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from __future__ import annotations
from collections import defaultdict
from emmet.core.grain_boundary import GBTypeEnum, GrainBoundaryDoc
from mp_api.client.core import BaseRester
from mp_api.client.core.utils import validate_ids
class GrainBoundaryRester(BaseRester[GrainBoundaryDoc]):
suffix = "materials/grain_boundaries"
document_model = GrainBoundaryDoc # type: ignore
primary_key = "material_id"
def search(
self,
material_ids: str | list[str] | None = None,
chemsys: str | None = None,
gb_plane: list[str] | None = None,
gb_energy: tuple[float, float] | None = None,
pretty_formula: str | None = None,
rotation_axis: list[str] | None = None,
rotation_angle: tuple[float, float] | None = None,
separation_energy: tuple[float, float] | None = None,
sigma: int | None = None,
type: GBTypeEnum | None = None,
num_chunks: int | None = None,
chunk_size: int = 1000,
all_fields: bool = True,
fields: list[str] | None = None,
) -> list[GrainBoundaryDoc] | list[dict]:
"""Query grain boundary docs using a variety of search criteria.
Arguments:
material_ids (str, List[str]): Search for grain boundary data associated with the specified Material IDs
chemsys (str): Dash-delimited string of elements in the material.
gb_plane(List[str]): The Miller index of grain boundary plane. e.g., [1, 1, 1]
gb_energy (Tuple[float,float]): Minimum and maximum grain boundary energy in J/m³ to consider.
material_ids (List[str]): List of Materials Project IDs to query with.
pretty_formula (str): Formula of the material.
rotation_angle (Tuple[float,float]): Minimum and maximum rotation angle in degrees to consider.
rotation_axis(List[str]): The Miller index of rotation axis. e.g., [1, 0, 0], [1, 1, 0], and [1, 1, 1]
sigma (int): Sigma value of grain boundary.
separation_energy (Tuple[float,float]): Minimum and maximum work of separation energy in J/m³ to consider.
sigma (int): Sigma value of the boundary.
type (GBTypeEnum): Grain boundary type.
num_chunks (int): Maximum number of chunks of data to yield. None will yield all possible.
chunk_size (int): Number of data entries per chunk.
all_fields (bool): Whether to return all fields in the document. Defaults to True.
fields (List[str]): List of fields in GrainBoundaryDoc to return data for.
Default is material_id and last_updated if all_fields is False.
Returns:
([GrainBoundaryDoc], [dict]) List of grain boundary documents or dictionaries.
"""
query_params = defaultdict(dict) # type: dict
if material_ids:
if isinstance(material_ids, str):
material_ids = [material_ids]
query_params.update({"material_ids": ",".join(validate_ids(material_ids))})
if gb_plane:
query_params.update({"gb_plane": ",".join([str(n) for n in gb_plane])})
if gb_energy:
query_params.update(
{"gb_energy_min": gb_energy[0], "gb_energy_max": gb_energy[1]}
)
if separation_energy:
query_params.update(
{"w_sep_min": separation_energy[0], "w_sep_max": separation_energy[1]}
)
if rotation_angle:
query_params.update(
{
"rotation_angle_min": rotation_angle[0],
"rotation_angle_max": rotation_angle[1],
}
)
if rotation_axis:
query_params.update(
{"rotation_axis": ",".join([str(n) for n in rotation_axis])}
)
if sigma:
query_params.update({"sigma": sigma})
if type:
query_params.update({"type": type.value})
if chemsys:
query_params.update({"chemsys": chemsys})
if pretty_formula:
query_params.update({"pretty_formula": pretty_formula})
query_params = {
entry: query_params[entry]
for entry in query_params
if query_params[entry] is not None
}
return super()._search(
num_chunks=num_chunks,
chunk_size=chunk_size,
all_fields=all_fields,
fields=fields,
**query_params,
)
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