Linting

examples/10-tuning.py

@@ -88,9 +88,7 @@
 
 # and this is how long it took to run with these parameters (est.)
 
-timings = TuningTimings(charges, cell, positions, 
-                        cutoff=max_cutoff, 
-                        run_backward=True)
+timings = TuningTimings(charges, cell, positions, cutoff=max_cutoff, run_backward=True)
 estimated_timing = timings(pme)
 
 print(f"""

src/torchpme/utils/tuning/__init__.py

@@ -1,7 +1,7 @@
 import math
+import time
 from typing import Optional
 
-import time
 import torch
 import vesin.torch
 
@@ -121,7 +121,7 @@ def __init__(
 
     def forward(self, *args, **kwargs):
         return self.error(*args, **kwargs)
-    
+
 
 class TuningTimings(torch.nn.Module):
     """Base class for error bounds."""
@@ -172,7 +172,6 @@ def forward(self, calculator: torch.nn.Module):
         Estimate the execution time of a given calculator for the structure
         to be used as benchmark.
         """
-
         for _ in range(self._n_warmup):
             result = calculator.forward(
                 positions=self._positions,
@@ -194,14 +193,14 @@ def forward(self, calculator: torch.nn.Module):
                 positions.requires_grad_(True)
                 cell.requires_grad_(True)
                 charges.requires_grad_(True)
-            execution_time -= time.time()            
+            execution_time -= time.time()
             result = calculator.forward(
                 positions=positions,
                 charges=charges,
                 cell=cell,
                 neighbor_indices=self._neighbor_indices,
                 neighbor_distances=self._neighbor_distances,
-                )
+            )
             value = result.sum()
             if self._run_backward:
                 value.backward(retain_graph=True)
@@ -210,4 +209,4 @@ def forward(self, calculator: torch.nn.Module):
                 torch.cuda.synchronize()
             execution_time += time.time()
 
-        return execution_time / self._n_repeat
+        return execution_time / self._n_repeat

src/torchpme/utils/tuning/grid_search.py

@@ -18,6 +18,7 @@
     _validate_parameters,
 )
 
+
 class GridSearchBase:
     r"""
     Base class for finding the optimal parameters for calculators using a grid search.
@@ -60,9 +61,17 @@ def __init__(
         self.device = charges.device
         self.err_func = self.ErrorBounds(charges, cell, positions)
         self._cell_dimensions = torch.linalg.norm(cell, dim=1)
-        self.time_func = self.Timings(charges, cell, positions, cutoff, 
-                                      neighbor_indices, neighbor_distances, 
-                                      4, 2, True)
+        self.time_func = self.Timings(
+            charges,
+            cell,
+            positions,
+            cutoff,
+            neighbor_indices,
+            neighbor_distances,
+            4,
+            2,
+            True,
+        )
 
         self._prefac = 2 * (charges**2).sum() / math.sqrt(len(positions))
 
@@ -156,4 +165,4 @@ def _timing(self, smearing: float, params: dict):
             **params,
         )
 
-        return self.time_func(calculator)
+        return self.time_func(calculator)