Replace vesin.torch, as it doesn’t support Windows OS.

tests/test_magnetostatics.py

@@ -1,14 +1,36 @@
 import pytest
 import torch
 from ase.io import read
-from helpers import DIPOLES_TEST_FRAMES
-from vesin.torch import NeighborList
+from helpers import DIPOLES_TEST_FRAMES, neighbor_list
 
 from torchpme.calculators import CalculatorDipole
 from torchpme.potentials import PotentialDipole
 from torchpme.prefactors import eV_A
 
 
+def compute_distance_vectors(
+    positions, neighbor_indices, cell=None, neighbor_shifts=None
+):
+    """Compute pairwise distance vectors."""
+    atom_is = neighbor_indices[:, 0]
+    atom_js = neighbor_indices[:, 1]
+
+    pos_is = positions[atom_is]
+    pos_js = positions[atom_js]
+
+    distance_vectors = pos_js - pos_is
+
+    if cell is not None and neighbor_shifts is not None:
+        shifts = neighbor_shifts.type(cell.dtype)
+        distance_vectors += shifts @ cell
+    elif cell is not None and neighbor_shifts is None:
+        raise ValueError("Provided `cell` but no `neighbor_shifts`.")
+    elif cell is None and neighbor_shifts is not None:
+        raise ValueError("Provided `neighbor_shifts` but no `cell`.")
+
+    return distance_vectors
+
+
 class System:
     def __init__(self):
         self.cell = torch.tensor(
@@ -122,21 +144,30 @@ def test_magnetostatic_ewald_crystal(frame, cutoff, alpha, energy, force):
         dtype=torch.float64,
         prefactor=eV_A,
     )
-    positions = torch.tensor(
-        frame.get_positions(), requires_grad=True, dtype=torch.float64
-    )
+    positions = torch.tensor(frame.get_positions(), dtype=torch.float64)
     dipoles = torch.tensor(frame.get_array("dipoles"), dtype=torch.float64)
     cell = torch.tensor(frame.get_cell().array, dtype=torch.float64)
-    calculator = NeighborList(cutoff=cutoff, full_list=False)
-    p, d = calculator.compute(
-        points=positions, box=cell, periodic=True, quantities="PD"
+    neighbor_indices, neighbor_shifts = neighbor_list(
+        positions=positions,
+        periodic=True,
+        box=cell,
+        cutoff=cutoff,
+        full_neighbor_list=False,
+        neighbor_shifts=True,
+    )
+    positions.requires_grad = True
+    neighbor_distance_vectors = compute_distance_vectors(
+        positions=positions,
+        neighbor_indices=neighbor_indices,
+        cell=cell,
+        neighbor_shifts=neighbor_shifts,
     )
     pot = calc(
         dipoles=dipoles,
         cell=cell,
         positions=positions,
-        neighbor_indices=p,
-        neighbor_vectors=d,
+        neighbor_indices=neighbor_indices,
+        neighbor_vectors=neighbor_distance_vectors,
     )
 
     result = torch.einsum("ij,ij->", pot, dipoles)