上下文管理器与进一步移植

src/heurams/kernel/particles/electron.py

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+#!/usr/bin/env python3
+import pathlib
+import toml
+import time
+import heurams.services.timer as timer
+
+class Electron:
+    """电子: 记忆分析元数据及算法"""
+    algorithm = "SM-2"  # 暂时使用 SM-2 算法进行记忆拟合, 考虑 SM-15 替代
+
+    def __init__(self, content: str, metadata: dict):
+        self.content = content
+        self.metadata = metadata
+        if metadata == {}:
+            # print("NULL")
+            self._default_init()
+
+    def _default_init(self):
+        defaults = {
+            'efactor': 2.5,  # 易度系数, 越大越简单, 最大为5
+            'real_rept': 0,  # (实际)重复次数
+            'rept': 0,  # (有效)重复次数
+            'interval': 0,  # 最佳间隔
+            'last_date': 0,  # 上一次复习的时间戳
+            'next_date': 0,  # 将要复习的时间戳
+            'is_activated': 0,  # 激活状态
+            # *NOTE: 此处"时间戳"是以天为单位的整数, 即 UNIX 时间戳除以一天的秒数取整
+            'last_modify': time.time()  # 最后修改时间戳(此处是UNIX时间戳)
+        }
+        self.metadata = defaults
+
+    def activate(self):
+        self.metadata['is_activated'] = 1
+        self.metadata['last_modify'] = time.time()
+
+    def modify(self, var: str, value):
+        if var in self.metadata:
+            self.metadata[var] = value
+            self.metadata['last_modify'] = time.time()
+        else:
+            print(f"警告: '{var}' 非已知元数据字段")
+
+    def revisor(self, quality: int = 5, is_new_activation: bool = False):
+        """SM-2 算法迭代决策机制实现
+        根据 quality(0 ~ 5) 进行参数迭代最佳间隔
+        quality 由主程序评估
+
+        Args:
+            quality (int): 记忆保留率量化参数
+        """
+        print(f"REVISOR: {quality}, {is_new_activation}")
+        if quality == -1:
+            return -1
+
+        self.metadata['efactor'] = self.metadata['efactor'] + (
+            0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02)
+        )
+        self.metadata['efactor'] = max(1.3, self.metadata['efactor'])
+
+        if quality < 3:
+            # 若保留率低于 3，重置重复次数
+            self.metadata['rept'] = 0
+            self.metadata['interval'] = 0  # 设为0，以便下面重新计算 I(1)
+        else:
+            self.metadata['rept'] += 1
+
+        self.metadata['real_rept'] += 1
+
+        if is_new_activation:  # 初次激活
+            self.metadata['rept'] = 0
+            self.metadata['efactor'] = 2.5
+
+        if self.metadata['rept'] == 0:  # 刚被重置或初次激活后复习
+            self.metadata['interval'] = 1  # I(1)
+        elif self.metadata['rept'] == 1:
+            self.metadata['interval'] = 6  # I(2) 经验公式
+        else:
+            self.metadata['interval'] = round(
+                self.metadata['interval'] * self.metadata['efactor']
+            )
+
+        self.metadata['last_date'] = timer.get_daystamp()
+        self.metadata['next_date'] = timer.get_daystamp() + self.metadata['interval']
+        self.metadata['last_modify'] = time.time()
+
+    def __str__(self):
+        return (
+            f"记忆单元预览 \n"
+            f"内容: '{self.content}' \n"
+            f"易度系数: {self.metadata['efactor']:.2f} \n"
+            f"已经重复的次数: {self.metadata['rept']} \n"
+            f"下次间隔: {self.metadata['interval']} 天 \n"
+            f"下次复习日期时间戳: {self.metadata['next_date']}"
+        )
+
+    def __eq__(self, other):
+        if self.content == other.content:
+            return True
+        return False
+
+    def __hash__(self):
+        return hash(self.content)
+
+    def __getitem__(self, key):
+        if key == "content":
+            return self.content
+        if key in self.metadata:
+            return self.metadata[key]
+        else:
+            raise KeyError(f"Key '{key}' not found in metadata.")
+
+    def __setitem__(self, key, value):
+        if key == "content":
+            raise AttributeError("content 应为只读")
+        self.metadata[key] = value
+        self.metadata['last_modify'] = time.time()
+
+    def __iter__(self):
+        yield from self.metadata.keys()
+
+    def __len__(self):
+        return len(self.metadata)
+
+    @staticmethod
+    def placeholder():
+        return Electron("电子对象样例内容", {})