# Nucleon 是 HeurAMS 软件项目使用的基于 TOML 的专有源文件格式, 版本 5
# 建议使用的 MIME 类型: application/vnd.xyz.imwangzhiyu.heurams-nucleon.v5+toml

["__metadata__"]
["__metadata__.attribution"] # 版权元信息
author = "__heurams__"
group = "高考古诗文"
name = "虞美人"
license = "CC-BY-SA 4.0"
desc = "高考古诗文 - 虞美人"

["__metadata__.annotation"] # 键批注
note = "笔记"
keyword_note = "关键词翻译"
translation = "语句翻译"

["__metadata__.formation"] # 文件配置
delimiter = "/"
tts_text = "eval:payload['content'].replace('/', '')"

["__metadata__.orbital.puzzles"] # 谜题定义
# 我们称 "Recognition" 为 recognition 谜题的 alia
"Recognition" = { __origin__ = "recognition", __hint__ = "", primary = "eval:payload['content']", secondary = ["eval:payload['keyword_note']", "eval:payload['note']"], top_dim = ["eval:payload['translation']"] }
"SelectMeaning" = { __origin__ = "mcq", __hint__ = "eval:payload['content']", primary = "eval:payload['content']", mapping = "eval:payload['keyword_note']", jammer = "eval:list(payload['keyword_note'].values())", max_riddles_num = "eval:default['mcq']['max_riddles_num']", prefix = "选择正确项: " }
"FillBlank" = { __origin__ = "cloze", __hint__ = "", text = "eval:payload['content']", delimiter = "eval:metadata['formation']['delimiter']", min_denominator = "eval:default['cloze']['min_denominator']"}

["__metadata__.orbital.schedule"] # 内置的推荐学习方案
quick_review = [["FillBlank", "1.0"], ["SelectMeaning", "0.5"], ["Recognition", "1.0"]]
recognition = [["Recognition", "1.0"]]
final_review = [["FillBlank", "0.7"], ["SelectMeaning", "0.7"], ["Recognition", "1.0"]]


["春花秋月何时了, 往事知多少."]
note = []
content = "春花/秋月/何时/了/, 往事/知/多少./"
translation = "春天的花, 秋天的月, 什么时候才能了结? 往事又能知道有多少! "
keyword_note = {"了" = "了结, 完结", "往事" = "过去的事情"}

["小楼昨夜又东风, 故国不堪回首月明中."]
note = []
content = "小楼/昨夜/又/东风/, 故国/不堪/回首/月明/中./"
translation = "昨夜小楼上又吹来了春风, 在这皓月当空的夜晚, 怎承受得了回忆故国的伤痛. "
keyword_note = {"东风" = "春风", "故国" = "指南唐故都金陵", "不堪" = "不能忍受", "回首" = "回忆"}

["雕阑玉砌应犹在, 只是朱颜改."]
note = []
content = "雕阑/玉砌/应/犹在/, 只是/朱颜/改./"
translation = "精雕细刻的栏杆、玉石砌成的台阶应该还在, 只是所怀念的人已衰老. "
keyword_note = {"雕阑玉砌" = "指远在金陵的南唐故宫", "应犹" = "应该还", "朱颜改" = "指所怀念的人已衰老"}

["问君能有几多愁, 恰是一江春水向东流."]
note = []
content = "问君/能/有/几多/愁/, 恰是/一江/春水/向/东流./"
translation = "要问我心中有多少哀愁, 就像这不尽的滔滔春水滚滚东流. "
keyword_note = {"君" = "作者自称", "能" = "或作'都'、'那'、'还'、'却'", "几多" = "多少", "恰是" = "正像是"}