Merge branch 'feature-jax' of https://github.com/mrava87/pylops into feature-jax

Author: mrava87

pylops/basicoperators/firstderivative.py

@@ -102,30 +102,13 @@ def __init__(
         self.order = order
         self.slice = {
             i: {
-                j: tuple(
-                    [
-                        slice(None, None),
-                    ]
-                    * (len(dims) - 1)
-                    + [
-                        slice(i, j),
-                    ]
-                )
+                j: tuple([slice(None, None)] * (len(dims) - 1) + [slice(i, j)])
                 for j in (None, -1, -2, -3, -4)
             }
             for i in (None, 1, 2, 3, 4)
         }
         self.sample = {
-            i: tuple(
-                [
-                    slice(None, None),
-                ]
-                * (len(dims) - 1)
-                + [
-                    i,
-                ]
-            )
-            for i in range(-3, 4)
+            i: tuple([slice(None, None)] * (len(dims) - 1) + [i]) for i in range(-3, 4)
         }
         self._register_multiplications(self.kind, self.order)
 

pylops/basicoperators/secondderivative.py

@@ -92,30 +92,13 @@ def __init__(
         self.edge = edge
         self.slice = {
             i: {
-                j: tuple(
-                    [
-                        slice(None, None),
-                    ]
-                    * (len(dims) - 1)
-                    + [
-                        slice(i, j),
-                    ]
-                )
+                j: tuple([slice(None, None)] * (len(dims) - 1) + [slice(i, j)])
                 for j in (None, -1, -2, -3, -4)
             }
             for i in (None, 1, 2, 3, 4)
         }
         self.sample = {
-            i: tuple(
-                [
-                    slice(None, None),
-                ]
-                * (len(dims) - 1)
-                + [
-                    i,
-                ]
-            )
-            for i in range(-3, 4)
+            i: tuple([slice(None, None)] * (len(dims) - 1) + [i]) for i in range(-3, 4)
         }
         self._register_multiplications(self.kind)
 

pylops/basicoperators/symmetrize.py

@@ -80,32 +80,12 @@ def __init__(
         self.nsym = dims[self.axis]
         dimsd = list(dims)
         dimsd[self.axis] = 2 * dims[self.axis] - 1
-        self.slice1 = tuple(
-            [
-                slice(None, None),
-            ]
-            * (len(dims) - 1)
-            + [
-                slice(1, None),
-            ]
-        )
+        self.slice1 = tuple([slice(None, None)] * (len(dims) - 1) + [slice(1, None)])
         self.slicensym_1 = tuple(
-            [
-                slice(None, None),
-            ]
-            * (len(dims) - 1)
-            + [
-                slice(self.nsym - 1, None),
-            ]
+            [slice(None, None)] * (len(dims) - 1) + [slice(self.nsym - 1, None)]
         )
         self.slice_nsym_1 = tuple(
-            [
-                slice(None, None),
-            ]
-            * (len(dims) - 1)
-            + [
-                slice(None, self.nsym - 1),
-            ]
+            [slice(None, None)] * (len(dims) - 1) + [slice(None, self.nsym - 1)]
         )
 
         super().__init__(dtype=np.dtype(dtype), dims=dims, dimsd=dimsd, name=name)

pylops/basicoperators/vstack.py

@@ -12,10 +12,10 @@
     from scipy.sparse.linalg.interface import LinearOperator as spLinearOperator
     from scipy.sparse.linalg.interface import _get_dtype
 else:
-    from scipy.sparse.linalg._interface import _get_dtype
     from scipy.sparse.linalg._interface import (
         LinearOperator as spLinearOperator,
     )
+    from scipy.sparse.linalg._interface import _get_dtype
 
 from typing import Callable, Optional, Sequence
 

pylops/signalprocessing/fft.py

@@ -62,13 +62,8 @@ def __init__(
         elif self.norm is _FFTNorms.ONE_OVER_N:
             self._scale = 1.0 / self.nfft
         self.slice = tuple(
-            [
-                slice(None, None),
-            ]
-            * (len(self.dims) - 1)
-            + [
-                slice(1, 1 + (self.nfft - 1) // 2),
-            ]
+            [slice(None, None)] * (len(self.dims) - 1)
+            + [slice(1, 1 + (self.nfft - 1) // 2)]
         )
 
     @reshaped

pylops/signalprocessing/nonstatconvolve1d.py

@@ -163,13 +163,8 @@ def _matvec(self, x: NDArray) -> NDArray:
             #     x[..., ix : ix + 1] * h[hextremes[0] : hextremes[1]]
             # )
             sl = tuple(
-                [
-                    slice(None, None),
-                ]
-                * (len(self.dimsd) - 1)
-                + [
-                    slice(xextremes[0], xextremes[1]),
-                ]
+                [slice(None, None)] * (len(self.dimsd) - 1)
+                + [slice(xextremes[0], xextremes[1])]
             )
             y = inplace_add(x[..., ix : ix + 1] * h[hextremes[0] : hextremes[1]], y, sl)
         return y
@@ -192,15 +187,7 @@ def _rmatvec(self, x: NDArray) -> NDArray:
             #     h[hextremes[0] : hextremes[1]] * x[..., xextremes[0] : xextremes[1]],
             #     axis=-1,
             # )
-            sl = tuple(
-                [
-                    slice(None, None),
-                ]
-                * (len(self.dimsd) - 1)
-                + [
-                    ix,
-                ]
-            )
+            sl = tuple([slice(None, None)] * (len(self.dimsd) - 1) + [ix])
             y = inplace_set(
                 ncp.sum(
                     h[hextremes[0] : hextremes[1]]
@@ -350,47 +337,19 @@ def _interpolate_hadj(htmp, hs, hextremes, ix, oh, dh, nh):
         ih_closest = int(np.floor((ix - oh) / dh))
         if ih_closest < 0:
             # hs[0, hextremes[0] : hextremes[1]] += htmp
-            sl = tuple(
-                [
-                    0,
-                ]
-                + [
-                    slice(hextremes[0], hextremes[1]),
-                ]
-            )
+            sl = tuple([0] + [slice(hextremes[0], hextremes[1])])
             hs = inplace_add(htmp, hs, sl)
         elif ih_closest >= nh - 1:
             # hs[nh - 1, hextremes[0] : hextremes[1]] += htmp
-            sl = tuple(
-                [
-                    nh - 1,
-                ]
-                + [
-                    slice(hextremes[0], hextremes[1]),
-                ]
-            )
+            sl = tuple([nh - 1] + [slice(hextremes[0], hextremes[1])])
             hs = inplace_add(htmp, hs, sl)
         else:
             dh_closest = (ix - oh) / dh - ih_closest
             # hs[ih_closest, hextremes[0] : hextremes[1]] += (1 - dh_closest) * htmp
-            sl = tuple(
-                [
-                    ih_closest,
-                ]
-                + [
-                    slice(hextremes[0], hextremes[1]),
-                ]
-            )
+            sl = tuple([ih_closest] + [slice(hextremes[0], hextremes[1])])
             hs = inplace_add((1 - dh_closest) * htmp, hs, sl)
             # hs[ih_closest + 1, hextremes[0] : hextremes[1]] += dh_closest * htmp
-            sl = tuple(
-                [
-                    ih_closest + 1,
-                ]
-                + [
-                    slice(hextremes[0], hextremes[1]),
-                ]
-            )
+            sl = tuple([ih_closest + 1] + [slice(hextremes[0], hextremes[1])])
             hs = inplace_add(dh_closest * htmp, hs, sl)
         return hs
 
@@ -412,13 +371,8 @@ def _matvec(self, x: NDArray) -> NDArray:
             #     self.inp[..., ix : ix + 1] * h[hextremes[0] : hextremes[1]]
             # )
             sl = tuple(
-                [
-                    slice(None, None),
-                ]
-                * (len(self.dimsd) - 1)
-                + [
-                    slice(xextremes[0], xextremes[1]),
-                ]
+                [slice(None, None)] * (len(self.dimsd) - 1)
+                + [slice(xextremes[0], xextremes[1])]
             )
             y = inplace_add(
                 self.inp[..., ix : ix + 1] * h[hextremes[0] : hextremes[1]], y, sl