Merge branch 'master' into reduce-warnings

docs/source/installation.rst

@@ -139,6 +139,19 @@ To ensure that everything has been setup correctly, run tests:
 
 Make sure no tests fail, this guarantees that the installation has been successfull.
 
+Finally, to ensure consistency in the coding style of our developers we rely on
+`pre-commit <https://pre-commit.com>`_ to perform a series of checks when you are
+ready to commit and push some changes. This is accomplished by means of git hooks
+that have been configured in the ``.pre-commit-config.yaml`` file.
+
+In order to setup such hooks in your local repository, run:
+
+.. code-block:: bash
+
+    >> pre-commit install
+
+Later on, ``pre-commit`` will automatically run for you and propose additional stylistic changes to your commits.
+
 If using Conda environment, always remember to activate the conda environment every time you open
 a new *bash* shell by typing:
 

examples/plot_causalintegration.py

@@ -87,14 +87,14 @@
 
 # Visualize data and inversion
 fig, axs = plt.subplots(1, 2, figsize=(18, 5))
-axs[0].plot(t, y, "k", LineWidth=3, label="data")
-axs[0].plot(t, yn, "--g", LineWidth=3, label="noisy data")
+axs[0].plot(t, y, "k", lw=3, label="data")
+axs[0].plot(t, yn, "--g", lw=3, label="noisy data")
 axs[0].legend()
 axs[0].set_title("Causal integration")
-axs[1].plot(t, x, "k", LineWidth=8, label="original")
-axs[1].plot(t[1:-1], xder[1:-1], "r", LineWidth=3, label="numerical derivative")
-axs[1].plot(t, xreg, "g", LineWidth=3, label="regularized")
-axs[1].plot(t, xp, "m", LineWidth=3, label="preconditioned")
+axs[1].plot(t, x, "k", lw=8, label="original")
+axs[1].plot(t[1:-1], xder[1:-1], "r", lw=3, label="numerical derivative")
+axs[1].plot(t, xreg, "g", lw=3, label="regularized")
+axs[1].plot(t, xp, "m", lw=3, label="preconditioned")
 axs[1].legend()
 axs[1].set_title("Inverse causal integration")
 
@@ -163,13 +163,13 @@
 
 # Visualize data and inversion at a chosen xlocation
 fig, axs = plt.subplots(1, 2, figsize=(18, 5))
-axs[0].plot(t, y[:, nx // 2], "k", LineWidth=3, label="data")
-axs[0].plot(t, yn[:, nx // 2], "--g", LineWidth=3, label="noisy data")
+axs[0].plot(t, y[:, nx // 2], "k", lw=3, label="data")
+axs[0].plot(t, yn[:, nx // 2], "--g", lw=3, label="noisy data")
 axs[0].legend()
 axs[0].set_title("Causal integration")
-axs[1].plot(t, x[:, nx // 2], "k", LineWidth=8, label="original")
-axs[1].plot(t, xder[:, nx // 2], "r", LineWidth=3, label="numerical derivative")
-axs[1].plot(t, xreg[:, nx // 2], "g", LineWidth=3, label="regularized")
-axs[1].plot(t, xp[:, nx // 2], "m", LineWidth=3, label="preconditioned")
+axs[1].plot(t, x[:, nx // 2], "k", lw=8, label="original")
+axs[1].plot(t, xder[:, nx // 2], "r", lw=3, label="numerical derivative")
+axs[1].plot(t, xreg[:, nx // 2], "g", lw=3, label="regularized")
+axs[1].plot(t, xp[:, nx // 2], "m", lw=3, label="preconditioned")
 axs[1].legend()
 axs[1].set_title("Inverse causal integration")

pylops/signalprocessing/FFT.py

@@ -75,7 +75,9 @@ def __init__(
         if self.fftshift_after:
             if self.real:
                 warnings.warn(
-                    "Using fftshift_after with real=True. fftshift should only be applied after a complex FFT. This is rarely intended behavior but if it is, ignore this message."
+                    "Using fftshift_after with real=True. fftshift should only be "
+                    "applied after a complex FFT. This is rarely intended behavior "
+                    "but if it is, ignore this message."
                 )
             self.f = np.fft.fftshift(self.f)
 
@@ -236,8 +238,10 @@ def __init__(
             fftshift_after,
             dtype,
         )
-        if len(dims) == 1:
-            self.dims = np.array((dims[0],))
+        if isinstance(dims, int) or len(dims) == 1:
+            self.dims = (
+                np.array((dims,)) if isinstance(dims, int) else np.array((dims[0],))
+            )
             self.dims_t = self.dims.copy()
             self.dims_t[self.dir] = self.nfft
             self.dims_fft = self.dims.copy()

pylops/signalprocessing/Radon2D.py

@@ -7,14 +7,9 @@
 try:
     from numba import jit
 
-    from ._Radon2D_numba import (
-        _create_table_numba,
-        _hyperbolic_numba,
-        _indices_2d_numba,
-        _indices_2d_onthefly_numba,
-        _linear_numba,
-        _parabolic_numba,
-    )
+    from ._Radon2D_numba import (_create_table_numba, _hyperbolic_numba,
+                                 _indices_2d_numba, _indices_2d_onthefly_numba,
+                                 _linear_numba, _parabolic_numba)
 except ModuleNotFoundError:
     jit = None
 
@@ -225,9 +220,10 @@ def Radon2D(
     dh, dt = np.abs(haxis[1] - haxis[0]), np.abs(taxis[1] - taxis[0])
     dpx = dh / dt
     pxaxis = pxaxis * dpx
-    haxisunitless = haxis // dh
-    if centeredh:
-        haxisunitless -= nh // 2
+    if not centeredh:
+        haxisunitless = haxis // dh
+    else:
+        haxisunitless = np.arange(nh) - nh // 2
     dims = (npx, nt)
     dimsd = (nh, nt)
 

pylops/signalprocessing/Radon3D.py

@@ -7,14 +7,9 @@
 try:
     from numba import jit
 
-    from ._Radon3D_numba import (
-        _create_table_numba,
-        _hyperbolic_numba,
-        _indices_3d_numba,
-        _indices_3d_onthefly_numba,
-        _linear_numba,
-        _parabolic_numba,
-    )
+    from ._Radon3D_numba import (_create_table_numba, _hyperbolic_numba,
+                                 _indices_3d_numba, _indices_3d_onthefly_numba,
+                                 _linear_numba, _parabolic_numba)
 except ModuleNotFoundError:
     jit = None
 
@@ -236,13 +231,14 @@ def Radon3D(
     dt = np.abs(taxis[1] - taxis[0])
     dpy = dhy / dt
     pyaxis = pyaxis * dpy
-    hyaxisunitless = hyaxis // dhy
     dpx = dhx / dt
     pxaxis = pxaxis * dpx
-    hxaxisunitless = hxaxis // dhx
-    if centeredh:
-        hyaxisunitless -= nhy // 2
-        hxaxisunitless -= nhx // 2
+    if not centeredh:
+        hyaxisunitless = hyaxis // dhy
+        hxaxisunitless = hxaxis // dhx
+    else:
+        hyaxisunitless = np.arange(nhy) - nhy // 2
+        hxaxisunitless = np.arange(nhx) - nhx // 2
 
     # create grid for py and px axis
     hyaxisunitless, hxaxisunitless = np.meshgrid(

tutorials/solvers.py

@@ -62,10 +62,10 @@
 x = FFTop.H * X
 
 fig, axs = plt.subplots(2, 1, figsize=(12, 8))
-axs[0].plot(f, np.abs(X), "k", LineWidth=2)
+axs[0].plot(f, np.abs(X), "k", lw=2)
 axs[0].set_xlim(0, 30)
 axs[0].set_title("Data(frequency domain)")
-axs[1].plot(t, x, "k", LineWidth=2)
+axs[1].plot(t, x, "k", lw=2)
 axs[1].set_title("Data(time domain)")
 axs[1].axis("tight")