack([h for h in pyr.highpasses])
+        coeffs = np.concatenate((highpass_coeffs, pyr.lowpass), axis=0)
+        return coeffs
+
+    def _array_to_coeff(self, X: NDArray) -> dtcwt.Pyramid:
+        lowpass = (X[-self.lowpass_size :].real).reshape((-1, self.otherdims))
+        _ptr = 0
+        highpasses = ()
+        for _sl in self.highpass_sizes:
+            _h = X[_ptr : _ptr + _sl]
+            _ptr += _sl
+            _h = _h.reshape(-1, self.otherdims)
+            highpasses += (_h,)
+        return dtcwt.Pyramid(lowpass, highpasses)
+
+    def get_pyramid(self, x: NDArray) -> dtcwt.Pyramid:
+        """Return Pyramid object from flat real-valued array"""
+        return self._array_to_coeff(x[0] + 1j * x[1])
+
+    @reshaped
+    def _matvec(self, x: NDArray) -> NDArray:
+        x = x.swapaxes(self.axis, 0)
+        y = self._nd_to_2d(x)
+        y = self._coeff_to_array(
+            self._transform.forward(
+                y, nlevels=self.level, include_scale=self.include_scale
+            )
+        )
+        y = y.reshape(self.dimsd_swapped)
+        y = y.swapaxes(self.axis, 0)
+        y = np.concatenate([y.real[np.newaxis], y.imag[np.newaxis]])
+        return y
+
+    @reshaped
+    def _rmatvec(self, x: NDArray) -> NDArray:
+        x = x[0] + 1j * x[1]
+        x = x.swapaxes(self.axis, 0)
+        y = self._transform.inverse(self._array_to_coeff(x))
+        y = y.reshape(self.dims_swapped)
+        y = y.swapaxes(self.axis, 0)
+        return y

pylops/utils/deps.py

@@ -2,6 +2,7 @@
     "cupy_enabled",
     "cusignal_enabled",
     "devito_enabled",
+    "dtcwt_enabled",
     "numba_enabled",
     "pyfftw_enabled",
     "pywt_enabled",
@@ -22,6 +23,7 @@
     util.find_spec("cusignal") is not None and int(os.getenv("CUSIGNAL_PYLOPS", 1)) == 1
 )
 devito_enabled = util.find_spec("devito") is not None
+dtcwt_enabled = util.find_spec("dtcwt") is not None
 numba_enabled = util.find_spec("numba") is not None
 pyfftw_enabled = util.find_spec("pyfftw") is not None
 pywt_enabled = util.find_spec("pywt") is not None
@@ -49,6 +51,23 @@ def devito_import(message):
     return devito_message
 
 
+def dtcwt_import(message):
+    if dtcwt_enabled:
+        try:
+            import dtcwt  # noqa: F401
+
+            dtcwt_message = None
+        except Exception as e:
+            dtcwt_message = f"Failed to import dtcwt (error:{e})."
+    else:
+        dtcwt_message = (
+            f"Dtcwt not available. "
+            f"In order to be able to use "
+            f'{message} run "pip install devito".'
+        )
+    return dtcwt_message
+
+
 def numba_import(message):
     if numba_enabled:
         try:

pyproject.toml

@@ -43,6 +43,7 @@ advanced = [
     "PyWavelets",
     "scikit-fmm",
     "spgl1",
+    "dtcwt",
 ]
 
 [tool.setuptools.packages.find]