Saravanan666/pypi-org huggingface dataset:
Package: kira_llm
That's right. I'mKira✍️Kirais a transformer built with JAX andEquinox, which is IMHO the best
neural network library built on top of JAX out there, because of its simple and
elegant design.The role ofKirais to serve as a baseline transformer implementation, which
is very easy to understand and extend.Kiranow allows for KV caching. It also allows you to interpolate
between Multi-Query Attention (MQA) and "regular" Multi-Head Attention (MHA).
This is different from other MHA implementations, where most of the time you can
only set the number of heads and that's it.Kiraoffers more flexibility in
that regard.These features ofKira(the interpolation between MQA and MHA and the RoPE
embeddings) will soon be integrated in the main Equinox repository at which
pointKira's MHA implementation will be replaced with the built-in Equinox's
MHA.Kiracan also be used as an encoder. Simply passmask=Nonewhen you callKiraand there will be no masking in the MHA (i.e. making it an encoder).To get started withKira, you can either install it withpip3 install kira_llmor simply clone the repository and cherry-pick what you need.ContributingTo contribute to this project, you'll need to fork this repository and runpoetry installwhich will install all of the dependencies. Then, simply make your changes and
start a pull request. The philosophy of this repository is to be simple and
understandable.
Package: kirami-cli
KiramiCLI✨ KiramiBot 命令行工具 ✨文档|安装|KiramiBot 文档功能创建新的 KiramiBot 项目启动 KiramiBot管理插件创建新的插件搜索/安装/更新/卸载在官方商店上发布的插件管理适配器创建新的适配器搜索/安装/更新/卸载在官方商店上发布的适配器管理驱动器搜索/安装/更新/卸载在官方商店上发布的驱动器从 NoneBot2 迁移到 KiramiBot支持 CLI 插件和运行脚本使用完整使用文档请参考文档。安装使用 pipx 安装pipxinstallkirami-cli命令行使用kirami--helpWarning如果找不到kirami命令，请尝试pipx ensurepath来添加路径到环境变量kirami create (init)创建新的 KiramiBot 项目kirami run在当前目录启动 KiramiBotkirami driver管理驱动器kirami plugin管理插件kirami adapter管理适配器kirami self管理 CLI 内部环境kirami migrate从 NoneBot2 迁移到 KiramiBotkirami <script>运行脚本交互式使用kirami开发翻译生成模板pdmrunextract初始化语言翻译文件或者更新现有语言翻译文件pdmruniniten_US更新语言翻译文件pdmrunupdate编译语言翻译文件pdmruncompile许可证本项目原始代码来自NB CLI，以相同的许可证发布。
Package: kirami-plugin-beauty-rate
kirami-plugin-beauty-rate✨ 让 Bot 为你的颜值打分！ ✨📖 介绍颜值打分插件。使用百度云人脸识别云服务💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-beauty-ratepdmpdmaddkirami-plugin-beauty-ratepoetrypoetryaddkirami-plugin-beauty-ratecondacondainstallkirami-plugin-beauty-rate打开 KiramiBot 项目根目录下的配置文件文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kirami_plugin_beauty_rate"]⚙️ 配置打开 KiramiBot 项目的配置文件，在[beauty_rate]部分添加下表中的必填配置api_key和secret_key可以从这里获取配置项必填默认值说明api_key是无无secret_key是无无🎉 使用指令表指令权限需要@范围说明颜值评分无否私聊/群聊别名：颜值打分效果图理论上，这里应该有些图片❤️ 鸣谢感谢以下 开发者 和 Github项目 对 本项目 作出的贡献：（排名不分先后）zhulinyv/NJS：基于 NoneBot 的 QQ 机器人（本项目直接参考）
Package: kirami-plugin-boardgame
kirami-plugin-boardgame✨ 棋类游戏 五子棋、围棋、黑白棋 ✨📖 介绍棋类游戏插件，包含五子棋、围棋、黑白棋。抄自隔壁nonebot-plugin-boardgame💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-boardgamepdmpdmaddkirami-plugin-boardgamepoetrypoetryaddkirami-plugin-boardgamecondacondainstallkirami-plugin-boardgame打开 KiramiBot 项目根目录下的配置文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kiramit_plugin_boardgame"]🎉 使用指令表指令权限需要@范围说明围棋/五子棋/黑白棋群员是群聊开启一局棋类游戏落子 字母+数字群员否群聊如：落子 A1停止下棋群员否群聊停止一局游戏查看棋盘群员否群聊查看当前棋盘跳过回合群员否群聊黑白棋规则下，跳过你的回合悔棋群员否群聊悔棋重载五子棋/围棋/黑白棋棋局群员否群聊继续群内已停止的棋局下棋
Package: kirami-plugin-helloworld
kirami-plugin-helloworld✨ Hello World! ✨📖 介绍你好，世界💿 安装使用包管理器安装在 kiramibot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippip install kirami-plugin-helloworldpdmpdm add kirami-plugin-helloworldpoetrypoetry add kirami-plugin-helloworldcondaconda install kirami-plugin-helloworld打开 kiramibot 项目根目录下的kirami.config.toml文件, 在[plugin]部分追加写入plugins = ["kirami_plugin_helloworld"]🎉 使用指令表指令权限需要@范围说明hello无否私聊/群聊world
Package: kirami-plugin-hitokoto
kirami-plugin-hitokoto✨ 有一句话能打动你的心 ✨📖 介绍一言。数据来源于Hitokoto💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-hitokotopdmpdmaddkirami-plugin-hitokotopoetrypoetryaddkirami-plugin-hitokotocondacondainstallkirami-plugin-hitokoto打开 KiramiBot 项目根目录下的配置文件文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kirami_plugin_hitokoto"]🎉 使用指令表指令权限需要@范围说明一言无是私聊/群聊随机一条一言
Package: kirami-plugin-horserace
kirami-plugin-horserace✨ 群内赛马小游戏 ✨📖 介绍适用于KiramiBot的赛马小游戏插件由zhenxun_bot_HorseRace&nonebot_plugin_horserace适配而来,稍作修改，添加合并消息返回配置项💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-horseracepdmpdmaddkirami-plugin-horseracepoetrypoetryaddkirami-plugin-horseracecondacondainstallkirami-plugin-horserace打开 KiramiBot 项目根目录下的配置文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kiramit_plugin_horserace"]⚙️ 配置在 KiramiBot 项目的配置文件中添加下表中的必填配置配置项必填默认值说明send_forward_msg否True将图片以合并消息发送pic_font_size否16图片字体大小setting_track_length否20跑道长度setting_random_min_length否0随机位置事件，最小能到的跑道距离setting_random_max_length否15随机位置事件，最大能到的跑道距离base_move_min否1每回合基础移动力最小值base_move_max否3每回合基础移动力最大值max_player否8最大支持玩家数min_player否2最小支持玩家数setting_over_time否120超时允许重置最少时间，秒event_rate否450事件概率 = event_rate / 1000🎉 使用指令表指令权限需要@范围说明赛马创建群员否群聊创建游戏赛马加入 [你的马儿名称]群员否群聊加入赛马游戏赛马开始群员否群聊开始游戏赛马重置主人否群聊重置已有赛马赛马清空主人否群聊清空已有赛马赛马事件重载主人否群聊重载事件文件效果图
Package: kirami-plugin-qweather
kirami-plugin-qweather✨ 基于和风天气的天气查询、订阅与推送插件 ✨📖 介绍基于和风天气的天气查询、订阅与推送插件在nonebot-plugin-heweather的基础上添加了私聊与群聊的天气订阅与推送功能💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-qweatherpdmpdmaddkirami-plugin-qweatherpoetrypoetryaddkirami-plugin-qweathercondacondainstallkirami-plugin-qweather打开 KiramiBot 项目根目录下的配置文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kiramit_plugin_qweather"]⚙️ 配置在 KiramiBot 项目的配置文件中添加下表中的必填配置配置项必填默认值说明qweather_apikey是无APIKEYqweather_apitype是无api 类型qweather_hourlytype否1逐小时天气返回类型：1 = 未来12小时 (默认值) 2 = 未来24小时api类型说明0 = 普通版 免费订阅 (7 天天气预报, 1000次请求/天)1 = 个人开发版 标准订阅 (7 天天气预报)2 = 商业版 (7 天天气预报)APIKEY获取方式1、注册和风天气账号进入官网注册https://id.qweather.com/#/login2、进入控制台登录后，点击 “和风天气开发者控制台”3、创建项目点击控制台左侧 “项目管理”，然后点击 “创建项目”，根据提示自行填写“选择订阅” -> “免费订阅”，“设置 KEY” -> “Web API”，都填好后“创建”4、获取 key返回 “项目管理”，可以看到创建的项目，点击 KEY 下面的 “查看”。🎉 使用指令表指令权限需要@范围说明天气用户否全部查询天气，支持跟随城市名、以英文逗号分隔的经纬度 如：北京天气、120.38946,36.07223天气、天气 武汉天气订阅用户否全部私人天气订阅，将以私聊形式推送（请确保有机器人好友），命令格式 天气订阅 [地名/经纬度] [推送时间(小时)] 如：天气订阅 北京 10。不跟随推送时间参数默认推送时间为早上8点查询天气订阅用户否全部查询你的私人天气订阅删除天气订阅群员否群聊按订阅id删除你的天气订阅，订阅id请先执行查询命令获取群天气订阅群员否群聊群天气订阅，将在群聊内推送并@订阅者，命令格式同私人订阅查询群天气订阅群员否群聊查询你在本群的天气订阅删除群天气订阅群员否群聊按订阅id删除你在本群的天气订阅，订阅id请先执行查询命令获取效果图
Package: kirami-plugin-sentry
kirami-plugin-sentry✨在 Sentry.io 上进行 KiramiBot 服务日志查看、错误处理 ✨📖 介绍在 Sentry.io 上进行 KiramiBot 服务日志查看、错误处理，以及群聊消息提醒💿 安装在 KiramiBot 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令pippipinstallkirami-plugin-sentrypdmpdmaddkirami-plugin-sentrypoetrypoetryaddkirami-plugin-sentrycondacondainstallkirami-plugin-sentry打开 KiramiBot 项目根目录下的配置文件, 以kirami.toml为例，在[plugin]部分追加写入plugins=["kiramit_plugin_sentry"]⚙️ 配置在 KiramiBot 项目的配置文件中添加下表中的必填配置配置项必填默认值说明sentry_dsn是无Sentry Docssentry_environment否NoneSentry Docssentry_default_integrations否Falsesentry-python #653🎉 使用填写必须配置项sentry_dsn，即刻开始 sentry 之旅！指令表本插件提供了一个仅SUPERUSERS能使用的命令。可以用来快捷查看错误信息指令权限需要@范围说明追踪主人否当前会话回复报错消息追踪主人否私聊/群聊追踪 <msg_id><msg_id> 代表这是一个必填的参数，即用<与>包裹的文本。效果图理论上，这里应该有几张效果图如果你觉得这个插件很赞, 欢迎返图!📄 许可证本项目使用MIT许可证开源THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: kira-setup
Kira Setup BotCLI utility to automate routine work with creating new projects.Part of the@kirabots family.Installationpip install kira-setupRunningkira-setup group_or_user_name/project_name --token=YOUR_ACCESS_TOKENFeaturesWe use this CLI to setup high quality standards for our repository.
Features that we care about:Protectedmasterand tags for releases onlyMandatory code reviewsIntegration withkira-staleandkira-release
Package: kirbacterai
This is the unofficial APIInstallationpipinstallkirbacteraiCode exampleimportasynciofromkirbacteraiimportClientasyncdefmain():client=Client()awaitclient.auth_as_guest()# You can use client.auth_with_token(token) insteadlink="https://c.ai/c/0FGHpcylr6O0l46xHrTMzRGnqAU6beVz0k3i294wbUQ"character=awaitclient.get_character_from_link(link)chat=awaitcharacter.chat_create()character_reply=awaitchat.send_message("Hello!")print(character_reply)asyncio.get_event_loop().run_until_complete(main())Using an Access TokenSome parts of the API, like managing a conversation requires for you to be logged in using anaccessToken.
To get it, you can open your browser, go to thecharacter.ai websiteinlocalStorage.— To do so:❒ Open the Character AI website in your browser❒ Open the developer toolsF12and go to theApplicationtab.❒ Go to theStoragesection and click onLocal Storage.❒ Look for the@@auth0spajs@@::dyD3gE281MqgISG7FuIXYhL2WEknqZzv::https://auth0.character.ai/::openid profile email offline_accesskey.❒ Open the body and copy the access token.Node.js versionMost of the stuff was taken from anotherJavaScriptrepository:Character AI Unofficial Node API
Package: kirby-transform
Kirby TransformKirby is an extremely strong circle, and that's all the explanation I need.AboutKirby Transform is middleware used to define a common data format and transformation logic to make it easier for me to
build IOT pipelines. This transformation logic is split into:PreprocessorThe preprocessor is responsible for:Input validationGenerating a set of data points, and metadata that is useful for tracking/debugging.Generating a common output format to be used down the line to send to specific data sources.OutputsOutputs should be (mostly) self contained ways of generating data in the required format and sending data to
storage solutions.Currently Implemented:InfluxDB2AWS TimestreamMotivationI'm finding that I'm spending a lot of time creating and maintaining IOT Device -> Data Sink pipelines compared
to creating the devices themselves.By ensuring that things willjust workif I put the data in a common input format, I should be able to build the
pipeline once and have something reasonably low maintenance.FormatTop levelMessages are expected to be valid JSON dictionariescollector:String [Required]Name of the collection sourcedata:List of dictonaries [Required]The actual data to send. See below for what this looks like.data_tags:Dict [Optional]Dictionary of tags to associate with every data pointdestination:String [Required]Initial Destination (EG AWS IOT)language:String [Required]Language the code is written inmessages:List[dict] [Optional]List of dictionaries with formatted as dict(fields,tags,timestamp). See the *data* section for more details.meta_tags:Dict [Optional]Dictonary of tags to associate with meta fieldsplatform:String [Required]Platform the collector is running on (arduino, router etc)timestamp:Unix Epoch [Optional]If missing then this will be treated as when the data was recieved (Which is a really bad workaround).uptime:Float [Optional]Total uptime if this is something continously sending dataversion:String [Required]Version of the thing sending dataDataThe actual datapoints come in as a list of dictionaries. The naming scheme is basically defined because InfluxDB
is the first thing that I created this for.tags:Dictionary [Optional]Dictonary of tags to add to individual datapointsfields:Dictonary [Required]Datapoint fields.timestamp:Unix Epoch [Optional]Timestamp for the data (Resolution in seconds).  If this is missing then the report timestamp will be usedExamplesThe best place to look for examples is in thetestsfolder. There are examples of data and how to use,
wrapped up as test cases.StructureAs this is middleware for very inter related projects, this how I'm expecting to structure the project:This package is uploaded to PiPiThings that depend on this package (Eg Lambdas) should be put in their own repo, being submoduled to this one to make
testing everything significantly easier.Dependent packages should be pinned to a specific Kirby Transform version, and should be updated as needed.
Package: kiri
KiriKiri is a Python library that makes it simple to solve AI tasks without requiring any data.Kiri is built around solving tasks with transfer learning. It implements state-of-the-art AI models that are general enough to solve real world tasks with no data required from the user.Out of the box tasks you can solve with Kiri:Conversational question answering in English (for FAQ chatbots, text analysis, etc.)Text Classification in 100+ languages (for email sorting, intent detection, etc.)Image Classification (for object recognition, OCR, etc.)Text Vectorisation in 50+ languages (semantic search for ecommerce, documentation, etc.)Summarisation in English (TLDRs for long documents)Emotion detection in English (for customer satisfaction, text analysis, etc.)Text Generation (for idea, story generation and broad task solving)For more specific use cases, you can adapt a task with little data and a couple of lines of code using finetuning. We are adding finetuning support for all tasks soon.You can run all tasks locally or in production with our optimised inferenceAPI, where you only pay for usage. It includes all the tasks, models in our library and lets you upload your own finetuned models.⚡Getting startedInstallation, few minute introduction💡ExamplesSample problems solved using Kiri📙DocsIn-depth documentation for advanced usageGetting startedInstallationInstall Kiri via PyPi:pipinstallkiriBasic task solvingfromkiriimportKiricontext="Take a look at the examples folder to see use cases!"# Use our inference APIk=Kiri(api_key="abc")# Or run locallyk=Kiri(local=True)# Start building!answer=k.qa("Where can I see what to build?",context)print(answer)# Prints"the examples folder"Basic finetuning and uploadingfromkiri.modelsimportT5fromkiri.tasksimportTextGenerationtg=TextGeneration(T5,local=True)# Any text works as training datainp=["I really liked the service I received!","Meh, it was not impressive."]out=["positive","negative"]# Finetune with a single line of codetg.finetune(inp,out)# Use your trained modelprediction=tg("I enjoyed it!")print(prediction)# Prints"positive"# Upload to Kiri for production ready inferenceimportkirimodel=tg.model# Describe your modelmodel.name="t5-sentiment"model.description="Predicts positive and negative sentiment"kiri.upload(model,api_key="abc")Why Kiri?No experience neededEntrance to practical AI should be simpleGet state-of-the-art performance in your task without being an expertData is a bottleneckUse AI without needing access to "big data"With transfer learning, no data is required, but even a small amount can adapt a task to your niche.There is an overwhelming amount of modelsWe implement the best ones for various tasksA few general models can accomplish more with less optimisationDeploying models cost effectively is hard workIf our models suit your use case, no deployment is neededAdapt and deploy your own model with a couple of lines of codeOur API scales, is always available, and you only pay for usageExamplesTake a look at theexamples folder.DocumentationCheck out ourdocs.
Package: kiri-pathfinding
kiri-pathfindingA toy module to generate a map and find the shortest path from two points on the mapUsagefrommatplotlibimportpyplotaspltfromkiri_pathfinding.map_generatorimportgenerate_map,draw_mapfromkiri_pathfinding.pathfindingimportPathFinding,draw_path# genetate a map and find shortest pathdata_map=generate_map(20,20)pathfinding=PathFinding(data_map)path=pathfinding.find((0,0),(19,19))# visualizefig,ax=plt.subplots()draw_map(data_map,ax)draw_path(path,ax)ExampleThe image above visualizes the generated map and the found path from point (0, 0) to point (19, 19).Different colors on the map mean different terrains.
Specifically, the green is grassland
and the gray means roads, providing a lower movement cost.
The brown and blue denote mud and river respectively, which are harder to pass.
Package: kirisearch
Kiri SearchKiri Search is an unmaintained fork ofKiri NLEwith added search functionality.Getting startedInstallationInstall Kiri via PyPi:pipinstallkirisearchBasic usagefromkiri_searchimportKiri,Document# Unprocessed documentsdocuments=[Document("Look at examples to see awesome use cases!"),Document("Check out the docs to see what's possible!")]# Use our inference APIkiri=Kiri(api_key="abc")# Or run locallykiri=Kiri(local=True)# Process documentskiri.upload(documents)# Start building!search_results=kiri.search("What are some cool apps that have been built?")print(search_results.to_json())# Prints{"max_score":0.3804888461635889,"total_results":2,"results":[{"document":{"id":"LzhtWcpV2eoMk8GJwaw7na","content":"Look at examples to see awesome use cases!"},"score":0.3804888461635889,"preview":" Look at examples to see awesome use cases!"},{"document":{"id":"bcLb8xUK585Zm6rZrwj89A","content":"Check out the docs to see what's possible!"},"score":0.1742559312454076,"preview":" Check out the docs to see what's possible!"}]}Why Kiri?No experience neededEntrance to practical AI should be simpleGet state-of-the-art performance in your task without being an expertThere is an overwhelming amount of modelsWe implement the best ones for various use casesA few general models can accomplish more with less optimisationDeploying models cost effectively is hard workIf our models suit your use case, no deployment is neededOur API scales, is always available, and you only pay for usage
Package: kiri-walkgame
kiri-walkgameThis is a mini game to visualize the path-finding algorithm.To play this game, you just need to click the map twice. The first clicking will place a cat namedKiri, and the following one will place a box. Then Kiri will find his way to get inside the box immediately.Usagekiri_walkgame-s20CreditsMusic track: Fantasy by Pufino
Source:https://freetouse.com/musicCopyright Free Music for Video
Package: kirje
KirjeA framing library for content with headers and body.Installation:pip install kirjeUsageImportKirjeandKirjeDetails. InitializeKirjeDetailsand use it as parameter to construct concreteKirjeobject.fromkirjeimportKirje,KirjeDetailsdetails=KirjeDetails(headers={'ID':2,'Title':'Hello world','Created':'2023-10-27 14:18:14','Modified':'2023-10-27 14:18:25'},content="Hello\nworld.")message=Kirje(details)message.display("rounded")Expected output:╭──────────Hello world──────────╮
│ ID: 2                         │
│ Created: 2023-10-27 14:18:14  │
│ Modified: 2023-10-27 14:18:25 │
├───────────────────────────────┤
│ Hello                         │
│ world.                        │
╰───────────────────────────────╯StylesDefault:+----------Hello world----------+
| ID: 2                         |
| Created: 2023-10-27 14:18:14  |
| Modified: 2023-10-27 14:18:25 |
+-------------------------------+
| Hello                         |
| world.                        |
+-------------------------------+Rounded:╭──────────Hello world──────────╮
│ ID: 2                         │
│ Created: 2023-10-27 14:18:14  │
│ Modified: 2023-10-27 14:18:25 │
├───────────────────────────────┤
│ Hello                         │
│ world.                        │
╰───────────────────────────────╯Doubled style letter:+==========Hello world==========+
‖ ID: 2                         ‖
‖ Created: 2023-10-27 14:18:14  ‖
‖ Modified: 2023-10-27 14:18:25 ‖
+===============================+
‖ Hello                         ‖
‖ world.                        ‖
+===============================+Streamlined style letter:+==========Hello world==========+
| ID: 2                         |
| Created: 2023-10-27 14:18:14  |
| Modified: 2023-10-27 14:18:25 |
+-------------------------------+
| Hello                         |
| world.                        |
+===============================+
Package: kirlent-docutils
kirlent_docutils is a set of tools
for generating impress.js and reveal.js presentations
from restructured text documents.Since it aims to be compatible with any restructured text tool,
it doesn’t define its own directives
and instead uses only standarddocutilsfeatures.At the moment, the tools are functional
but their usage and the slide markup might change.
This is also the reason why there is no documentation yet.
You can check out the files in the “examples” directory.
Package: kirlent-sphinx
kirlent_sphinx is a Sphinx extension that is primarily meant to be used with
theKırlenteducational content management system, although it can be used
as a regular Sphinx extension.Featureskirlent_sphinx provides the following components:An extendedtabledirective derived from theCloud Sphinx Themeproject.Aslidedirective and corresponding HTML themes usingRevealJSorImpressJS.Getting startedYou can install kirlent_sphinx with pip:pip install kirlent_sphinxTo enable it in your project, make the following changes inconf.py:Addkirlent_sphinxto extensions:extensions = ["kirlent_sphinx"]Set the theme to use revealjs or impressjs using one of the below lines:html_theme = "kirlent_revealjs"
html_theme = "kirlent_impressjs"Disable index generation:html_use_index = FalseUsageFor the extendedtabledirective, consult the documentation
of thetable_stylingextension of theCloud Sphinx Themeproject.Theslidedirectives can support most of thedata-attributes
as described in the documentations of theRevealJSandImpressJSprojects.The themes includeTailwindutility classes for styling:.. slide:: Slide title

   .. container:: columns

      .. container:: column w-1/3 bg-blue-lighter

         - item 1a
         - item 1b

      .. container:: column bg-red-lighter

         - item 2

   .. speaker-notes::

      some extra explanationLicenseCopyright (C) 2019 H. Turgut Uyar <uyar@tekir.org>kirlent_sphinx is released under the BSD license. Read the includedLICENSE.txtfile for details.kirlent_sphinx contains code derived from theCloud Sphinx Themeproject
which is released under the BSD license. Read the includedLICENSE_cloud_spheme.txtfile for details.kirlent_sphinx contains code derived from thesphinxjp.themes.revealjsproject which is released under the MIT license. Read the includedLICENSE_sphinxjp.themes.revealjs.txtfile for details.kirlent_sphinx contains code from theRevealJSproject which is
released under the MIT license. Read the includedLICENSE_revealjs.txtfile for details.kirlent_sphinx contains code from theImpressJSproject which is
released under the MIT license. Read the includedLICENSE_impressjs.txtfile for details.kirlent_sphinx contains code from theTailwindproject which is
released under the MIT license. Read the includedLICENSE_tailwind.txtfile for details.
Package: kirsche
kirscheKirsche is a python package to build up connections between papers.How to UseInstall the package:pip install kirscheIn your command line, runkirsche --helpto see possible commands.CaveatsAbout SemanticScholar APIWe are usingSemanticScholar APIto get the paper metadata. The API has some rate limits (100 requests per 5 minutes).A possible workaround for large number of papers is to split them into batches.Todo:Use API keys from SemanticScholar.
Package: kirzner-serializer
Welcome to Kirzner's Serializer!This module supports two formats:jsonandxmlEach serializer contains:dump(obj, filepath)- serializeobjinto the file with providedfilepathdumps(obj)- serializeobjand returns string resultload(obj, filepath)- return the result of deserialization content from the file with providedfilepathloads(obj, s)- return the result of deserialization content from the provided strings