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‎LICENSE

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MIT License
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Copyright (c) 2023 Zhuoyuan Li
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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APEX: Alloy Properties EXplorer using simulations
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# APEX
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## General alloy properties test with VASP, ABACUS, and LAMMPS.
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This workflow is a part of [AI Square](https://aissquare.com/). We want to refract the autotest code based on dflow.
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This is "general properties test" (elastic parameters, EOS, surface energy, interstitial energy, vacancy energy and stacking fault energy supported so far) using VASP, LAMMPS, or ABACUS.
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## Easy Install：
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```
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pip install "git+https://github.com/deepmodeling/APEX.git"
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```
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## Quick Start
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You can go to the `example` folder and there are some examples for reference. You can go to one of them and fill in the `global.json` file. Then you can submit the workflow.
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If you want to use VASP code to do the DFT autotest, like the folder `vasp_demo`. You need to prepare `INCAR`, `POTCAR`, `POSCAR`, `global.json`(notice that json files for relaxation and properties task are needed as input arguments), then ：
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```
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apex param_relax.json para_props.json --vasp
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```
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If you want to run only relaxation or only properties test (notice that properties test requires relaxation results under corresponding path in ./confs), for example for relaxation, just give one argument like:
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```
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apex param_relax.json --vasp
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```
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If you want to use ABACUS code, like the folder `abacus_demo`. You need to prepare `INPUT`, `STRU`, `*.UPF`, `global.json`, `param_relax.json`, `param_props.json` (notice that `*.orb` and `KPT` are optional ), then：
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```
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apex param_relax.json param_props.json --abacus
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```
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If you want to use LAMMPS to do MD calculation, like the folder `lammps_demo`. You need to prepare `POSCAR`, `frozen_model.pb`, `global.json`, `param_relax.json`, `param_props.json`, then:
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```
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apex param_relax.json param_props.json --lammps
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```
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You can monitor the workflow process on the [website](https://workflows.deepmodeling.com).
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‎apex/ABACUS_OPs.py

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from dflow.python import (
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PythonOPTemplate,
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OP,
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OPIO,
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OPIOSign,
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Artifact,
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Slices,
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upload_packages
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)
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import subprocess, os, shutil, glob, dpdata, pathlib
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from pathlib import Path
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from typing import List
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from monty.serialization import loadfn
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from dflow.python import upload_packages
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upload_packages.append(__file__)
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from apex.lib.utils import return_prop_list
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try:
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from apex.property.common_equi import (make_equi, post_equi)
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from apex.property.common_prop import (make_property, post_property)
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except:
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pass
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class RelaxMakeABACUS(OP):
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"""
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class for making calculation tasks
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"""
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def __init__(self):
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pass
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@classmethod
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def get_input_sign(cls):
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return OPIOSign({
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'input': Artifact(Path),
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'param': Artifact(Path)
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})
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@classmethod
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def get_output_sign(cls):
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return OPIOSign({
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'output': Artifact(Path),
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'njobs': int,
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'task_paths': Artifact(List[Path])
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})
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@OP.exec_sign_check
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def execute(
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self,
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op_in: OPIO,
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) -> OPIO:
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from apex.property.common_equi import make_equi
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cwd = os.getcwd()
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os.chdir(op_in["input"])
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work_d = os.getcwd()
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param_argv = op_in["param"]
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structures = loadfn(param_argv)["structures"]
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inter_parameter = loadfn(param_argv)["interaction"]
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parameter = loadfn(param_argv)["relaxation"]
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make_equi(structures, inter_parameter, parameter)
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conf_dirs = []
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for conf in structures:
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conf_dirs.extend(glob.glob(conf))
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conf_dirs.sort()
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task_list = []
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for ii in conf_dirs:
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conf_dir_global = os.path.join(work_d, ii)
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task_list.append(os.path.join(conf_dir_global, 'relaxation/relax_task'))
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all_jobs = task_list
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njobs = len(all_jobs)
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jobs = []
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for job in all_jobs:
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jobs.append(pathlib.Path(job))
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os.chdir(cwd)
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op_out = OPIO({
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"output": op_in["input"],
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"njobs": njobs,
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"task_paths": jobs
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})
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return op_out
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class RunABACUS(OP):
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"""
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class for ABACUS calculation
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"""
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def __init__(self, infomode=1):
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self.infomode = infomode
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@classmethod
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def get_input_sign(cls):
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return OPIOSign({
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'input_abacus': Artifact(Path),
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'run_command': str
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})
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@classmethod
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def get_output_sign(cls):
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return OPIOSign({
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'output_abacus': Artifact(Path, sub_path=False)
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})
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@OP.exec_sign_check
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def execute(self, op_in: OPIO) -> OPIO:
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cwd = os.getcwd()
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os.chdir(op_in["input_abacus"])
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cmd = op_in["run_command"]
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subprocess.call(cmd, shell=True)
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os.chdir(cwd)
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op_out = OPIO({
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"output_abacus": op_in["input_abacus"]
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})
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return op_out
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class RelaxPostABACUS(OP):
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"""
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class for analyzing calculation results
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"""
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def __init__(self):
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pass
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@classmethod
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def get_input_sign(cls):
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return OPIOSign({
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'input_post': Artifact(Path, sub_path=False),
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'input_all': Artifact(Path, sub_path=False),
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'param': Artifact(Path),
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'path': str
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})
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@classmethod
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def get_output_sign(cls):
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return OPIOSign({
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'output_all': Artifact(Path, sub_path=False),
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'output_post': Artifact(Path, sub_path=False)
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})
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@OP.exec_sign_check
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def execute(self, op_in: OPIO) -> OPIO:
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from apex.property.common_equi import post_equi
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cwd = os.getcwd()
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os.chdir(str(op_in['input_all']) + op_in['path'])
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shutil.copytree(str(op_in['input_post']) + op_in['path'], './', dirs_exist_ok=True)
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param_argv = op_in['param']
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post_equi(loadfn(param_argv)["structures"], loadfn(param_argv)["interaction"])
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os.chdir(cwd)
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shutil.copytree(str(op_in['input_all']) + op_in['path'] + '/confs', './confs', dirs_exist_ok=True)
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op_out = OPIO({
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'output_all': Path(str(op_in["input_all"]) + op_in['path']),
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'output_post': Path('./confs')
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})
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return op_out
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class PropsMakeABACUS(OP):
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"""
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class for making calculation tasks
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"""
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def __init__(self):
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pass
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@classmethod
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def get_input_sign(cls):
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return OPIOSign({
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'input': Artifact(Path),
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'param': Artifact(Path)
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})
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@classmethod
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def get_output_sign(cls):
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return OPIOSign({
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'output': Artifact(Path),
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'njobs': int,
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'task_paths': Artifact(List[Path])
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})
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@OP.exec_sign_check
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def execute(
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self,
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op_in: OPIO,
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) -> OPIO:
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from apex.property.common_prop import make_property
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cwd = os.getcwd()
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os.chdir(op_in["input"])
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work_d = os.getcwd()
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param_argv = op_in["param"]
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structures = loadfn(param_argv)["structures"]
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inter_parameter = loadfn(param_argv)["interaction"]
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parameter = loadfn(param_argv)["properties"]
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make_property(structures, inter_parameter, parameter)
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conf_dirs = []
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for conf in structures:
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conf_dirs.extend(glob.glob(conf))
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conf_dirs.sort()
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prop_list = return_prop_list(parameter)
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task_list = []
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for ii in conf_dirs:
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conf_dir_global = os.path.join(work_d, ii)
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for jj in prop_list:
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prop = os.path.join(conf_dir_global, jj)
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os.chdir(prop)
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prop_tasks = glob.glob(os.path.join(prop, 'task.*'))
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prop_tasks.sort()
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for kk in prop_tasks:
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task_list.append(kk)
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all_jobs = task_list
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njobs = len(all_jobs)
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jobs = []
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for job in all_jobs:
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jobs.append(pathlib.Path(job))
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os.chdir(cwd)
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op_out = OPIO({
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"output": op_in["input"],
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"njobs": njobs,
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"task_paths": jobs
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})
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return op_out
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class PropsPostABACUS(OP):
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"""
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class for analyzing calculation results
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"""
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def __init__(self):
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pass
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@classmethod
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def get_input_sign(cls):
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return OPIOSign({
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'input_post': Artifact(Path, sub_path=False),
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'input_all': Artifact(Path, sub_path=False),
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'param': Artifact(Path),
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'path': str
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})
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@classmethod
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def get_output_sign(cls):
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return OPIOSign({
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'output_post': Artifact(Path, sub_path=False)
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})
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@OP.exec_sign_check
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def execute(self, op_in: OPIO) -> OPIO:
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from apex.property.common_prop import post_property
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cwd = os.getcwd()
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os.chdir(str(op_in['input_all']) + op_in['path'])
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shutil.copytree(str(op_in['input_post']) + op_in['path'], './', dirs_exist_ok=True)
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param_argv = op_in["param"]
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post_property(loadfn(param_argv)["structures"], loadfn(param_argv)["interaction"], loadfn(param_argv)["properties"])
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os.chdir(cwd)
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shutil.copytree(str(op_in['input_all']) + op_in['path'] + '/confs', './confs', dirs_exist_ok=True)
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op_out = OPIO({
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'output_post': Path('./confs')
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})
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return op_out

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