Saravanan666/pypi-org huggingface dataset:
Package: shihua-liquid
Liquid-算法应用流程管理工具介绍liquid是一个pipeline工具，主要功能提供算法应用快捷组织功能，主要技术使用基于pluggy的hook技术。安装liquid采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$ pip install liquid-0.1-xxxxxxxxxxxx.whl快速指南python-sdk使用liquid提供python-sdk的使用方式，以下是liquid主程脚本示例：fromliquid.nodeimportNodefromliquid.pipelineimportPipelinefromliquid.ioimportDataCatalogfromliquid.hookimportHookManagerfromliquid.runnerimportSequentialRunner### 开始liquid测试### 设置函数节点对象deffirst_func(a,b):c=a+breturncdefsecond_func(c,d):e=c*dreturnedefthird_func(e):f=e+1returnffirst_node=Node(func=first_func,inputs=['a','b'],outputs='c',name='firstfunc')second_node=Node(func=second_func,inputs=['c','d'],outputs='e',name='secondfunc')third_node=Node(func=third_func,inputs=['e'],outputs=['f'],name='thirdfunc')### 创建一个pipelinetest_pipeline=Pipeline(nodes=[first_node,second_node,third_node])### catalog加载初始参数test_cache_data=DataCatalog()test_cache_data.save(data_name='a',data_obj=5)test_cache_data.save(data_name='b',data_obj=6)test_cache_data.save(data_name='d',data_obj=7)### hook_manager加载已挂载的前后处理功能函数hook_manager=HookManager()sequential_runner=SequentialRunner(pipeline=test_pipeline,catalog=test_cache_data,hook_manager=hook_manager)# print(sequential_runner.pipeline,sequential_runner.catalog,sequential_runner.hook_manager)print('--------------------------------------------------------------------------------------------------------------')sequential_runner.execute(is_release=False)print(test_cache_data.load(data_name='f'))print(test_cache_data.cache_data)### 测试过程(5+6)*7+1=78设计基于pluggy的hook技术实现灵活扩展性设计关键概念，Node，Pipeline,Runner函数节点化-Node开放挂载点-Pipeline扩展运行方式(顺序运行和并行运行)-Runner技术列表property动态属性基于pluggy的hookcalladdcache_dict设计UML图以下是设计的UML图：
Package: shihua-meoteofence
Meoteofence-数值天气预报数据预处理工具介绍meoteofence是数值天气预报的预处理程序，主要用于根据起始时间、预报时间、空间层次、气象物理量和台站坐标网格五个维度对原始气象数据进行分割。主要技术使用HDF5组织分析后的矩阵数据（以加速磁盘IO），使用hook技术组织代码（方便扩展，动态挂载），并在预测时间级别添加并行处理（使用multiprocessing以异步非阻塞方式实现）。安装meoteofence采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$ pip install meoteofence-0.1-xxxxxxxxxxxx.whl快速指南python-sdk使用meoteofence提供python-sdk的使用方式meoteofence设计为三级API。第一级API为__init__.py中的基础操作函数，包括根据给定坐标点形成对应分辨率网格，HDF5文件读写操作以及使用pygrib从grib2文件中读取数据矩阵。第二级API为组合一级API进行气象数据预处理的必要步骤，主要包括四步：（1）根据场站坐标确定需求的区域（使用正方形网格）（2）使用pygrib从grib2中抽取（1）步骤中正方形网格上的对应数据矩阵（3）按照气象物理量-空间层级两个维度的层次将矩阵数据组织存储在HDF5中（4）使用scipy对抽取的结果数据从时间维度进行内插值（插值步骤：1.对两个时间点的数据文件夹下的数据进行解析抽取存储到HDF5文件中；2.对处理后的HDF5文件进行上钻操作，拼接两个时间点的数据矩阵；3.从拼接的数据矩阵进行时间维度上的内插值，然后选择前96点作为最终结果；注意，使用两天数据是为了避免一天数据插值会始终少3个点的缺陷）第三级API（推荐）并行处理流程，优化速度；抽取算法应用必要的API，简化操作，适合不同程度开发人员使用。大致流程分为两步，第一步，气象数据预抽取解析，该步骤属低频操作，主要为了降低数据量，利用HDF5提高磁盘IO速度；第二步，对数据进行上钻操作，该步骤属高频操作，主要为了方便查询，解耦耗费IO的数据重组织和数据查询。以下为三级API的使用流程示例：载入必要程序包frommeoteofence.interfaceimportMeoteodata,data_drill_up,data_interpolatefrommeoteofenceimporthdf5_read_ndarrayimportpandasaspdimportnumpyasnp第一步，气象数据预抽取解析（示例中data文件夹为下载的原始气象数据，data_handled为预抽取解析后的气象数据存储路径，第二步数据上钻都在此文件夹中的数据上进行）### 一次执行多个文件夹folders=['20221123','20221124']fortmp_folderinfolders:### 创建气象数据对象### 配置初始参数，生成气象数据处理对象meoteodata=Meoteodata(base_folder_path='./data/{}/'.format(tmp_folder),hdf5_store_path_pre='./data_handled/{}/'.format(tmp_folder),station_location=(35.23,95.24),variable_name_list=['Temperature','Relative humidity','Total Cloud Cover','Wind speed (gust)'],resolution_ratio=0.25,grid_size=6,n_jobs=24)### 开始执行气象预处理程序result=meoteodata.run()# result = meoteodata.run(func=secondary_api_meoteo_pretreatment)print(result)第二步，从气象抽取解析结果中进行上钻操作，并整合出96时间点位的气象物理量（该步骤为频繁使用步骤，基于HDF5保证数据读取速度）### 根据变量和层级对指定文件夹下的数据进行上钻操作tmp_concat_df_a=data_drill_up(hdf5_store_path_pre='./data_handled/20221123/',variable_name='Wind speed (gust)',level_name='level_0',forecast_time=24)tmp_concat_df_b=data_drill_up(hdf5_store_path_pre='./data_handled/20221124/',variable_name='Wind speed (gust)',level_name='level_0',forecast_time=24)tmp_concat_df=pd.concat([tmp_concat_df_a,tmp_concat_df_b],axis=0)tmp_concat_df=tmp_concat_df.reset_index(drop=True)### 对目标数据集合进行内插值tmp_interpolate_df=data_interpolate(tmp_concat_df=tmp_concat_df,index_delta=0.25,kind='linear')tmp_target_df=tmp_interpolate_df.iloc[:96,:]print(tmp_target_df)另外，如需要对第一步的预抽取解析数据直接进行读取，可参造如下例程使用meoteofence的一级API：### 从指定h5中读取对应ndarray数据（此步骤为一级API，一般不使用，为更高级自定义操作提供支持）variable_name='Wind speed (gust)'#'#'Temperature' # 'Wind speed (gust)'tmp_ndarray=hdf5_read_ndarray(h5path='./data_handled/20221124/f23.h5',var_name=variable_name,level_name='level_0')# level_100_mprint(tmp_ndarray)设计基于pluggy的hook技术实现灵活扩展性基于HDF5存储解析后数据，提高数据IO速度基于multiprocessing实现异步非阻塞并行技术列表HDF5存储技术基于pluggy的hook技术气象专用pygribmultiprocessing异步非阻塞并行静态方法all设计UML图以下是设计的UML图：
Package: shihua-roshareclient
RoShare介绍RoShare是一个金融数据服务，满足金融量化的各种数据需求，它与市面上的金融数据服务的最大不同是提供进阶版的金融数据，比如基于金融新闻使用NLP技术构建的每日主题热度、基于基础数据使用经典理论构建的各类因子。技术说明RoShare的服务端和客户端都使用Python3.9.13开发，依赖第三方包如下：pandasnumpyfastapi安装使用pip安装$pipinstallshihua-roshareclient使用wheel安装$pipinstallroshareclient-0.1.1-py3-none-any.whl快速指南首先，需要注册roshare账号，使用tushare的个人token(roshare只是进阶数据的算法提供方，具体的数据服务还需要自行接入相应的基础数据，目前支持tushare的数据接入)示例1：获取每日新闻咨询fromroshareclient.client.apiimportDataAPItmp_dict={}tmp_dict['src']='eastmoney'tmp_dict['start_date']='2023-06-09+00:00:00'### url中+号表示空格tmp_dict['end_date']='2023-06-10+00:00:00'roshareclient=DataAPI(token_key=7890,token='xxxxxxxxxxxxxxxxxxx',tushare_token='xxxxxxxxxxxxxxxxxxxxxx',timeout=6000)df=roshareclient.get_tushare_news(params=tmp_dict)print(df)更多文档想要了解RoShare提供的进阶数据背后的算法设计，想要了解RoShare提供哪些进阶数据，请移步此处了解更新记录0.1.1------2023/06/10推出了新闻主题热度数据
Package: shihua-roshareservice
RoShareService介绍RoShareService是一个数据服务包，主要功能是为底层数据向外提供了一个接口服务,可实现用户权限验证和流量限制。roShareService采用Client-Server架构设计，服务端使用HTTP协议构建数据服务的OpenAPI,暂时采用GET-method；用户权限采用token机制。安装roShareService采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallroshareservice-0.1.1-xxxxxxxxxxxx.whl快速指南服务端使用对于服务端数据服务启动，首先使用roShareService命令行进行元数据库初始化,然后再启动服务端服务。以下是roshareservicectl命令行示例：$roshareservicectlset-metadb$roshareservicectlstart-service设计采用Client-Server架构设计服务端使用HTTP协议构建数据服务的OpenAPI用户权限采用token机制用户流量限制技术列表微服务-FastAPI加密验证-Token-JWT技术数据库-SQLite3设计UML图以下是设计的UML图：
Package: shihua-seaflow
About seaflowSeaflow is an algorithm orchestration tool developed based on directed acyclic graphs. Its main functions include algorithm workflow design, runtime scheduling parallel automatic optimization, cache optimization, and support for heterogeneity. Its main technologies include metaprogramming technology, Ray computing engine, and Networkx graph theory technology.DocumentationThe documentationn for user guide  is athttps://seaflow-officialdoc.netlify.appThe documentation for code comments is athttps://seaflow-codedoc.netlify.appMain ConceptsDAGrunner:DAG executor is an execution control for Seaflow runtime, which mainly manages the actual calculation and execution of directed acyclic graphs.Sequencerunner:The sequence executor is an execution control for Seaflow runtime, which mainly manages the actual calculation and execution of sequence pipelines.Scheduler:The scheduler is an orchestration control for Seaflow runtime, providing algorithm orchestration and directed acyclic graph related algorithms.Datacatalog:The data directory is a storage control for Seaflow runtime, mainly providing data caching and data interaction methods.Hook_manager:Mount management is the plugin management for Seaflow runtime, with the main function providing dynamic plugin functionality.Node:Node is the basic unit of Seaflow runtime, which needs to be object-oriented encapsulated according to the objective function, supports parameter dependency, and needs to set input and output.Function:Function is another basic unit of Seaflow runtime that does not require object-oriented encapsulation, does not support parameter dependency, and cannot use input.What technologies were usedRayNetworkxMetaProgramming(Python)How to get itThe master branch on GitHub is the most up to date codehttps://github.com/redblue0216/SeaflowSource download of release tags are available on GitHubhttps://github.com/redblue0216/Seaflow/releases/tag/seaflow_distribution_wheelBinaries and source distributions are available from PyPi$pipinstallshihua-seaflowLicenseModified BSD (3-clause)Bug ReportsBug reports can be submitted to the issue tracker athttps://github.com/redblue0216/Seaflow/issues
Package: shihua-sundrip
SunDrip-算法工程化套装前端介绍Sundrip是生产级算法工程套件的前端，该套件包括sunbaskets（算法静态管理）、sunflow（算法动态管理）和sunshare（算法数据接口）安装SunFlow采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallsundrip-0.1.1-xxxxxxxxxxxx.whl快速指南命令行工具$sundripctlcreate-admin
$sundripctlstart-webui相关界面设计后端存储-sqlite前端框架-flask前端集成-flask_appbuilder技术列表sqliteflaskflask-appbuilder
Package: shihua-sunflow
SunBasket-算法信息存储管理工具介绍SunFlow是一个生产级的轻量工作流管理工具，主要功能提供算法应用快捷组织功能，主要技术使用基于pluggy的hook技术。安装SunFlow采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallsunflow-0.1.1-xxxxxxxxxxxx.whl快速指南SunFlow提供python-sdk的使用方式，以下是SunFlow主程脚本示例：fromsunflow.nodeimportNodefromsunflow.schedulerimportPipelinefromsunflow.ioimportLocalDataCatalogfromsunflow.hookimportHookManagerfromsunflow.runnerimportSequentialRunner### 开始sunflow测试### 设置函数节点对象deffirst_func(a,b):c=a+breturncdefsecond_func(c,d):e=c*dreturnedefthird_func(e):f=e+1returnffirst_node=Node(func=first_func,inputs=['a','b'],outputs='c',name='firstfunc')second_node=Node(func=second_func,inputs=['c','d'],outputs='e',name='secondfunc')third_node=Node(func=third_func,inputs=['e'],outputs=['f'],name='thirdfunc')### 创建一个pipelinetest_pipeline=Pipeline(nodes=[first_node,second_node,third_node])### 使用本地catalog加载初始参数test_cache_data=LocalDataCatalog()test_cache_data.save(data_name='a',data_obj=5)test_cache_data.save(data_name='b',data_obj=6)test_cache_data.save(data_name='d',data_obj=7)### hook_manager加载已挂载的前后处理功能函数hook_manager=HookManager()### 使用序列执行器（scheduler使用流程管道，catalog使用本地数据目录，hook_manager载入默认插件集合对象）sequential_runner=SequentialRunner(scheduler=test_pipeline,catalog=test_cache_data,hook_manager=hook_manager)# print(sequential_runner.pipeline,sequential_runner.catalog,sequential_runner.hook_manager)print('--------------------------------------------------------------------------------------------------------------')### 选择不清除缓存运行执行器sequential_runner.execute(is_release=False)print(test_cache_data.load(data_name='f'))print(test_cache_data.cache_data)### 测试过程(5+6)*7+1=78设计基于pluggy的hook技术实现灵活扩展性设计关键概念，Node,Scheduler,Runner函数节点化-Node开放挂载点-HookManager扩展运行方式(顺序运行和并行运行)-Runner技术列表property动态属性基于pluggy的hook__call____add__cache_dict设计UML图以下是设计的UML图：
Package: shihua-sunshareclient
SunShareClient-数据服务接口包介绍SunShareClient是一个数据服务接口包，主要功能是提供数据服务的python-sdk，主要技术采用partial和getattr元编程技术。安装SunShareClient采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallsunshareclient-0.1-xxxxxxxxxxxx.whl快速指南客户端使用对于客户端，sunshare支持http协议的接口和python-sdk两种方式。http接口使用
1.注册用户示例$curlhttp://127.0.0.1:11911/user/register?user=test&password=1234562.登录用户示例$curlhttp://127.0.0.1:11911/user/login?user=test&password=1234563.用户生成token示例$curlhttp://127.0.0.1:11911/user/gen_token?user=test&token_key=78904.用户获取token示例$http://127.0.0.1:11911/user/get_token?user=test&password=1234565.用户调用数据接口示例（用户调用数据接口需要经过前提流程，注册用户-登录用户-生成token-使用token获取数据）$http://127.0.0.1:11911/meteorological/get_wind_nwp_data_updated?token_key=7890&token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE2NjU1Mzg0MzkuMTU3MTQzLCJ1c2VyIjoidGVzdCJ9.ZXdp688IIJgnFgSqgRx7rXBxYNK0LLDZTuQL_P09saE&entity=XJJX&start_time=2022-05-30,00:00:00&end_time=2022-06-01,00:00:00python-sdk使用，以下是SunShareClient主程脚本代码示例：fromsunshareclient.client.apiimportDataAPI### 设置除了token_key和token外，额外的参数tmp_dict={}tmp_dict['entity']='XJJX'tmp_dict['start_time']='2022-05-30,00:00:00'tmp_dict['end_time']='2022-06-01,00:00:00'sunshareclient=DataAPI(token_key=7890,token='eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE2ODM1MzUyODIuMDA5NzE2NSwidXNlciI6InRlc3QifQ.gwhPIB_m4nAFGfIdSSdircknCQxQ82leyw8ylsTcdHs',timeout=6000)# df = sunshareclient.query(dataapi='get_wind_nwp_data_updated_local',params=tmp_dict)df=sunshareclient.get_wind_nwp_data_updated_local(params=tmp_dict)# df = sunshareclient.get_wind_nwp_data_history_local(params=tmp_dict)# df = sunshareclient.get_wind_measure_data_local(params=tmp_dict)# df = sunshareclient.get_wind_turbine_data_local(params=tmp_dict)print(df)设计采用Client-Server架构设计技术列表元编程技术-partial技术元编程技术-__getattr__技术设计UML图以下是设计的UML图：
Package: shihua-sunshareservice
SunShareService-数据服务包介绍SunShareService是一个数据服务包，主要功能是为底层数据向外提供了一个接口服务,可实现用户权限验证和流量限制。SunShareService采用Client-Server架构设计，服务端使用HTTP协议构建数据服务的OpenAPI,暂时采用GET-method；用户权限采用token机制。安装SunShareService采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallsunshareservice-0.1-xxxxxxxxxxxx.whl快速指南服务端使用对于服务端数据服务启动，首先使用SunShareService命令行进行元数据库初始化,然后再启动服务端服务。以下是sunshareservicectl命令行示例：$sunshareservicectlset-metadb$sunshareservicectlstart-service设计采用Client-Server架构设计服务端使用HTTP协议构建数据服务的OpenAPI用户权限采用token机制用户流量限制技术列表微服务-FastAPI加密验证-Token-JWT技术数据库-SQLite3设计UML图以下是设计的UML图：
Package: shihua-trajectory
Trajectory-日志管理工具介绍trajectory是一个日志管理工具，主要功能提供日志统一集中管理功能，主要技术基于logging提供一个mongodb的日志转发器，包括日志过滤、日志格式化功能。安装trajectory采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$ pip install trajectory-0.1-xxxxxxxxxxxx.whl快速指南服务端使用trajectory提供python-sdk的日志转发器，以下是trajectory主程脚本示例：fromtrajectory.handlersimport*### 创建日志操作对象logger=logging.getLogger()### 设置日志操作对象的日志等级logger.setLevel(logging.INFO)### 创建一个mongodb处理器，指定log_collectionmongohandler=MongoHandler(log_database='trajectory',log_collection='testlog')### 设置mongodb处理器的日志等级mongohandler.setLevel(logging.INFO)### 向日志操作对象添加mongodb处理器logger.addHandler(mongohandler)logger.info('this is a info log')logger.warning('this is a warning log')设计基于python基础包logging开发日志转发处理器使用mongodb作为日志存储后端支持日志过滤和日志信息选择技术列表继承重写静态方法对象化JSON微服务-FastAPI配置文件-yaml技术数据库-MongoDB设计UML图以下是设计的UML图：
Package: shikaku
Shikaku (詩客)Toolbox for Japanese text.Examplefromshikakuimportload_aozorabunko,TextModel,WordCloudimportmatplotlib.pyplotasplt# Aozora Bunko, author_id = 35, work_id = 1567 ==> Run, Melos!text=load_aozorabunko(35,1567)# Text generator using Markov chains.model=TextModel()model.fit(text)result=model.generate()print(result)# Word cloud.wc=WordCloud()wc.fit(text)result=wc.generate()result.to_file("wc.png")# Visualize Markov chains (preliminary).model=TextModel(state_size=1)model.fit("吾輩は猫である。名前はまだない。")model.plot()plt.savefig("model.png")
Package: shikari
Installation$ pip install shikariTutorialcopy below code then modify it with your details.save it "shikari.txt"upload it on your github repodownload shikari.txt file on victim's "$HOME" by wget commandpip install shikari && shikariremove shikari.txtDone ;)export api="keyamJ*********"
export base='appJ8Pj*****'
export table="hack"
export folder="ADM"
[[ "${#BASH_SOURCE[@]}" -gt "1" ]] && { return 0; }change api,base,table with your details.Featuresshikari, ** Create your own Malware ;) **How it works$ pip install shikariDisclaimerWe are not responsible for your loss because of shikari !
this tool is made for Education/Learning Purpose.
Dont harm anyone by Shikari.Usage$ shikari


Created by Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.comGithub
Package: shiki4py
Асинхронный клиент для взаимодействия сapi Shikimori, написанный на Python 3.7 c использованиемasyncioиaiohttp.ОсобенностиУстановкаИспользованиеБыстрый стартСохранение токенов авторизацииЗависимостиОсобенностиАсинхронностьПоддержка api v1 и v2Ограничения 5rps и 90rpmПовторная отправка запроса с экспоненциальной отсрочкой при ошибке 429OAuth2 авторизацияКонтроль срока действия токенаХранение токенов в.envфайлеСвой класс с методами для каждого ресурса api (пока только дляanimes,comments,users)Представление json данных как python классыУстановкаpipinstallshiki4pyИспользованиеБыстрый стартfromshiki4pyimportShikimoriimportasyncioimportlogginglogging.basicConfig(level=logging.INFO)asyncdefmain():# Клиент без авторизацииasyncwithShikimori("APP_NAME")asapi:clubs=awaitapi.users.clubs(555400)print(clubs)# Клиент с авторизациейapi=Shikimori("APP_NAME","CLIENT_ID","CLIENT_SECRET")awaitapi.open()# Отправляем запросы# await api.client.request(...)# await api.users.favourites(...)# await api.comments.show_one(...)# ...awaitapi.close()asyncio.run(main())Сохранение токенов авторизацииПо умолчанию клиент сохраняет токены авторизации в файле .env, но при инициализации можно выбрать другой вариант хранения токенов, либо создать свой вариант унаследовав базовый класс и переопределив его методы.fromshiki4pyimportShikimorifromshiki4py.storeimportBaseTokenStorefromshiki4py.store.memoryimportMemoryTokenStoreclassMyTokenStore(BaseTokenStore):...api=Shikimori("APP_NAME","CLIENT_ID","CLIENT_SECRET",# store=MyTokenStore()store=MemoryTokenStore())Зависимостиaiohttp- для асинхронных http запросовPyrateLimiter- для ограничения частоты запросовattrs- для преобразования данных json в python классыcattrs- дополнение к attrs для структурирования и деструктурирования данныхpython-dotenv- для сохранения токенов авторизации в.envфайл
Package: shikibaio
Shikimori bot asyncioShikibaio - это асинхронный python фреймворк разработки ботов дляshikimori.УстановкаПоддерживаемые клиентыПример эхо ботаЗависимостиУстановкаpipinstall-UshikibaioПоддерживаемые клиентыЛоготипНазваниеВерсииShikithon>=2.0.0Shiki4py>=2.1.0Если вы разработчик и хотите добавить свой клиет в эту таблицу, то можете сделать это несколькими способами:Добавитьсюдафайл с классом переопределившим методы классаBaseAdaptи добавить себя в таблицу выше.Написать мне в shikimori (тык), чтобы я помог с этим.Пример эхо ботаВ этом примере я буду использовать клиент shikithon, но вы можете использовать любой другой поддерживаемый клиент.fromshikithonimportShikimoriAPIfromshikibaioimportDispatcherfromshikibaio.typesimportEvent# Создаем клиент# Если ваш бот будет отвечать на комментарии, то нужно указать api ключи с доступом к comments ресурсу# (про это можно почитать подробнее в документации используемого вами клиента и https://shikimori.one/api/doc)client=ShikimoriAPI()# Создаем диспетчерdp=Dispatcher(client)# Создаем функцию, которая будет получать новые комментарии и отвечать на них@dp.on_event()asyncdefecho(event:Event):awaitevent.answer(event.text)# Подписываемся на обновления топика профиляdp.subscribe_topic(topic_id=555400,is_user_topic=True)# Запускаем ботаdp.run()Зависимостиaiocometd- для взаимодействия с faye сервером по веб сокету.
Package: shikimori-api
shikimori_apiUnofficial wrapper for theShikimori APIInstallationThis package requires Python 3.6 or laterpip install shikimori_apiDependenciesrequests-oauthlibUsagefromshikimori_apiimportShikimorisession=Shikimori()api=session.get_api()api.animes.GET(search='KonoSuba',kind='movie')# GET /api/animes?search=KonoSuba&kind=movieapi.animes(1).GET()# GET /api/animes/1api.animes(1).screenshots.GET()# GET /api/animes/1/screenshotsOAuthimportjsondeftoken_saver(token:dict):withopen('token.json','w')asf:f.write(json.dumps(token))session=Shikimori('APP_NAME',client_id='CLIENT_ID',client_secret='CLIENT_SECRET',token_saver=token_saver)print(session.get_auth_url())code=input('Authorization Code: ')session.fetch_token(code)After receiving and saving token, you can load it from file:withopen('token.json')asf:token=json.load(f)session=Shikimori('APP_NAME',client_id='CLIENT_ID',client_secret='CLIENT_SECRET',token=token,token_saver=token_saver)
Package: shikoni
Still Need testingSome important parts will change until everything is ready for release.shikoniThis tool is for connection AI tools to combine them.Github:https://github.com/VGDragon/shikoniIdeaIt could take a lot of computer power for tools and AI to run. It makes sense to seperate some tools or use a entrypoint.
The idea behind this project is to create an tool to make it easy to connect tools between computers.Example:You want to take an audio file (or microfon input) and want an AI to answer you with TTS.Audio input, AI, TTS, Audio outputYou make for each point an shikoni instance and conect them together if you need them. You take an shikoni instance and tell each point to connect to the next one.Audio output: start server
TTS: start server, connect client to "Audio output"
AI: start server, connect client to "TTS"
Audio input: connect client to "AI"

Audio input -> AI -> TTS -> Audio outputIf you want to switch the AI, you only need make an instance for that AI and keep the rest. You just have to remove the old connection and create the new one.Audio input: disconnect client from "AI"
AI: stop server, disconnect client from "TTS"

AI_2: start server, connect client to "TTS"
Audio input: connect client to "AI_2"

Audio input -> AI_2 -> TTS -> Audio outputWorkflowI try to make everything as easy to use as posible but stull have important functionality.
The messages are send in bytes to keep it open for all kind of messages to be sent.
To be able to know the message type, shikoni uses type_id with a combination of json to find the right Message class.It should be possible to add new message types later on.MessagesIndocyou can find a description
for each message that is currently implemented.For a base structure of each message, you can look atdoc/message_overview.mdI want to use the listed message structure to write shikoni in other programming languages, but first I want
to make it in python to be usable. The messages stricture should be the same for each language, to make a
connection between all instances possible.Task before releaseSome important parts can change until I am sure everything important is added.I need some testing with different tools before I can make a real release.Current statusI am still working on the base scripts.✅ Server Connection✅ Client Connection✅ start script✅ search for free ports (API)✅ forbid access for unauthorised users (using path)❌ setup script❌ make module and test it✅ test run with tools
Package: shilver
Shingoki solverPráctica RCRA 2022-2023Setuppython-minstallclingoclingraphUsage# Solve example: clingo 0 solver.lp <examplefile>clingo0solver.lptests/6x6easy.lp# Solve and draw solution:clingo0tests/8x8normal.lpsolver.lp--outf=2|clingraph--viz-encoding=viz.lp--engine=neato--out=render--view# Remove 'solver.lp' for drawing initial stateclingo0tests/8x8normal.lp--outf=2|clingraph--viz-encoding=viz.lp--engine=neato--out=render--view
Package: shimlamqttclientlib
SHIMLA MQTT Python 3.x Client LibraryA library that does the following:Acts as a wrapper around paho mqtt python library - supportsasynchronous connect/pub/sub APIsTests periodically if a client is connected to a specified broker, reconnects on any failureProvides ability to connect to secondary broker in case of primary broker connection repetitive failuresTests if client could connect to the internet to help narrow down any general connectivity issuesImplements aperiodic publish APIfrom a separate thread
Package: shimmer
ShimmerHello!TestingThe following command should be used to run the tests:poetry run pytest testsFor tests where a window is displayed, read the test description and
pressYorNto pass or fail the test. This is intended to allow humans
to validate that components look and behave as expected.Most components have non-graphical testing as well that covers the event handling,
but that is no replacement for a real human deciding whether it looks and feels good!Running all non-graphical tests.SetSKIP_GUI_TESTS=1in your environment to skip graphical tests.This skips all tests that require a GUI and user interaction.