from typing import List
from dargs import Argument, Variant
from dpgen.arginfo import general_mdata_arginfo
from dpgen.generator.arginfo import (
basic_args,
data_args,
training_args,
fp_style_vasp_args,
fp_style_gaussian_args,
)
[docs]def general_simplify_arginfo() -> Argument:
"""General simplify arginfo.
Returns
-------
Argument
arginfo
"""
doc_labeled = "If true, the initial data is labeled."
doc_pick_data = "Path to the directory with the pick data with the deepmd/npy format. Systems are detected recursively."
doc_init_pick_number = "The number of initial pick data."
doc_iter_pick_number = "The number of pick data in each iteration."
doc_model_devi_f_trust_lo = "The lower bound of forces for the selection for the model deviation."
doc_model_devi_f_trust_hi = "The higher bound of forces for the selection for the model deviation."
return [
Argument("labeled", bool, optional=True, default=False, doc=doc_labeled),
Argument("pick_data", str, doc=doc_pick_data),
Argument("init_pick_number", int, doc=doc_init_pick_number),
Argument("iter_pick_number", int, doc=doc_iter_pick_number),
Argument("model_devi_f_trust_lo", float, optional=False, doc=doc_model_devi_f_trust_lo),
Argument("model_devi_f_trust_hi", float, optional=False, doc=doc_model_devi_f_trust_hi),
]
[docs]def fp_style_variant_type_args() -> Variant:
"""Generate variant for fp style variant type.
Returns
-------
Variant
variant for fp style
"""
doc_fp_style = 'Software for First Principles, if `labeled` is false. Options include “vasp”, “gaussian” up to now.'
doc_fp_style_none = 'No fp.'
doc_fp_style_vasp = 'VASP.'
doc_fp_style_gaussian = 'Gaussian. The command should be set as `g16 < input`.'
return Variant("fp_style", [
Argument("none", dict, doc=doc_fp_style_none),
# simplify use the same fp method as run
Argument("vasp", dict, fp_style_vasp_args(), doc=doc_fp_style_vasp),
Argument("gaussian", dict, fp_style_gaussian_args(),
doc=doc_fp_style_gaussian),
],
optional=True,
default_tag="none",
doc=doc_fp_style)
[docs]def fp_args() -> List[Argument]:
"""Generate arginfo for fp.
Returns
-------
List[Argument]
arginfo
"""
doc_fp_task_max = 'Maximum of structures to be calculated in 02.fp of each iteration.'
doc_fp_task_min = 'Minimum of structures to be calculated in 02.fp of each iteration.'
doc_fp_accurate_threshold = 'If the accurate ratio is larger than this number, no fp calculation will be performed, i.e. fp_task_max = 0.'
doc_fp_accurate_soft_threshold = 'If the accurate ratio is between this number and fp_accurate_threshold, the fp_task_max linearly decays to zero.'
return [
Argument("fp_task_max", int, optional=True, doc=doc_fp_task_max),
Argument("fp_task_min", int, optional=True, doc=doc_fp_task_min),
Argument("fp_accurate_threshold", float,
optional=True, doc=doc_fp_accurate_threshold),
Argument("fp_accurate_soft_threshold", float,
optional=True, doc=doc_fp_accurate_soft_threshold),
]
[docs]def simplify_jdata_arginfo() -> Argument:
"""Generate arginfo for dpgen simplify jdata.
Returns
-------
Argument
arginfo
"""
doc_run_jdata = "Parameters for simplify.json, the first argument of `dpgen simplify`."
return Argument("simplify_jdata",
dict,
sub_fields=[
*basic_args(),
# TODO: we may remove sys_configs; it is required in train method
*data_args(),
*general_simplify_arginfo(),
# simplify use the same training method as run
*training_args(),
*fp_args(),
],
sub_variants=[
fp_style_variant_type_args(),
],
doc=doc_run_jdata,
)
[docs]def simplify_mdata_arginfo() -> Argument:
"""Generate arginfo for dpgen simplify mdata.
Returns
-------
Argument
arginfo
"""
return general_mdata_arginfo("simplify_mdata", ("train", "model_devi", "fp"))