chore: prepare for merge with upstream

Author: cako

examples/plot_symmetrize.py

@@ -75,7 +75,7 @@
 
 
 x = np.outer(np.ones(nt), np.arange(nx))
-Sop = pylops.Symmetrize(dims=(nt, nx), axis=1)
+Sop = pylops.Symmetrize((nt, nx), axis=1)
 
 y = Sop * x.ravel()
 xadj = Sop.H * y.ravel()

examples/plot_tvreg.py

@@ -148,10 +148,10 @@
 
 Dop = [
     pylops.FirstDerivative(
-        dims=(ny, nx), axis=0, edge=False, kind="backward", dtype=np.complex128
+        (ny, nx), axis=0, edge=False, kind="backward", dtype=np.complex128
     ),
     pylops.FirstDerivative(
-        dims=(ny, nx), axis=1, edge=False, kind="backward", dtype=np.complex128
+        (ny, nx), axis=1, edge=False, kind="backward", dtype=np.complex128
     ),
 ]
 

pylops/signalprocessing/FFT2D.py

@@ -237,7 +237,7 @@ def FFT2D(
     the adjoint is multiplied by :math:`1 / \sqrt{2}` for the same frequencies.
 
     For a real valued input signal, it is advised to use the flag ``real=True``
-    as it stores the values of the Fourier transform of the last direction at positive
+    as it stores the values of the Fourier transform of the last axis in ``axes`` at positive
     frequencies only as values at negative frequencies are simply their complex conjugates.
 
     Parameters
@@ -255,15 +255,15 @@ def FFT2D(
             instead of (0, 1) which was the default for ``dirs``.
 
     nffts : :obj:`tuple` or :obj:`int`, optional
-        Number of samples in Fourier Transform for each direction. In case only one
+        Number of samples in Fourier Transform for each axis in ``axes``. In case only one
         dimension needs to be specified, use ``None`` for the other dimension in the
-        tuple. The direction with None will use ``(dims[axes[0]], dims[axes[1]])``
-        as ``nffts``. When supplying a tuple, the order must agree with that
-        of ``axes``. When a single value is passed, it will be used for both
-        directions. As such the default is equivalent to ``nffts=(None, None)``.
+        tuple. An axis with ``None`` will use ``dims[axis]`` as ``nfft``.
+        When supplying a tuple, the length must be 2.
+        When a single value is passed, it will be used for both
+        axes. As such the default is equivalent to ``nffts=(None, None)``.
     sampling : :obj:`tuple` or :obj:`float`, optional
-        Sampling steps for each direction. When supplied a single value, it is used
-        for both directions. Unlike ``nffts``, any ``None`` will not be converted to the
+        Sampling steps for each axis in ``axes``. When supplied a single value, it is used
+        for both axes. Unlike ``nffts``, any ``None`` will not be converted to the
         default value.
     norm : `{"ortho", "none", "1/n"}`, optional
         .. versionadded:: 1.17.0
@@ -293,15 +293,15 @@ def FFT2D(
         coincide with the zero index sample. With such an arrangement, FFT will not
         introduce a sample-dependent phase-shift when compared to the continuous Fourier
         Transform.
-        When passing a single value, the shift will the same for every direction. Pass
+        When passing a single value, the shift will the same for every axis in ``axes``. Pass
         a tuple to specify which dimensions are shifted.
     fftshift_after : :obj:`tuple` or :obj:`bool`, optional
         Apply fftshift (``True``) or not (``False``) to data vector (after FFT).
         Consider using this option when you require frequencies to be arranged
         naturally, from negative to positive. When not applying fftshift after FFT,
         frequencies are arranged from zero to largest positive, and then from negative
         Nyquist to the frequency bin before zero.
-        When passing a single value, the shift will the same for every direction. Pass
+        When passing a single value, the shift will the same for every axis in ``axes``. Pass
         a tuple to specify which dimensions are shifted.
     engine : :obj:`str`, optional
         .. versionadded:: 1.17.0

pylops/signalprocessing/FFTND.py

@@ -84,9 +84,9 @@ def _rmatvec(self, x):
             y = np.fft.ifftn(x, s=self.nffts, axes=self.axes, **self._norm_kwargs)
         if self.norm is _FFTNorms.NONE:
             y *= self._scale
-        for direction, nfft in zip(self.axes, self.nffts):
-            if nfft > self.dims[direction]:
-                y = np.take(y, range(self.dims[direction]), axis=direction)
+        for ax, nfft in zip(self.axes, self.nffts):
+            if nfft > self.dims[ax]:
+                y = np.take(y, range(self.dims[ax]), axis=ax)
         if self.doifftpad:
             y = np.pad(y, self.ifftpad)
         if not self.clinear:
@@ -172,9 +172,9 @@ def _rmatvec(self, x):
             y = scipy.fft.ifftn(x, s=self.nffts, axes=self.axes, **self._norm_kwargs)
         if self.norm is _FFTNorms.NONE:
             y *= self._scale
-        for direction, nfft in zip(self.axes, self.nffts):
-            if nfft > self.dims[direction]:
-                y = np.take(y, range(self.dims[direction]), axis=direction)
+        for ax, nfft in zip(self.axes, self.nffts):
+            if nfft > self.dims[ax]:
+                y = np.take(y, range(self.dims[ax]), axis=ax)
         if self.doifftpad:
             y = np.pad(y, self.ifftpad)
         if not self.clinear:
@@ -222,7 +222,7 @@ def FFTND(
     :math:`1 / \sqrt{2}` for the same frequencies.
 
     For a real valued input signal, it is advised to use the flag ``real=True``
-    as it stores the values of the Fourier transform of the last direction at positive
+    as it stores the values of the Fourier transform of the last axis in ``axes`` at positive
     frequencies only as values at negative frequencies are simply their complex conjugates.
 
     Parameters
@@ -240,12 +240,12 @@ def FFTND(
             instead of (0, 1, 2) which was the default for ``dirs``.
 
     nffts : :obj:`tuple` or :obj:`int`, optional
-        Number of samples in Fourier Transform for each direction. In case only one
+        Number of samples in Fourier Transform for each axis in ``axes``. In case only one
         dimension needs to be specified, use ``None`` for the other dimension in the
-        tuple. The direction with None will use ``dims[axis]`` for each ``axis`` in
-        ``axes`` as ``nffts``. When supplying a tuple, the order must agree with that
-        of ``axes``. When a single value is passed, it will be used for both
-        directions. As such the default is equivalent to ``nffts=(None, ..., None)``.
+        tuple. An axis with ``None`` will use ``dims[axis]`` as ``nfft``.
+        When supplying a tuple, the length must agree with that
+        of ``axes``. When a single value is passed, it will be used for all
+        ``axes`. As such the default is equivalent to ``nffts=(None, ..., None)``.
     sampling : :obj:`tuple` or :obj:`float`, optional
         Sampling steps for each direction. When supplied a single value, it is used
         for all directions. Unlike ``nffts``, any ``None`` will not be converted to the

pylops/utils/describe.py

@@ -235,7 +235,7 @@ def _describe(Op):
 
 
 def describe(Op):
-    r"""Describe a PyLops operator
+    """Describe a PyLops operator
 
     .. versionadded:: 1.17.0
 

pylops/waveeqprocessing/marchenko.py

@@ -245,7 +245,7 @@ def __init__(
             "even when R is provided in frequency domain. It is "
             "recommended to start using the operator without the R1 "
             "input as this behaviour will become default in "
-            "version v2.0 and R1 will be removed from the inputs.",
+            "version v2.0.0 and R1 will be removed from the inputs.",
             FutureWarning,
         )
         # Save inputs into class