from abc import (
ABC,
abstractmethod,
)
from typing import (
List,
Optional,
)
import numpy as np
[docs]
class DeviManager(ABC):
r"""A class for model deviation management."""
MAX_DEVI_V = "max_devi_v"
MIN_DEVI_V = "min_devi_v"
AVG_DEVI_V = "avg_devi_v"
MAX_DEVI_F = "max_devi_f"
MIN_DEVI_F = "min_devi_f"
AVG_DEVI_F = "avg_devi_f"
def __init__(self) -> None:
super().__init__()
self.ntraj = 0
def _check_name(self, name: str):
assert name in (
DeviManager.MAX_DEVI_V,
DeviManager.MIN_DEVI_V,
DeviManager.AVG_DEVI_V,
DeviManager.MAX_DEVI_F,
DeviManager.MIN_DEVI_F,
DeviManager.AVG_DEVI_F,
), f"Error: unknown deviation name {name}"
[docs]
def add(self, name: str, deviation: np.ndarray) -> None:
r"""Add a model deviation into this manager.
Parameters
----------
name : str
The name of the deviation. The name is restricted to
(DeviManager.MAX_DEVI_V, DeviManager.MIN_DEVI_V,
DeviManager.AVG_DEVI_V, DeviManager.MAX_DEVI_F,
DeviManager.MIN_DEVI_F, DeviManager.AVG_DEVI_F)
deviation : np.ndarray
The model deviation is a one-dimensional array extracted
from a trajectory file.
"""
self._check_name(name)
return self._add(name, deviation)
@abstractmethod
def _add(self, name: str, deviation: np.ndarray) -> None:
pass
[docs]
def get(self, name: str) -> List[Optional[np.ndarray]]:
r"""Gat a model deviation from this manager.
Parameters
----------
name : str
The name of the deviation. The name is restricted to
(DeviManager.MAX_DEVI_V, DeviManager.MIN_DEVI_V,
DeviManager.AVG_DEVI_V, DeviManager.MAX_DEVI_F,
DeviManager.MIN_DEVI_F, DeviManager.AVG_DEVI_F)
"""
self._check_name(name)
self._check_data()
return self._get(name)
@abstractmethod
def _get(self, name: str) -> List[Optional[np.ndarray]]:
pass
[docs]
@abstractmethod
def clear(self) -> None:
r"""Clear all data in this manager."""
pass
@abstractmethod
def _check_data(self) -> None:
r"""Check if data is valid"""
pass