Add pre commit

.github/workflows/pre-commit.yml

@@ -0,0 +1,18 @@
+name: "Lint"
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  pre-commit:
+    env:
+      SKIP: 'no-commit-to-branch'
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Run pre-commit
+        uses: pre-commit/[email protected]

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+default_language_version:
+    # all hooks should run with python 3.6+
+    python: python3
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: no-commit-to-branch
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: check-added-large-files
+        args: ['--maxkb=500', '--enforce-all']
+        exclude: mlptrain/sampling/tests/data.zip
+      - id: check-yaml
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.1.3
+    hooks:
+      - id: ruff
+        args: [--show-source, --fix]

README.md

@@ -1,6 +1,8 @@
 [![Test with pytest](https://github.com/duartegroup/mlp-train/actions/workflows/pytest.yml/badge.svg?event=push)](https://github.com/duartegroup/mlp-train/actions/workflows/pytest.yml)
 [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![License](https://img.shields.io/badge/License-MIT%202.0-blue.svg)](https://opensource.org/licenses/mit)
+[![GitHub issues](https://img.shields.io/github/issues/duartegroup/mlp-train.svg)](https://github.com/duartegroup/mlp-train/issues)
 
 # mlp-train
 General machine learning potentials (MLP) training for molecular systems in gas phase and solution
@@ -11,8 +13,7 @@ Available models:
 - MACE
 
 
-***
-### Install
+## Install
 
 Each model is installed into individual conda environment:
 
@@ -27,12 +28,21 @@ Each model is installed into individual conda environment:
 ./install_mace.sh 
 ```
 
-
 ### Notes
 
 - Units are: distance (Å), energy (eV), force (eV Å$`^{-1}`$), time (fs)
 
-## Citation
+## For developers
+
+We are happy to accept pull requests from users. Please first fork mlp-train repository. We use `pre-commit`, `Ruff` and `pytest` to check the code. Your PR needs to pass through these checks before is accepted. `Pre-commit` is installed as one the dependecies. To use it in your repository, run the following command in the mlp-train folder:
+
+```
+pre-commit install 
+```
+
+`Pre-commit` will then run automatically at each commit and will take care of installation and running of `Ruff`.
+
+## Citations
 
 If _mlptrain_ is used in a publication please consider citing the [paper](https://doi.org/10.1039/D2CP02978B):
 
@@ -47,3 +57,8 @@ If _mlptrain_ is used in a publication please consider citing the [paper](https:
   journal = {Phys. Chem. Chem. Phys.}
 }
 ```
+
+## Contact
+
+For bugs or implementation requests, please use [GitHub Issues](https://github.com/duartegroup/mlp-train/issues)
+

create_conda_environment.sh

@@ -1,4 +1,3 @@
-#!/bin/bash
 # NOTE: This script should not be called on its own,
 # but should be sourced from other install scripts such as install_ace.sh
 set -euo pipefail

environment.yml

@@ -6,6 +6,7 @@ channels:
 dependencies:
   - python=3.9
   - pip
+  - pre-commit
   - ase
   - autode=1.1
   - coloredlogs

environment_ace.yml

@@ -6,6 +6,7 @@ channels:
 dependencies:
   - python=3.9
   - pip
+  - pre-commit
   - ase
   - autode=1.1
   - coloredlogs

environment_mace.yml

@@ -7,6 +7,7 @@ channels:
 dependencies:
   - python=3.9
   - pip
+  - pre-commit
   - ase
   - autode=1.1
   - coloredlogs

examples/DA_paper/1d_fes/fes.py

@@ -1,4 +1,3 @@
-import sys
 import mlptrain as mlt
 import numpy as np
 from mlptrain.box import Box

examples/DA_paper/2D_pes/pes.py

@@ -187,9 +187,6 @@ def get_energy(self, calc):
     def get_free_energy(self, calc):
         return None
 
-    def get_free_energy(self, calc):
-        return None
-
     def get_enthalpy(self, calc):
         return None
 
@@ -263,7 +260,7 @@ def optimise_with_fix_solute(solute, configuration, fmax, mlp, constraint = True
     from ase.optimize import BFGS
     from ase.io.trajectory import Trajectory as ASETrajectory
 
-    assert configuration.box != None, 'configuration must have box'
+    assert configuration.box is not None, 'configuration must have box'
     logger.info('Optimise the configuration with fixed solute (solute coords should at the first in configuration coords) by MLP')
 
     n_cores = kwargs['n_cores'] if 'n_cores' in kwargs else min(Config.n_cores, 8)

examples/DA_paper/training/explicit/endo_ace_ex.py

@@ -1,10 +1,9 @@
 import mlptrain as mlt
 import numpy as np
-from autode.atoms import Atoms, Atom
+from autode.atoms import Atom
 from mlptrain.log import logger
 from mlptrain.box import Box
 from mlptrain.training.selection import MaxAtomicEnvDistance
-import random
 
 mlt.Config.n_cores = 10
 mlt.Config.orca_keywords = ['wB97M-D3BJ', 'def2-TZVP','def2/J', 'RIJCOSX','EnGrad']
@@ -30,7 +29,6 @@ def add_water(solute, n = 2):
        n: number of water molecules to add"""
     from ase import Atoms
     from ase.calculators.tip3p import rOH, angleHOH
-    from ase.io import read , write
 
     # water molecule
     x = angleHOH * np.pi / 180 / 2
@@ -46,7 +44,7 @@ def add_water(solute, n = 2):
     water0.rotate(180, 'x')
     water0.rotate(180, 'z')
 
-    assert solute.box != None, 'configuration must have box'
+    assert solute.box is not None, 'configuration must have box'
     sol = solute.ase_atoms
     sol.center()
     sys = sol.copy()
@@ -104,13 +102,13 @@ def solvation(solute_config, solvent_config, apm, radius, enforce = True):
     """function to generate solvated system by adding the solute at the center of box,
        then remove the overlapped solvent molecules
        adapted from https://doi.org/10.1002/qua.26343
-       solute: mlt.Configuration() solute.box != None
-       solvent: mlt.Configuration() solvent.box != None
+       solute: mlt.Configuration() solute.box is not None
+       solvent: mlt.Configuration() solvent.box is not None
        aps: number of atoms per solvent molecule
        radius: cutout radius around each solute atom
        enforce: True / False Wrap solvent regardless of previous solvent PBC choices"""
-    assert solute_config.box != None, 'configuration must have box'
-    assert solvent_config.box != None, 'configuration must have box'
+    assert solute_config.box is not None, 'configuration must have box'
+    assert solvent_config.box is not None, 'configuration must have box'
 
     solute = solute_config.ase_atoms
     solvent = solvent_config.ase_atoms
@@ -221,7 +219,7 @@ def generate_init_configs(n, bulk_water = True, TS = True):
 
     # TS bounded with two water molecules at carbonyl group to form hydrogen bond
     else:
-        assert TS == True, 'cannot generate initial configuration'
+        assert TS is True, 'cannot generate initial configuration'
         for i in range(n):
             TS_with_water = add_water(solute=TS, n=2)
             init_configs.append(TS_with_water)

examples/DA_paper/uphill/generate_rs.py

@@ -4,6 +4,8 @@
 from mlptrain.log import logger
 from mlptrain.config import Config
 from mlptrain.md import  _convert_ase_traj
+from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
+from numpy.random import RandomState
 import numpy as np
 
 mlt.Config.n_cores = 10
@@ -28,13 +30,13 @@ def from_ase_to_autode(atoms):
 
 def solvation(solute_config, solvent_config, apm, radius, enforce = True):
     """same function applied in training an MLP for reaction in explicit water
-       solute: mlt.Configuration() solute.box != None
-       solvent: mlt.Configuration() solvent.box != None
+       solute: mlt.Configuration() solute.box is not None
+       solvent: mlt.Configuration() solvent.box is not None
        aps: number of atoms per solvent molecule
        radius: cutout radius around each solute atom
        enforce: True / False Wrap solvent regardless of previous solvent PBC choices"""
-    assert solute_config.box != None, 'configuration must have box'
-    assert solvent_config.box != None, 'configuration must have box'
+    assert solute_config.box is not None, 'configuration must have box'
+    assert solvent_config.box is not None, 'configuration must have box'
 
     solute = solute_config.ase_atoms
     solvent = solvent_config.ase_atoms
@@ -135,7 +137,7 @@ def mlpmd_fix_solute(solute, configuration, mlp, temp, dt, interval, n_steps, **
     from ase.io.trajectory import Trajectory as ASETrajectory
     from ase.md.langevin import Langevin
     from ase import units as ase_units
-    assert configuration.box != None, 'configuration must have box'
+    assert configuration.box is not None, 'configuration must have box'
 
     logger.info('Run MLP MD with fixed solute (solute coords should at the first in configuration coords) by MLP')
 
@@ -174,7 +176,7 @@ def optimize_sys(configuration, mlp, **kwargs):
     # applied MLP to optimised geometry with BFGS method
     from ase.io.trajectory import Trajectory as ASETrajectory
     from ase.optimize import BFGS
-    assert configuration.box != None, 'configuration must have box'
+    assert configuration.box is not None, 'configuration must have box'
 
     logger.info('Optimise the configuratoin with fixed solute (solute coords should at the first in configuration coords) by MLP')
 
@@ -266,6 +268,10 @@ def baised_md(configuration, mlp, temp, dt, interval, bias, **kwargs):
                                      rng=RandomState())
 
     traj = ASETrajectory("tmp.traj", 'w', ase_atoms)
+    energies = []
+
+    def append_energy(_atoms=ase_atoms):
+        energies.append(_atoms.get_potential_energy())
 
     if temp > 0:                                         # Default Langevin NVT
         dyn = Langevin(ase_atoms, dt * ase_units.fs,
@@ -312,7 +318,7 @@ def generate_rs(TS, solution, mlp, box_size):
     for i, species in enumerate(reactants):
         bias = mlt.Bias(zeta_func=mlt.AverageDistance((1,12), (6,11)), kappa=0.5, reference=ref[i])
         traj = baised_md(configuration=species,
-                              mlp=endo,
+                              mlp=mlp,
                               temp=300,
                               dt=0.5,
                               interval=20,

examples/DA_paper/uphill/uphill.py

@@ -1,14 +1,9 @@
 import mlptrain as mlt
 import numpy as np
-import matplotlib.pyplot as plt
 from mlptrain.log import logger
-import random
 from mlptrain.box import Box
 from scipy.spatial import distance_matrix
 import os
-import math
-from ase.constraints import Hookean
-from ase.geometry import find_mic
 from generate_rs import generate_rs
 
 mlt.Config.n_cores = 10
@@ -116,7 +111,7 @@ def get_cavity_volume(atoms = ase_atoms):
 
     dyn.attach(append_energy, interval=interval)
     dyn.attach(get_reaction_coord,interval=interval)
-    dyn.attach(get_cavity_volumn,interval=interval)
+    dyn.attach(get_cavity_volume,interval=interval)
     dyn.attach(traj.write, interval=interval)
 
     logger.info(f'Running {n_steps:.0f} steps with a timestep of {dt} fs')
@@ -133,20 +128,10 @@ def get_cavity_volume(atoms = ase_atoms):
 
 
 def traj_study(configs,  ml_potential,  init_md_time_fs = 500, max_time_fs = 3000):     
-    num_config = len(configs)
-
-    C2_C7_recrossing_list = []
-    C4_C6_recrossing_list = []
-
-    C2_C7_product_list = []
-    C4_C6_product_list = []
-
+
     C2_C7_initial_list = []
     C4_C6_initial_list = []
 
-    time_sep = []
-    intermediate_time = []
-
     for k in range(500):
         config =configs[k]
         logger.info(f'start trajectory study for {k} th configuration')
@@ -175,7 +160,7 @@ def traj_study(configs,  ml_potential,  init_md_time_fs = 500, max_time_fs = 300
                                                                 temp=300,
                                                                 dt=0.5,
                                                                 interval=2,
-                                                                fs=md_time_f)
+                                                                fs=md_time_fs_f)
             ending = 0
             for (i, j) in zip (C2_C7_list, C4_C6_list):
                 logger.info(f'C2-C7 and C4-C6 bond lengths are {(i,j)}')

mlptrain/__init__.py

@@ -28,6 +28,7 @@
            'Box',
            'Bias',
            'PlumedBias',
+           'PlumedCalculator', 
            'UmbrellaSampling',
            'Metadynamics',
            'AverageDistance',

mlptrain/config.py

@@ -1,4 +1,4 @@
-from autode.wrappers.keywords import *
+from autode.wrappers.keywords import GradientKeywords
 
 
 class _ConfigClass:

mlptrain/configurations/calculate.py

@@ -1,3 +1,5 @@
+import mlptrain
+import autode
 from typing import Tuple
 from mlptrain.log import logger
 from mlptrain.utils import work_in_tmp_dir

mlptrain/configurations/configuration.py

@@ -1,8 +1,11 @@
+import mlptrain
+import ase
 import numpy as np
 from typing import Optional, Union, List
 from copy import deepcopy
-from autode.atoms import AtomCollection, Atoms, Atom
-from ase.atoms import Atoms
+from autode.atoms import AtomCollection, Atom
+import autode.atoms
+import ase.atoms
 from mlptrain.log import logger
 from mlptrain.energy import Energy
 from mlptrain.forces import Forces
@@ -14,7 +17,7 @@ class Configuration(AtomCollection):
     """Configuration of atoms"""
 
     def __init__(self,
-                 atoms:  Union[Atoms, List[Atom], None] = None,
+                 atoms:  Union[autode.atoms.Atoms, List[Atom], None] = None,
                  charge: int = 0,
                  mult:   int = 1,
                  box:    Optional[Box] = None
@@ -55,7 +58,7 @@ def ase_atoms(self) -> 'ase.atoms.Atoms':
         Returns:
             (ase.atoms.Atoms): ASE atoms
         """
-        _atoms = Atoms(symbols=[atom.label for atom in self.atoms],
+        _atoms = ase.atoms.Atoms(symbols=[atom.label for atom in self.atoms],
                        positions=self.coordinates,
                        pbc=self.box is not None)
 

mlptrain/configurations/configuration_set.py

@@ -1,3 +1,4 @@
+import mlptrain
 import os
 import numpy as np
 from time import time
@@ -447,12 +448,12 @@ def plumed_coordinates(self) -> Optional[np.ndarray]:
                 n_cvs_set.add(len(config.plumed_coordinates))
 
         if len(n_cvs_set) == 0:
-            logger.info(f'PLUMED coordinates not defined - returning None')
+            logger.info('PLUMED coordinates not defined - returning None')
             return None
 
         elif len(n_cvs_set) != 1:
-            logger.info(f'Number of CVs differ between configurations - '
-                        f'returning None')
+            logger.info('Number of CVs differ between configurations - '
+                        'returning None')
             return None
 
         n_cvs = n_cvs_set.pop()

mlptrain/configurations/plotting.py

@@ -1,3 +1,4 @@
+import mlptrain
 import numpy as np
 import matplotlib as mpl
 import matplotlib.pyplot as plt