Drop Python 3.9 support

.github/workflows/release.yml

@@ -44,7 +44,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-14, windows-latest]
-        python-version: ["39", "310", "311", "312"]
+        python-version: ["310", "311", "312"]
     runs-on: ${{ matrix.os }}
     steps:
       - name: Check out repo

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.4
+    rev: v0.6.5
     hooks:
       - id: ruff
         args: [--fix]

README.md

@@ -8,7 +8,7 @@
 ![GitHub repo size](https://img.shields.io/github/repo-size/CederGroupHub/chgnet?logo=github&logoColor=white&label=Repo%20Size)
 [![PyPI](https://img.shields.io/pypi/v/chgnet?logo=pypi&logoColor=white)](https://pypi.org/project/chgnet?logo=pypi&logoColor=white)
 [![Docs](https://img.shields.io/badge/API-Docs-blue?logo=readthedocs&logoColor=white)](https://chgnet.lbl.gov)
-[![Requires Python 3.9+](https://img.shields.io/badge/Python-3.9+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)
+[![Requires Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)
 
 </h4>
 

chgnet/graph/converter.py

@@ -215,7 +215,9 @@ def _create_graph_legacy(
             Graph data structure used to create Crystal_Graph object
         """
         graph = Graph([Node(index=idx) for idx in range(n_atoms)])
-        for ii, jj, img, dist in zip(center_index, neighbor_index, image, distance):
+        for ii, jj, img, dist in zip(
+            center_index, neighbor_index, image, distance, strict=True
+        ):
             graph.add_edge(center_index=ii, neighbor_index=jj, image=img, distance=dist)
 
         return graph

chgnet/graph/graph.py

@@ -309,7 +309,7 @@ def line_graph_adjacency_list(self, cutoff) -> tuple[list[list[int]], list[int]]
             # We will need to find directed edges with center = center1
             # and create angles with DE1, then do the same for center2 and DE2
             for center, dir_edge in zip(
-                u_edge.nodes, u_edge.info["directed_edge_index"]
+                u_edge.nodes, u_edge.info["directed_edge_index"], strict=True
             ):
                 for directed_edges in self.nodes[center].neighbors.values():
                     for directed_edge in directed_edges:

chgnet/model/composition_model.py

@@ -141,7 +141,7 @@ def fit(
         composition_feas = torch.zeros([num_data, self.max_num_elements])
         e = torch.zeros([num_data])
         for index, (structure, energy) in enumerate(
-            zip(structures_or_graphs, energies)
+            zip(structures_or_graphs, energies, strict=True)
         ):
             if isinstance(structure, Structure):
                 atomic_number = torch.tensor(

chgnet/model/dynamics.py

@@ -499,7 +499,7 @@ def __init__(
         """
         self.ensemble = ensemble
         self.thermostat = thermostat
-        if isinstance(atoms, (Structure, Molecule)):
+        if isinstance(atoms, Structure | Molecule):
             atoms = AseAtomsAdaptor().get_atoms(atoms)
             # atoms = atoms.to_ase_atoms()
 

chgnet/model/functions.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import itertools
 from collections.abc import Sequence
 
 import torch
@@ -83,7 +84,7 @@ def __init__(
                 find_activation(activation),
             ]
             if len(hidden_dim) != 1:
-                for h_in, h_out in zip(hidden_dim[0:-1], hidden_dim[1:]):
+                for h_in, h_out in itertools.pairwise(hidden_dim):
                     layers.append(nn.Linear(h_in, h_out, bias=bias))
                     layers.append(find_activation(activation))
             layers.append(nn.Dropout(dropout))

chgnet/model/model.py

@@ -379,7 +379,10 @@ def forward(
         if return_site_energies and self.composition_model is not None:
             site_energy_shifts = self.composition_model.get_site_energies(graphs)
             prediction["site_energies"] = [
-                i + j for i, j in zip(prediction["site_energies"], site_energy_shifts)
+                i + j
+                for i, j in zip(
+                    prediction["site_energies"], site_energy_shifts, strict=True
+                )
             ]
         return prediction
 
@@ -437,7 +440,12 @@ def _compute(
 
         # Message Passing
         for idx, (atom_layer, bond_layer, angle_layer) in enumerate(
-            zip(self.atom_conv_layers[:-1], self.bond_conv_layers, self.angle_layers)
+            zip(
+                self.atom_conv_layers[:-1],
+                self.bond_conv_layers,
+                self.angle_layers,
+                strict=False,
+            )
         ):
             # Atom Conv
             atom_feas = atom_layer(
@@ -522,7 +530,7 @@ def _compute(
             )
             # Convert Stress unit from eV/A^3 to GPa
             scale = 1 / g.volumes * 160.21766208
-            stress = [i * j for i, j in zip(stress, scale)]
+            stress = [i * j for i, j in zip(stress, scale, strict=False)]
             prediction["s"] = stress
 
         # Normalize energy if model is intensive
@@ -614,7 +622,7 @@ def predict_graph(
                 m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr
                     magneton mu_B
         """
-        if not isinstance(graph, (CrystalGraph, Sequence)):
+        if not isinstance(graph, CrystalGraph | Sequence):
             raise TypeError(
                 f"{type(graph)=} must be CrystalGraph or list of CrystalGraphs"
             )

chgnet/trainer/trainer.py

@@ -830,7 +830,7 @@ def forward(
         if "m" in self.target_str:
             mag_preds, mag_targets = [], []
             m_mae_size = 0
-            for mag_pred, mag_target in zip(prediction["m"], targets["m"]):
+            for mag_pred, mag_target in zip(prediction["m"], targets["m"], strict=True):
                 # exclude structures without magmom labels
                 if mag_target is not None:
                     mag_preds.append(mag_pred)

chgnet/utils/vasp_utils.py

@@ -81,13 +81,13 @@ def parse_vasp_dir(
                 tokens = [float(token) for token in re.findall(r"[\d\.\-]+", clean)]
                 tokens.pop(0)
                 if read_charge:
-                    charge.append(dict(zip(header, tokens)))
+                    charge.append(dict(zip(header, tokens, strict=True)))
                 elif read_mag_x:
-                    mag_x.append(dict(zip(header, tokens)))
+                    mag_x.append(dict(zip(header, tokens, strict=True)))
                 elif read_mag_y:
-                    mag_y.append(dict(zip(header, tokens)))
+                    mag_y.append(dict(zip(header, tokens, strict=True)))
                 elif read_mag_z:
-                    mag_z.append(dict(zip(header, tokens)))
+                    mag_z.append(dict(zip(header, tokens, strict=True)))
             elif clean.startswith("tot"):
                 if ion_step_count == (len(mag_x_all) + 1):
                     mag_x_all.append(mag_x)

examples/crystaltoolkit_relax_viewer.ipynb

@@ -377,7 +377,7 @@
     "    coords = trajectory.atom_positions[step]\n",
     "    structure.lattice = lattice  # update structure in place for efficiency\n",
     "    assert len(structure) == len(coords)\n",
-    "    for site, coord in zip(structure, coords):\n",
+    "    for site, coord in zip(structure, coords, strict=True):\n",
     "        site.coords = coord\n",
     "\n",
     "    title = make_title(*structure.get_space_group_info())\n",
@@ -406,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

pyproject.toml

@@ -3,7 +3,7 @@ name = "chgnet"
 version = "0.3.8"
 description = "Pretrained Universal Neural Network Potential for Charge-informed Atomistic Modeling"
 authors = [{ name = "Bowen Deng", email = "[email protected]" }]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 readme = "README.md"
 license = { text = "Modified BSD" }
 dependencies = [
@@ -19,7 +19,6 @@ classifiers = [
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: BSD License",
     "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -53,7 +52,8 @@ requires = ["Cython", "numpy>=2.0.0", "setuptools>=65", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [tool.ruff]
-target-version = "py39"
+target-version = "py310"
+output-format = "concise"
 
 [tool.ruff.lint]
 select = ["ALL"]