stricter dead link checks: swap markdown-link-check for lychee

.github/workflows/link-check.yml

@@ -6,18 +6,17 @@ on:
   pull_request:
     branches: [main]
 
-concurrency:
-  group: ${{ github.workflow }}-pr-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
 jobs:
-  markdown-link-check:
+  link-check:
     runs-on: ubuntu-latest
     steps:
       - name: Check out repo
         uses: actions/checkout@v4
 
-      - name: Run markdown link check
-        uses: gaurav-nelson/github-action-markdown-link-check@v1
+      - name: Discover broken links
+        uses: lycheeverse/lychee-action@v2
         with:
-          config-file: .github/workflows/link-check-config.json
+          # ignore ipynb links since they're generated on the fly
+          args: --exclude-path dist --exclude '\.ipynb$' --accept 100..=103,200..=299,403,429,500 -- ./**/*.{md,py,yml,json}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.pre-commit-config.yaml

@@ -11,18 +11,14 @@ repos:
     hooks:
       - id: ruff
         args: [--fix]
-        types_or: [python, jupyter]
       - id: ruff-format
-        types_or: [python, jupyter]
-        exclude: \.pyx$
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
       - id: check-case-conflict
       - id: check-symlinks
       - id: check-yaml
-        exclude: .*\/copilot\/.*
       - id: destroyed-symlinks
       - id: end-of-file-fixer
       - id: forbid-new-submodules
@@ -34,7 +30,6 @@ repos:
     hooks:
       - id: codespell
         stages: [pre-commit, commit-msg]
-        args: [--ignore-words-list, "statics,atomate,nd,te,titel,coo,slite,fro"]
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
     rev: v0.44.0

examples/scripts/7_Others/7.3_Batched_neighbor_list.py

@@ -11,8 +11,8 @@
 from ase.build import bulk
 
 import torch_sim as ts
+from torch_sim import transforms
 from torch_sim.neighbors import torch_nl_linked_cell, torch_nl_n2
-from torch_sim.transforms import compute_cell_shifts, compute_distances_with_cell_shifts
 
 
 atoms_list = [bulk("Si", "diamond", a=5.43), bulk("Ge", "diamond", a=5.65)]
@@ -28,8 +28,8 @@
 mapping, mapping_batch, shifts_idx = torch_nl_linked_cell(
     cutoff, pos, cell, pbc_tensor, batch, self_interaction
 )
-cell_shifts = compute_cell_shifts(cell, shifts_idx, mapping_batch)
-dds = compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
+cell_shifts = transforms.compute_cell_shifts(cell, shifts_idx, mapping_batch)
+dds = transforms.compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
 
 print(mapping.shape)
 print(mapping_batch.shape)
@@ -40,8 +40,8 @@
 mapping_n2, mapping_batch_n2, shifts_idx_n2 = torch_nl_n2(
     cutoff, pos, cell, pbc_tensor, batch, self_interaction
 )
-cell_shifts_n2 = compute_cell_shifts(cell, shifts_idx_n2, mapping_batch_n2)
-dds_n2 = compute_distances_with_cell_shifts(pos, mapping_n2, cell_shifts_n2)
+cell_shifts_n2 = transforms.compute_cell_shifts(cell, shifts_idx_n2, mapping_batch_n2)
+dds_n2 = transforms.compute_distances_with_cell_shifts(pos, mapping_n2, cell_shifts_n2)
 
 print(mapping_n2.shape)
 print(mapping_batch_n2.shape)

examples/scripts/readme.md

@@ -26,9 +26,11 @@ This folder contains a series of examples demonstrating the use of TorchSim, a l
 
    1. **Batched MACE Gradient Descent** - [`examples/2_Structural_optimization/2.6_Batched_MACE_Gradient_Descent.py`](2_Structural_optimization/2.6_Batched_MACE_Gradient_Descent.py): Optimize multiple structures simultaneously using batched gradient descent with the MACE model.
 
-   1. **Batched MACE UnitCellFilter Gradient Descent** - [`examples/2_Structural_optimization/2.7_Batched_MACE_UnitCellFilter_Gradient_Descent.py`](2_Structural_optimization/2.7_Batched_MACE_UnitCellFilter_Gradient_Descent.py): Optimize multiple structures and their unit cells using batched gradient descent with MACE.
+   1. **Batched MACE FIRE** - [`examples/2_Structural_optimization/2.7_Batched_MACE_FIRE.py`](2_Structural_optimization/2.7_Batched_MACE_FIRE.py): Optimize multiple structures simultaneously using the batched FIRE optimizer with MACE.
 
-   1. **Batched MACE UnitCellFilter FIRE** - [`examples/2_Structural_optimization/2.8_Batched_MACE_UnitCellFilter_FIRE.py`](2_Structural_optimization/2.8_Batched_MACE_UnitCellFilter_FIRE.py): Optimize multiple structures and their unit cells using the batched FIRE optimizer with MACE.
+   1. **Batched MACE UnitCellFilter Gradient Descent** - [`examples/2_Structural_optimization/2.8_Batched_MACE_UnitCellFilter_Gradient_Descent.py`](2_Structural_optimization/2.8_Batched_MACE_UnitCellFilter_Gradient_Descent.py): Optimize multiple structures and their unit cells using batched gradient descent with MACE.
+
+   1. **Batched MACE UnitCellFilter FIRE** - [`examples/2_Structural_optimization/2.9_Batched_MACE_UnitCellFilter_FIRE.py`](2_Structural_optimization/2.9_Batched_MACE_UnitCellFilter_FIRE.py): Optimize multiple structures and their unit cells using the batched FIRE optimizer with MACE.
 
 1. **Dynamics**
 

examples/tutorials/using_graphpes_tutorial.py

@@ -15,7 +15,7 @@
 # Integrating TorchSim with `graph-pes`
 
 This brief tutorial demonstrates how to use models trained with the
-[graph-pes](https://github.com/mir-group/graph-pes) package to drive
+[graph-pes](https://github.com/jla-gardner/graph-pes) package to drive
 MD simulations and geometry optimizations in TorchSim.
 
 ## Step 1: loading a model

tests/models/test_fairchem.py

@@ -81,7 +81,7 @@ def ocp_calculator(model_path_oc20: str) -> OCPCalculator:
 
 
 # Skip this test due to issues with how the older models
-# handled supercells (see related issue here: https://github.com/FAIR-Chem/fairchem/issues/428)
+# handled supercells (see related issue here: https://github.com/facebookresearch/fairchem/issues/428)
 
 test_fairchem_ocp_model_outputs = pytest.mark.skipif(
     os.environ.get("HF_TOKEN") is None,

tests/test_neighbors.py

@@ -8,8 +8,7 @@
 from ase.build import bulk, molecule
 from ase.neighborlist import neighbor_list
 
-from torch_sim import neighbors
-from torch_sim.transforms import compute_cell_shifts, compute_distances_with_cell_shifts
+from torch_sim import neighbors, transforms
 
 
 @pytest.fixture
@@ -207,7 +206,9 @@ def test_primitive_neighbor_list(
 
         # Calculate distances with cell shifts
         cell_shifts_prim = torch.mm(shifts_tensor, row_vector_cell)
-        dds_prim = compute_distances_with_cell_shifts(pos, mapping, cell_shifts_prim)
+        dds_prim = transforms.compute_distances_with_cell_shifts(
+            pos, mapping, cell_shifts_prim
+        )
         dds_prim = np.sort(dds_prim.numpy())
 
         # Get the neighbor list from ase
@@ -229,7 +230,9 @@ def test_primitive_neighbor_list(
 
         # Calculate distances with cell shifts
         cell_shifts_ref = torch.mm(shifts_ref, row_vector_cell)
-        dds_ref = compute_distances_with_cell_shifts(pos, mapping_ref, cell_shifts_ref)
+        dds_ref = transforms.compute_distances_with_cell_shifts(
+            pos, mapping_ref, cell_shifts_ref
+        )
 
         # Sort the distances
         dds_ref = np.sort(dds_ref.numpy())
@@ -280,7 +283,7 @@ def test_neighbor_list_implementations(
 
         # Calculate distances with cell shifts
         cell_shifts = torch.mm(shifts, row_vector_cell)
-        dds = compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
+        dds = transforms.compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
         dds = np.sort(dds.numpy())
 
         # Get the reference neighbor list from ASE
@@ -300,7 +303,9 @@ def test_neighbor_list_implementations(
             shifts_ref, dtype=torch.float64, device=torch.device("cpu")
         )
         cell_shifts_ref = torch.mm(shifts_ref, row_vector_cell)
-        dds_ref = compute_distances_with_cell_shifts(pos, mapping_ref, cell_shifts_ref)
+        dds_ref = transforms.compute_distances_with_cell_shifts(
+            pos, mapping_ref, cell_shifts_ref
+        )
         dds_ref = np.sort(dds_ref.numpy())
         dist_ref = np.sort(dist)
 
@@ -342,8 +347,10 @@ def test_torch_nl_implementations(
     )
 
     # Calculate distances
-    cell_shifts = compute_cell_shifts(row_vector_cell, shifts_idx, mapping_batch)
-    dds = compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
+    cell_shifts = transforms.compute_cell_shifts(
+        row_vector_cell, shifts_idx, mapping_batch
+    )
+    dds = transforms.compute_distances_with_cell_shifts(pos, mapping, cell_shifts)
     dds = np.sort(dds.numpy())
 
     # Get reference results from ASE