Merge branch 'mkphuthi/develop' of https://github.com/BattModels/asim…

…tools into mkphuthi/develop

asimtools/asimmodules/matgl/train_matgl.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+'''
+Asimmodule for training MACE models
+
+Author: [email protected]
+'''
+from typing import Dict, Optional, Union
+import sys
+from pathlib import Path
+import logging
+import warnings
+warnings.filterwarnings("ignore")
+import json
+from numpy.random import randint
+from mace.cli.run_train import main as mace_run_train_main
+from mace.cli.create_lammps_model import main as create_lammps_model
+
+def train_mace(
+    config: Union[Dict,str],
+    randomize_seed: bool = False,
+    compile_lammps: bool = False,
+) -> Dict:
+    """Runs MACE training
+
+    :param config: MACE config dictionary or path to config file
+    :type config: Union[Dict,str]
+    :param randomize_seed: Whether to randomize the seed, defaults to False
+    :type randomize_seed: bool
+    :return: Dictionary of results
+    :rtype: Dict
+    """
+
+    if isinstance(config, str):
+        with open(config, 'r') as fp:
+            config = json.load(fp)
+
+    if randomize_seed:
+        config['seed'] = randint(0, 1000000)
+
+    config_file_path = str(Path("mace_config.yaml").resolve())
+    with open(config_file_path, "w") as f:
+        json.dump(config, f, indent=2)
+
+    logging.getLogger().handlers.clear()
+    sys.argv = ["program", "--config", config_file_path]
+    mace_run_train_main()
+
+    if compile_lammps:
+        create_lammps_model('mace_test_compiled.model')
+    return {}

asimtools/asimmodules/transformations/delete_atoms.py

@@ -0,0 +1,79 @@
+'''
+Produce a set of images with unit cells scaled compared to the input
+
+author: [email protected]
+'''
+
+from typing import Dict, Optional, Sequence
+import numpy as np
+from ase.io import write
+from asimtools.utils import (
+    get_atoms,
+)
+
+def apply_scale(old_atoms, scale):
+    ''' Applies a scaling factor to a unit cell '''
+    atoms = old_atoms.copy()
+    new_cell = atoms.get_cell() * scale
+    atoms.set_cell(new_cell, scale_atoms=True)
+    atoms.info['scale'] = f'{scale:.3f}'
+    return atoms
+
+def scale_unit_cells(
+    image: Dict,
+    scales:  Optional[Sequence] = None,
+    logspace: Optional[Sequence] = None,
+    linspace: Optional[Sequence] = None,
+    scale_by: str = 'a',
+) -> Dict:
+    """Produce a set of images with unit cells scaled compared to the input
+
+    :param image: Image specification, see :func:`asimtools.utils.get_atoms`
+    :type image: Dict
+    :param scales: Scaling values by which to scale cell, defaults to None
+    :type scales: Optional[Sequence], optional
+    :param logspace: Parameters to pass to np.logspace for scaling values,
+        defaults to None
+    :type logspace: Optional[Sequence], optional
+    :param linspace: Parameters to pass to np.linspace for scaling values,
+        defaults to None
+    :type linspace: Optional[Sequence], optional
+    :param scale_by: Scale either "volume" or "a" which is lattice parameter,
+        defaults to 'a'
+    :type scale_by: str, optional
+    :raises ValueError: If more than one of scales, linspace, logspace are
+        provided
+    :return: Path to xyz file
+    :rtype: Dict
+    """
+
+    assert scale_by in ['volume', 'a'], \
+        'Only scaling by "a" and "volume" allowed'
+
+    if  (scales is None and linspace is None and logspace is not None):
+        scales = np.logspace(*logspace)
+    elif (scales is None and linspace is not None and logspace is None):
+        scales = np.linspace(*linspace)
+    elif (scales is not None and linspace is None and logspace is None):
+        pass
+    else:
+        raise ValueError(
+            'Provide only one of factors, factor_logspacem factor_linspace'
+        )
+
+    atoms = get_atoms(**image)
+
+    scales = np.array(scales)
+    if scale_by == 'volume':
+        scales = scales**(1/3)
+
+    # Make a database of structures with the volumes scaled appropriately
+    scaled_images = []
+    for scale in scales:
+        new_atoms = apply_scale(atoms, scale)
+        scaled_images.append(new_atoms)
+
+    scaled_images_file = 'scaled_unitcells_output.xyz'
+    write(scaled_images_file, scaled_images, format='extxyz')
+
+    return {'files': {'images': scaled_images_file}}

asimtools/job.py

@@ -44,6 +44,7 @@ def __init__(
         sim_input: Dict,
         env_input: Union[Dict,None] = None,
         calc_input: Union[Dict,None] = None,
+        asimrun_mode: bool = False,
     ) -> None:
         if env_input is None:
             env_input = get_env_input()
@@ -63,7 +64,7 @@ def __init__(
             self.sim_input['src_dir'] = self.launchdir
 
         self.env_id = self.sim_input.get('env_id', None)
-        if self.env_id is not None and self.env_input is not None:
+        if self.env_id is not None and not asimrun_mode:
             self.env = self.env_input[self.env_id]
         else:
             self.env = {
@@ -1002,7 +1003,7 @@ def submit(self, dependency: Union[List,None] = None, debug: bool = False) -> Li
         return job_ids
 
 
-def load_job_from_directory(workdir: os.PathLike) -> Job:
+def load_job_from_directory(workdir: os.PathLike, asimrun_mode=False) -> Job:
     ''' Loads a job from a given directory '''
     workdir = Path(workdir)
     assert workdir.exists(), f'Work directory "{workdir}" does not exist'
@@ -1029,6 +1030,7 @@ def load_job_from_directory(workdir: os.PathLike) -> Job:
         sim_input=sim_input,
         env_input=env_input,
         calc_input=calc_input,
+        asimrun_mode=asimrun_mode,
     )
 
     # This makes sure that wherever we may be loading the job from, we refer

asimtools/scripts/asim_run.py

@@ -132,7 +132,7 @@ def main(args=None) -> None:
     sim_func = getattr(sim_module, func_name)
 
     cwd = Path('.').resolve()
-    job = load_job_from_directory(cwd)
+    job = load_job_from_directory(cwd, asimrun_mode=True)
     job.start()
 
     try: