a few minor changes after ruff and after running tests on CPU and GPU

[skip tests]

Author: ar4

docs/example_custom_imaging_condition.py

@@ -38,7 +38,9 @@
 peak_time = 1.5 / freq
 
 # Smooth to use as starting model
-v = torch.tensor(1 / gaussian_filter(1 / v_true.numpy(), 40)).to(device)
+v = torch.tensor(1 / gaussian_filter(1 / v_true.cpu().numpy(), 40)).to(
+    device
+)
 v.requires_grad_()
 
 # source_locations

docs/example_elastic.py

@@ -111,7 +111,7 @@ def closure():
         )[-2]
         loss = 1e20 * loss_fn(out, observed_data)
         loss.backward()
-        return loss
+        return loss.item()
 
     optimiser.step(closure)
 

docs/example_fwi.py

@@ -18,7 +18,8 @@
 ny_full = 2301
 nx_full = 751
 dx = 4.0
-v_true_full = torch.from_file("marmousi_vp.bin", size=ny_full * nx_full).reshape(ny_full, nx_full)
+v_true_full = torch.from_file("marmousi_vp.bin", size=ny_full * nx_full)
+v_true_full = v_true_full.reshape(ny_full, nx_full)
 
 # Select portion of model for inversion
 ny = 600

docs/example_gsot.py

@@ -40,9 +40,7 @@ def gsot(y_pred: torch.Tensor, y: torch.Tensor, eta: float):
                 loss
                 + (
                     eta
-                    * torch.tensor(
-                        row_ind - col_ind, device=y_pred.device
-                    )
+                    * torch.tensor(row_ind - col_ind, device=y_pred.device)
                     ** 2
                     + (y_pred[s, r] - y_sigma) ** 2
                 ).sum()

docs/example_hessian.py

@@ -32,13 +32,17 @@
 source_locations = torch.tensor(
     [[[1, 15]]], dtype=torch.long, device=device
 )
-source_amplitudes = deepwave.wavelets.ricker(
-    freq,
-    nt,
-    dt,
-    1.3 / freq,
-    dtype=dtype,
-).reshape(1, 1, -1).to(device)
+source_amplitudes = (
+    deepwave.wavelets.ricker(
+        freq,
+        nt,
+        dt,
+        1.3 / freq,
+        dtype=dtype,
+    )
+    .reshape(1, 1, -1)
+    .to(device)
+)
 receiver_locations = torch.ones(
     1, nx - 20, 2, dtype=torch.long, device=device
 )
@@ -125,7 +129,8 @@ def wrap(v):
     )[0].item()
     tau = max(-1.5 * eig0, 0)
     L = torch.linalg.cholesky(
-        hess.reshape(v.numel(), v.numel()) + tau * torch.eye(v.numel(), dtype=v.dtype, device=v.device),
+        hess.reshape(v.numel(), v.numel())
+        + tau * torch.eye(v.numel(), dtype=v.dtype, device=v.device),
     )
     h = torch.cholesky_solve(grad.reshape(-1, 1).neg(), L).reshape(ny, nx)
     v = (v.detach() + h).requires_grad_()

docs/example_location_interpolation.py

@@ -99,7 +99,7 @@
     dt,
     source_amplitudes=source_amplitudes,
     source_locations=source_locations.long(),
-    receiver_locations=receiver_locations.long(),
+    receiver_locations=receiver_locations[:, :1].long(),
     pml_freq=freq,
 )
 
@@ -110,7 +110,7 @@
     dt,
     source_amplitudes=source_amplitudes * 4,
     source_locations=(source_locations * 2).long(),
-    receiver_locations=(receiver_locations * 2).long(),
+    receiver_locations=(receiver_locations * 2)[:, :1].long(),
     pml_freq=freq,
 )
 

docs/example_lsrtm.py

@@ -15,7 +15,8 @@
 ny_full = 2301
 nx_full = 751
 dx = 4.0
-v_true_full = torch.from_file("marmousi_vp.bin", size=ny_full * nx_full).reshape(ny_full, nx_full)
+v_true_full = torch.from_file("marmousi_vp.bin", size=ny_full * nx_full)
+v_true_full = v_true_full.reshape(ny_full, nx_full)
 
 # Select portion of model for inversion
 ny = 600

docs/example_target_wavefield.py

@@ -83,7 +83,7 @@ def closure():
 
 (source_amplitudes.detach().cpu().numpy().tofile("source_amplitudes.bin"))
 
-target_abs_max = target.abs().max()
+target_abs_max = target.abs().max().item()
 
 
 def forward_callback(state):

docs/index.rst

@@ -32,7 +32,7 @@ This command forces pip to download and compile the source distribution on your
 Support
 -------
 
-If you encounter any issues (e.g., typos, broken links, unexpected behaviour), or have suggestions for improvement, please `file an issue on GitHub <https://github.com/ar4/deepwave/issues>`_ or `send me an email <mailto:alan@ausargeo.com>`_. Your feedback is highly appreciated.
+If you encounter any issues (e.g., typos, broken links, unexpected behaviour, examples that don't work), or have suggestions for improvement, please `file an issue on GitHub <https://github.com/ar4/deepwave/issues>`_ or `send me an email <mailto:alan@ausargeo.com>`_. Your feedback is highly appreciated.
 
 .. toctree::
    :maxdepth: 2

src/deepwave/location_interpolation.py

@@ -51,7 +51,7 @@ def _get_hicks_for_one_location_dim(
         or not all(isinstance(f, (int, float)) for f in free_surface_loc)
     ):
         raise RuntimeError("extent must be a list of two floats.")
-    if size <= 0:
+    if (free_surface[0] or free_surface[1]) and size <= 0:
         raise RuntimeError("n_grid_points must be positive.")
     if monopole and abs(location - round(location)) < eps:
         locations = torch.tensor([location]).round().long().to(beta.device)