Update package to use python version to 3.14

README.md

@@ -33,9 +33,9 @@ Once you have `micromamba` installed and have already cloned this repo, you can
 
 ### Linux
 ```bash
-micromamba create -n qligfep_new python=3.11
+micromamba create -n qligfep_new python=3.14
 micromamba activate qligfep_new
-micromamba install gfortran=11.3.0 openff-toolkit=0.17.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.1.1 openff-nagl=0.5.4 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 michellab::fkcombu konnektor -c conda-forge --yes
+micromamba install gfortran=11.3.0 openff-toolkit=0.18.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.4.0 openff-nagl=0.5.5 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 michellab::fkcombu konnektor -c conda-forge --yes
 ```
 
 Now that you have the environment ready and activated, [clone the repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository), enter the `Q` directory with `cd Q/`, and install qligfep:
@@ -49,7 +49,7 @@ The `qprep` Fortran binary will be automatically compiled during installation.
 <summary>To install everything in one line...</summary>
 
 ```bash
-micromamba create -n qligfep_new python=3.11 gfortran=11.3.0 openff-toolkit=0.17.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.1.1 openff-nagl=0.5.4 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 michellab::fkcombu konnektor -c conda-forge --yes && micromamba activate qligfep_new && python -m pip install -e .
+micromamba create -n qligfep_new python=3.14 gfortran=11.3.0 openff-toolkit=0.18.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.4.0 openff-nagl=0.5.5 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 michellab::fkcombu konnektor -c conda-forge --yes && micromamba activate qligfep_new && python -m pip install -e .
 ```
 </details>
 
@@ -58,7 +58,7 @@ micromamba create -n qligfep_new python=3.11 gfortran=11.3.0 openff-toolkit=0.17
 Similar to Linux, [clone the repository](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository), enter the `Q` directory with `cd Q/`, create the environment and install:
 
 ``` bash
-micromamba create -n qligfep_new python=3.11 gfortran=11.3.0 openff-toolkit=0.17.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.1.1 openff-nagl=0.5.4 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 davidararipe::kcombu_bss konnektor -c conda-forge --yes
+micromamba create -n qligfep_new python=3.14 gfortran=11.3.0 openff-toolkit=0.18.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.4.0 openff-nagl=0.5.5 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 davidararipe::kcombu_bss konnektor -c conda-forge --yes
 micromamba activate qligfep_new
 python -m pip install joblib scipy tqdm
 python -m pip install -e .
@@ -70,7 +70,7 @@ The `qprep` Fortran binary will be automatically compiled during installation.
 <summary>To install everything in one line...</summary>
 
 ```bash
-micromamba create -n qligfep_new python=3.11 gfortran=11.3.0 openff-toolkit=0.17.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.1.1 openff-nagl=0.5.4 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 davidararipe::kcombu_bss konnektor -c conda-forge --yes && micromamba activate qligfep_new && python -m pip install joblib scipy tqdm && python -m pip install -e .
+micromamba create -n qligfep_new python=3.14 gfortran=11.3.0 openff-toolkit=0.18.1 "openff-utilities>=0.1.12" openff-forcefields=2026.01.0 openmm=8.4.0 openff-nagl=0.5.5 openff-nagl-models=2025.9.0 lomap2 kartograf=1.0.1 davidararipe::kcombu_bss konnektor -c conda-forge --yes && micromamba activate qligfep_new && python -m pip install joblib scipy tqdm && python -m pip install -e .
 ```
 </details>
 

pyproject.toml

@@ -7,7 +7,7 @@ name = "QligFEP"
 dynamic = ["version"]
 description = "Python CLI designed to facilitate a robust and fast setup of free energy perturbation (FEP)."
 readme = { file = "README.md", content-type = "text/markdown" }
-requires-python = ">=3.10"
+requires-python = ">=3.14"
 license = { file = "LICENSE" }
 # keywords = [""]
 authors = [{ name = "Willem Jespers", email = "w.jespers@rug.nl" },
@@ -24,7 +24,7 @@ classifiers = [
     "Intended Audience :: Science/Research",
     "Topic :: Scientific/Engineering :: Chemistry",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10" # todo: might have other versions
+    "Programming Language :: Python :: 3.14" # todo: might have other versions
 ]
 dependencies = [
     "joblib",
@@ -69,15 +69,16 @@ repository = "https://github.com/qusers/qligfep"
 
 [tool.black]
 line-length =  110
-target-version = ['py310']
+target-version = ['py314']
 
 [tool.isort]
 profile = "black"
+line_length = 110
 
 [tool.ruff]
 line-length = 110
 indent-width = 4
-target-version = 'py39'
+target-version = 'py314'
 
 [tool.ruff.lint]
 select = [

src/QligFEP/CLI/cog_cli.py

@@ -65,7 +65,7 @@ def _cog_sdf(self):
             for i, center in enumerate(centers):
                 logger.debug(f"Ligand {i+1} center: {center}")
 
-        overall_center = [sum(x) / len(centers) for x in zip(*centers)]
+        overall_center = [sum(x) / len(centers) for x in zip(*centers, strict=False)]
         return f"[{round(overall_center[0], 3):.3f} {round(overall_center[1], 3):.3f} {round(overall_center[2], 3):.3f}]"
 
     def _calculate_center(self, coordinates):

src/QligFEP/CLI/konnektor_cli.py

@@ -3,7 +3,6 @@
 import argparse
 import json
 from pathlib import Path
-from typing import Optional
 
 import numpy as np
 from kartograf import KartografAtomMapper, SmallMoleculeComponent
@@ -34,18 +33,18 @@ class KonnektorWrap:
     def __init__(
         self,
         inp: str,
-        out: Optional[str] = None,
+        out: str | None = None,
         network: str = "mst",
         scorer: str = "combined",
         restraint_method: str = "heavyatom_p",
         processes: int = 1,
         log_level: str = "info",
-        central_ligand: Optional[str] = None,
+        central_ligand: str | None = None,
         n_redundancy: int = 2,
         connectivity: int = 3,
         separate_charges: bool = False,
         charge_changes_score: float = 0.0,
-        exp_key: Optional[str] = None,
+        exp_key: str | None = None,
         self_solve: bool = False,
     ):
         self.inp = inp
@@ -66,7 +65,7 @@ def __init__(
         self.out = self._parse_output(out)
         self._sdf_dir = self._prepare_input()
 
-    def _parse_output(self, output: Optional[str]) -> str:
+    def _parse_output(self, output: str | None) -> str:
         inpath = Path(self.inp)
         if output is None:
             if inpath.is_dir():
@@ -310,7 +309,7 @@ def run(self) -> dict:
 
         return result
 
-    def _resolve_sdf_path(self) -> Optional[Path]:
+    def _resolve_sdf_path(self) -> Path | None:
         """Find the SDF file used as input."""
         inp = Path(self.inp)
         if inp.is_file() and inp.suffix == ".sdf":
@@ -354,7 +353,7 @@ def _mcs_rmsd(mol_a, mol_b, timeout=5):
     conf_a = ha.GetConformer()
     conf_b = hb.GetConformer()
     sq_dists = []
-    for ia, ib in zip(match_a, match_b):
+    for ia, ib in zip(match_a, match_b, strict=False):
         pa = conf_a.GetAtomPosition(ia)
         pb = conf_b.GetAtomPosition(ib)
         sq_dists.append((pa.x - pb.x) ** 2 + (pa.y - pb.y) ** 2 + (pa.z - pb.z) ** 2)
@@ -423,7 +422,7 @@ def _realign_to_neighbors(outlier_mol, neighbor_mols):
             continue
         conf_n = hn.GetConformer()
         o_to_core = {oa: i for i, oa in enumerate(match_o)}
-        for oa, na in zip(match_o_nb, match_n_nb):
+        for oa, na in zip(match_o_nb, match_n_nb, strict=False):
             if oa in o_to_core:
                 p = conf_n.GetAtomPosition(na)
                 core_positions.setdefault(o_to_core[oa], []).append(

src/QligFEP/CLI/lomap_wrap_cli.py

@@ -5,7 +5,6 @@
 import re
 from multiprocessing import cpu_count
 from pathlib import Path
-from typing import Optional
 
 import lomap
 import numpy as np
@@ -22,10 +21,10 @@ class LomapWrap:
     def __init__(
         self,
         inp: str,
-        out: Optional[str] = None,
+        out: str | None = None,
         time=30,
         verbose="info",
-        exp_key: Optional[str] = None,
+        exp_key: str | None = None,
         **kwargs,
     ):
         self.nodes = {}

src/QligFEP/CLI/qligfep_cli.py

@@ -4,7 +4,6 @@
 import datetime
 import json
 from pathlib import Path
-from typing import Optional
 
 from QligFEP import __version__
 
@@ -15,7 +14,7 @@
 from .parser_base import parse_arguments
 
 
-def main(args: Optional[argparse.Namespace] = None, **kwargs) -> None:
+def main(args: argparse.Namespace | None = None, **kwargs) -> None:
     """Main function for qligfep_cli.py. Takes arguments from argparse and passes them
     to QligFEP class. If no arguments are given, the function will use the keyword arguments
     that are passed to it.

src/QligFEP/CLI/setupFEP.py

@@ -7,7 +7,6 @@
 import shutil
 import subprocess
 from pathlib import Path
-from typing import Optional
 
 from ..IO import parse_qprep_total_charge
 from ..logger import logger, setup_logger
@@ -76,7 +75,7 @@ def submit_command(command: str) -> None:
         raise
 
 
-def main(args: Optional[argparse.Namespace] = None, **kwargs) -> None:
+def main(args: argparse.Namespace | None = None, **kwargs) -> None:
     # setup the logger with the desired log level
     setup_logger(level=args.log)
 
@@ -113,7 +112,7 @@ def main(args: Optional[argparse.Namespace] = None, **kwargs) -> None:
 
     lig_pairs = ligpairs_from_json(args.json_map)
     protein_dir = cwd / "2.protein"
-    for system, sys_dir in zip(systems, sys_directories):
+    for system, sys_dir in zip(systems, sys_directories, strict=False):
         for lig1, lig2, same_charge in lig_pairs:
 
             # For cross-charge water edges, look up the protein leg's total charge

src/QligFEP/IO.py

@@ -4,7 +4,7 @@
 import stat
 import subprocess
 from pathlib import Path
-from typing import NamedTuple, Optional
+from typing import NamedTuple
 
 import numpy as np
 import pandas as pd
@@ -34,8 +34,8 @@ class SlurmRunInfo(NamedTuple):
     """Per-replicate run metadata parsed from one ``slurm*.out`` footer."""
 
     runtime: str
-    seed: Optional[str]
-    replicate: Optional[str]
+    seed: str | None
+    replicate: str | None
     status: str
 
 

src/QligFEP/analysis_plotting.py

@@ -238,7 +238,7 @@ def result_to_latex(res, latexify_each=False):  # TODO: move this out of this me
         (1.04, hori_height - spacing),
         (1.04, hori_height - spacing * 2),
     )
-    for txt_position, body in zip(txt_positions, text_body):
+    for txt_position, body in zip(txt_positions, text_body, strict=False):
         plt.text(
             *txt_position,
             body,

src/QligFEP/analyze_FEP.py

@@ -4,7 +4,6 @@
 import json
 import os
 from pathlib import Path
-from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -31,7 +30,7 @@ def __init__(
         system: str,
         target_name: str,
         mapping_json: str,
-        n_lambdas: Optional[int] = None,
+        n_lambdas: int | None = None,
         allow_missing_edges: bool = False,
     ) -> None:
         """Initialize the FEP reader class. This class will store the FEP information inside

src/QligFEP/analyze_neq.py

@@ -26,7 +26,6 @@
 import os
 from collections import defaultdict
 from pathlib import Path
-from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -75,7 +74,7 @@ def dF_to_kcal(dF: float, work_units: str, temperature: float) -> float:
 WORK_TAIL_BYTES = 256 * 1024
 
 
-def read_final_work(log_path: str) -> Optional[float]:
+def read_final_work(log_path: str) -> float | None:
     """Read the final accumulated switching work from a qdyn NEQ-mode log.
 
     Each switch prints ``At step N, work accumulated was <W> ...`` every output interval;
@@ -101,7 +100,7 @@ def read_final_work(log_path: str) -> Optional[float]:
         if "work accumulated" in line:
             try:
                 return float(line.split()[6])
-            except (IndexError, ValueError):
+            except IndexError, ValueError:
                 logger.warning(f"Could not parse the work value in {log_path}")
                 return None
     return None
@@ -264,7 +263,7 @@ def _dF_by_rep_from_works(by_rep: dict, beta: float, work_units: str, temperatur
             continue
         try:
             result[rep] = dF_to_kcal(bar_delta_f(forward, reverse, beta), work_units, temperature)
-        except (ValueError, RuntimeError):
+        except ValueError, RuntimeError:
             result[rep] = None
     return result
 
@@ -573,7 +572,7 @@ def populate_mapping_json(df: pd.DataFrame, mapping_json: str, output_file: str)
     logger.info(f"Injected NEQ ddG into {matched} edge(s); wrote {output_file}")
 
 
-def _nan_to_none(value) -> Optional[float]:
+def _nan_to_none(value) -> float | None:
     """Return ``None`` for a missing/NaN value (so it serializes to JSON null), else a float."""
     return None if pd.isna(value) else float(value)