170 lines
5.5 KiB
Python
170 lines
5.5 KiB
Python
import pathlib
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import toml
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import time
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import auxiliary as aux
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class Electron():
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"""电子: 记忆分析元数据及算法"""
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algorithm = "SM-2" # 暂时使用 SM-2 算法进行记忆拟合, 考虑 SM-15 替代
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"""
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content = "" # 内容
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efactor = 2.5 # 易度系数, 越大越简单, 最大为5
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real_rept = 0 # (实际)重复次数
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rept = 0 # (有效)重复次数
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interval = 0 # 最佳间隔
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last_date = 0 # 上一次复习的时间戳
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next_date = 0 # 将要复习的时间戳
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is_activated = 0 # 激活状态
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# *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
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last_modify = 0 # 最后修改时间戳(此处是UNIX时间戳)
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"""
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def __init__(self, content: str, data: dict):
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self.content = content
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self.efactor = data.get('efactor', 2.5)
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self.real_rept = data.get('real_rept', 0)
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self.rept = data.get('rept', 0)
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self.interval = data.get('interval', 0)
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self.last_date = data.get('last_date', 0)
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self.next_date = data.get('next_date', 0)
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self.is_activated = data.get('is_activated', 0)
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self.last_modify = time.time()
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def activate(self):
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self.is_activated = 1
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def modify(self, var: str, value):
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setattr(self, var, value)
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self.last_modify = time.time()
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def export_data(self):
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return {
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'efactor': self.efactor,
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'real_rept': self.real_rept,
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'rept': self.rept,
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'interval': self.interval,
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'last_date': self.last_date,
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'next_date': self.next_date,
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'is_activated': self.is_activated
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}
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def revisor(self, quality):
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"""SM-2 算法迭代决策机制实现
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根据 quality(0 ~ 5) 进行参数迭代最佳间隔
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quality 由主程序评估
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Args:
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quality (int): 记忆保留率量化参数
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"""
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if quality == -1:
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return -1
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self.efactor = self.efactor + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02))
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self.efactor = max(1.3, self.efactor)
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if quality < 3:
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# 若保留率低于 3，重置重复次数
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self.rept = 0
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self.interval = 0 # 设为0，以便下面重新计算 I(1)
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else:
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self.rept += 1
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self.real_rept += 1
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if self.rept == 0: # 刚被重置或初次激活后复习
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self.interval = 1 # I(1)
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elif self.rept == 1:
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self.interval = 6 # I(2) 经验公式
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else:
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self.interval = round(self.interval * self.efactor)
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self.last_date = aux.get_daystamp()
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self.next_date = aux.get_daystamp() + self.interval
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def __str__(self):
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return (f"记忆单元预览 \n"
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f"内容: '{self.content}' \n"
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f"易度系数: {self.efactor:.2f} \n"
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f"已经重复的次数: {self.rept} \n"
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f"下次间隔: {self.interval} 天 \n"
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f"下次复习日期时间戳: {self.next_date}")
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def __eq__(self, other):
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if self.content == other.content:
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return 1
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return 0
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def __hash__(self):
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return hash(self.content)
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@staticmethod
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def placeholder():
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return Electron("电子对象样例内容", {})
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@staticmethod
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def import_from_file(path: pathlib.Path):
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name = path.name.replace(path.suffix, "")
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with open(path, 'r') as f:
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all = toml.load(f)
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lst = list()
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for i in all.keys():
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lst.append(Electron(i, all[i]))
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return (name, lst)
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@staticmethod
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def save_to_file(electron_dictized, path: pathlib.Path):
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with open(path, 'w') as f:
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toml.dump(electron_dictized, f)
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class Nucleon():
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"""核子: 材料元数据"""
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def __init__(self, content: str, data: dict):
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self.metadata = data
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self.content = content
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@staticmethod
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def import_from_file(path: pathlib.Path):
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name = path.name.replace(path.suffix, "")
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with open(path, 'r') as f:
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all = toml.load(f)
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lst = list()
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for i in all.keys():
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lst.append(Nucleon(i, all[i]))
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return (name, lst)
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@staticmethod
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def save_to_file(nucleon_dictized, path: pathlib.Path):
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with open(path, 'w') as f:
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toml.dump(nucleon_dictized, f)
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@staticmethod
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def placeholder():
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return Nucleon("核子对象样例内容", {})
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class AtomicFile():
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def __init__(self, path, type_="unknown"):
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self.path = path
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self.type_ = type_
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if type_ == "nucleon":
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self.name, self.datalist = Nucleon.import_from_file(pathlib.Path(path))
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if type_ == "electron":
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self.name, self.datalist = Electron.import_from_file(pathlib.Path(path))
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def save(self):
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dictobj = {i.content: i.export_data() for i in self.datalist}
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print(dictobj)
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if self.type_ == "nucleon":
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Nucleon.save_to_file(dictobj, self.path)
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if self.type_ == "electron":
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Electron.save_to_file(dictobj, self.path)
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def get_full_content(self):
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if self.type_ == "nucleon":
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text = ""
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for i in self.datalist:
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text += i.content
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return text
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return ""
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def get_len(self):
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return len(self.datalist)
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class Atom():
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@staticmethod
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def placeholder():
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return (Electron.placeholder(), Nucleon.placeholder()) |