Saravanan666/pypi-org huggingface dataset:
Package: sahriswiki
Overviewsahriswiki is alightweightWiki Enginebuilt using thecircuitsand circuits.web framework framework, is modular, plugable
and themeable.sahriswiki aims to implement a core set of features that are a mixture
of “best of breed” Wiki, Blog, and CMS features.Website:http://sahriswiki.org/Documentation:http://sahriswiki.org/Docs/Project website:https://bitbucket.org/prologic/sahriswiki/PyPI page:http://pypi.python.org/pypi/sahriswikiFeaturessmall and lightweightmercurial storage engineplugin architectureRequirementssahriswiki depends on the following software:circuitsMercurialSQLAlchemycreoleparsergenshifeedformatterInstallationThe simplest and recommended way to install sahriswiki is with pip.
You may install the latest stable release from PyPI with pip:> pip install sahriswikiIf you do not have pip, you may use easy_install:> easy_install sahriswikiAlternatively, you may download the source package from thesahriswiki Page on PyPior thesahriswiki Downloads pageon thesahriswiki Website; extract it and install using:> python setup.py installLicensesahriswiki is licensed under theMIT License.FeedbackI welcome any questions or feedback about bugs and suggestions on how to
improve sahriswiki. Let me know what you think about sahriswiki.
I am on twitter@therealprologic.Do you have suggestions for improvement? Then pleaseCreate an Issuewith details of what you would like to see. I’ll take a look at it and
work with you to either incorporate the idea or find a better solution.Release NotesBug FixesFixed creation and population of database tables upon startup.
Package: sai-airflow-plugins
sai-airflow-pluginsis a Python package that contains various operators, hooks and utilities for Apache Airflow.Full documentation is available athttps://slimmer-ai.github.io/sai-airflow-plugins/.FeaturesHook, operator and sensor for executing an SSH command using theFabriclibrary,
with support for adding output respondersOperator for sending messages to anincoming Mattermost webhookConditional operators and sensors that are skipped when a Python callable evaluates toFalseInstallationThrough pip (PyPI):pipinstallsai-airflow-pluginsFrom source:pythonsetup.pyinstallDocumentationIf you want to build the documentation, please install the documentation dependencies by executing:pipinstallsai-airflow-plugins[docs]orpipinstall.[docs]Documentation can be built by executing:pythonsetup.pybuild_docsDocumentation can also be build from thedocsfolder directly. In that casesai-airflow-pluginsshould be
installed and available in your current working environment. Execute:makehtmlin thedocsfolder.
Package: saiblo-dev-tools
游戏开发人员脚本工具配置安装本工具：pip3installsaiblo-dev-tools使用前请先执行：saiblo-dev-toolsinit脚本会在 ~/ 下创建 .saiblo-dev-tools 目录，里面将会存放所有配置文件。初始化后，请编辑 ~/.saiblo-dev-tools/config.json，按照模板填写配置文件。选项脚本包含了若干子命令，在每个子命令下有对应的帮助信息：saiblo-dev-tools<subcommand>--helpinitsaiblo-dev-toolsinit会将 ~/.saiblo-dev-tools/config.json 和 database.json 复原为模板文件。aiAI 管理脚本。AI 以<tag>: <token>的形式存储在 ~/.saiblo-dev-tools/database.json 的ai字段下。<tag>为人类友好的名称，<token>用于在 Saiblo 数据库中唯一确定某个 AI."human"保留给人类玩家，请勿使用。-d,--delete<tag>删除名为<tag>的 AI，并将其 token 打印出来，防止误操作。-i,--insert<tag><token>添加 AI，若<tag>已存在，则会覆盖，但会将原有 AI 的 token 打印出来。脚本不会对 token 的合法性进行检验。-l,--list显示数据库中所有 AI.-r,--rename<old_tag><new_tag>将名为<old_tag>的 AI 更名为<new_tag>. 若<new_tag>已存在，则会覆盖，但会将原有 AI 的 token 打印出来。-u,--upload<config_path>上传 AI 至 Saiblo，并将 token 添加进本地数据库。<config_path>为配置文件的路径，格式如下：{
    "game": number,  // <game_id>
    "language": string,  // {c, cmake, cpp, make, python, python_zip}
    "path": string,  // AI 代码的路径，需要打包成单文件
    "tag": string,  // <tag>
}game游戏管理脚本。-t,--test<config_path><result_dir>在 Saiblo 上发起一次评测。<result_dir>为存放评测信息的目录，将包含配置信息、评测结果、回放文件。<config_path>是配置文件的路径，格式如下：{
    "ai": [
        string,  // <tag>
        ...
    ],
    "config": any,  // 自定义配置信息，若无，可以填空字符串
    "game": number,  // <game_id>
}saiblo-dev-tools 现已支持启动人机、人人对战，你只需要将上述配置文件中原本填写 ai tag 的位置改为填写"human"，即可在相应位置添加人类玩家。该脚本随后会输出人类玩家的 token.-u,--upload<config_path>上传游戏逻辑至 Saiblo.<config_path>为配置文件的路径，格式如下：{
    "id": number,  // <game_id>
    "path": string,  // 游戏逻辑的路径，需要打包成单文件
}注意：目前 Saiblo 对游戏逻辑代码的管理与 AI 不同，因此该命令会覆盖 Saiblo 上先前的游戏逻辑，请谨慎使用。loginsaiblo-dev-toolslogin<username>登录 Saiblo 开发账号。脚本将会在 ~/.saiblo-dev-tools/cookie 中保存登录凭据，请妥善保管。logoutsaiblo-dev-toolslogout删除 ~/.saiblo-dev-tools/cookie，下次使用与 Saiblo 交互的命令将需要登录。
Package: saiblo-local-judger
Saiblo Local Judger (Exp)实验版的Saiblo本地调试用评测机。OverviewCore核心：使用TCP Socket提供本地AI连接与游戏运行支持，和Saiblo上的Judger保证行为相同。同时提供了CLI接口。GUI：提供选手易于使用的图形界面AI Adapter：为未支持的AI提供原始协议和TCP协议的转发，但是仍然建议新的游戏的ADK直接提供相关支持核心运行流程读取提供的游戏配置，其中可能包含游戏人数游戏逻辑路径游戏配置通讯协议版本在随机端口启动本地TCP Socket服务器，并等待AI连接。全部AI均已成功连接后，启动游戏逻辑按照Saiblo 文档运行游戏流程
Package: saibr
SAIBR (python implementation)Python implementation of SAIBR: a simple, platform independent protocol for spectral autofluorescence correctionPaperFIJI pluginTutorial notebookAs a first step, I would recommend checking out thetutorial notebook. This can be run in the cloud using Binder (please note that it may take several minutes to open the notebook):To run locally, download the code and install the relevant requirements (requirements.txt) in a virtual environment.Install instructionsTo explore further and incorporate into your own analysis pipelines, you can install the package from PyPI using pip:pip install saibrIf you want to make changes to the code you can download/clone this folder, navigate to it, and run:pip install -e .[dev]CitationIf you use this method as part of a publication, please cite the following reference:Nelio T. L. Rodrigues, Tom Bland, Joana Borrego-Pinto, KangBo Ng, Nisha Hirani, Ying Gu, Sherman Foo, Nathan W. Goehring; SAIBR: a simple, platform-independent method for spectral autofluorescence correction. Development 15 July 2022; 149 (14): dev200545. doi:https://doi.org/10.1242/dev.200545LicenseThis work is licensed under aCreative Commons Attribution 4.0 International License.
Package: saic-ismart-client
SAIC Python client libraryAboutA Python package for interacting with the MG iSMART API.MG iSMART is the connectivity system in your MG car (MG5 MG4, ZS...).The implementation is based on the findings from theSAIC-iSmart-API Documentationproject.Supported functionalityloginset alarm switchesget message listget vehicle statusget charging statuslock vehicleunlock vehiclestart rear window heatingstop rear window heatingPrerequisitesYou have an iSMART account (can be created in the iSMART app)
Package: saic-ismart-client-ng
SAIC Python client libraryAboutA Python package for interacting with the MG iSMART API.MG iSMART is the connectivity system in your MG car (MG5 MG4, ZS...).Supported functionalityloginset alarm switchesget message listget vehicle statusget charging statuslock vehicleunlock vehiclestart rear window heatingstop rear window heatingPrerequisitesYou have an iSMART account (can be created in the iSMART app)
Package: saih-hidrosur
python-saih-hidrosurWhat is this lib ?Python library aroundS.A.I.H HidrosurdataHow to install the lib ?You can get it frompypilike so:pip3 install saih-hidrosurHow to use the lib>>>importdatetime>>>frompprintimportpprintaspprint>>>fromsaihhidrosurimportget_stations,Station>>>pprint(get_stations()){1:'SIERRA MIJAS  (MA)',2:'SIERRA DE LUNA (CA)',3:'EMBALSE DE CHARCO REDONDO (CA)',4:'DEPÓSITO REGULADOR CHARCO RDO (CA)',5:'TORRE TOMA DE CHARCO REDONDO (CA)',6:'LOS REALES (MA)',7:'DEPÓSITO DI-1 (CA)',8:'EMBALSE DE GUADARRANQUE (CA)',...}>>>murtas=Station(59)>>>murtas<MURTAS(GR)withsensors['TEMPERATURA EXTERIOR','NIVOMETRO','PLUVIOMETRO']>>>>murtas.sensors{'NIVOMETRO':'059N02','PLUVIOMETRO':'059P01','TEMPERATURA EXTERIOR':'059M02'}>>>date_from=datetime.datetime(2022,1,1)>>>date_to=datetime.datetime(2022,1,2)>>>murtas.sensor_values('TEMPERATURA EXTERIOR',date_from,date_to){datetime.datetime(2022,10,1,0,0):15.2,datetime.datetime(2022,10,1,1,0):14.7,…datetime.datetime(2022,10,1,23,0):17.0,datetime.datetime(2022,10,2,0,0):16.6}
Package: saiive.chain
BigchainDB allows developers and enterprises to deploy blockchain proof-of-concepts, platforms and applications with a blockchain database. BigchainDB supports a wide range of industries and use cases from identity and intellectual property to supply chains, energy, IoT and financial ecosystems. With high throughput, low latency, powerful query functionality, decentralized control, immutable data storage and built-in asset support, BigchainDB is like a database with blockchain characteristics.
Package: sail
SAILThe library is for experimenting with streaming processing engines (SPEs) and incremental machine learning (IML) models. The main features of Sail are:Common interface for all incremental models available in libraries like Scikit-Learn, Pytorch, Keras and River.Distributed computing for model selection, ensembling etc.Hyperparameter optimization for incremental models.Interface and pipelines that implement incremental models for both offline and online learning.A robust framework to construct an end-to- end AutoML pipeline with pipeline strategies that enables data ingestion, feature engineering, model selection, incremental training, and monitoring for data drifts on a streaming data.DocumentationSee theSAIL Wikifor full documentation, installation guide, operational details and other information.ArchitectureSAIL PipelineSAIL Model FrameworkDifference with River and other existing incremental machine learning libraries.Sail leverages the existing machine learning libraries like River, sklearn etc and creates a common set of APIs to run these models in the backend. In particular, while River provides minimal utilities for deep learning models, it does not focus on deep learning models developed through Pytorch and Keras. In addition, models in Sail are parallelized using Ray. The parallelization results in three major advatages that are particularly important for incremental models with high volume and high velocity data:Faster computational times for ensemble models.Faster computational times for ensemble of forecasts.Creates a clean interface for developing AutoML algorithms for incremental models.Spark vs Ray for incremental models.Sail could have been parallelized using Spark as well. However, to keep the streaming processing engines and machine learning tasks independent, Ray was preferred as the data can then be handled using Pandas, Numpy etc efficiently. This flexibility further allows using other SPEs like Flink or Storm without updating the parallelization framework for IML models.🛠 InstallationSail is intended to work withPython 3.8 and above. You can install the latest version from GitHub as so:gitclonehttps://github.com/IBM/sail.gitcdsail
pipinstall-e".[OPTION]"SupportedOPTIONinclude:tensorflowtensorflow_arm64pytorchriverraydevtestsexamples : to run notebooks and examplesall : all of the aboveall_arm64 : Apple ARM64 version all of the above✍️ Examples and NotebooksExamples and notebooks are located in theexamplesandnotebookrespectively. Please run the below command to install the necessary packages to run examples.pipinstall-e".[examples]"RecognitionSAIL has been identified as innovations with market potential that can contribute to UN Sustainable Development Goals by the European Commission Innovation Radar. More detailshere.AcknowledgmentThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957345 for MORE project.
Package: sailboat-playground
Sailboat PlaygroundLanguage:EN(incomplete) /PT-BR(completo)A very simple framework for developing autonomous sailing algorithms and testing them with 2D simulations/visualizations.Getting startedThere are two ways to install this on your machine:Option #1 - From GitHub repository (recommended)Using this method, you'll be able to execute the examples as they are, with no further changes required.Clone this repositoryhttps://github.com/gabriel-milan/sailboat-playgroundcdinto the cloned repository and install itpython3 -m pip install .And it's done! If you want to run theupwindexample, dopython3 examples/upwind/sailing_upwind.pyOption #2 - From PyPIThis package is also available on PyPI, but you'll need to create your own environment and boat configuration files before you use it.Install from PyPI:python3 -m pip install sailboat_playgroundBasic usageThis framework is split in two main modules:engineandvisualization.Theenginemodule handles the simulation and generates files with simulation data for later debugging and visualization. The main class of the engine is theManagerclass. There, you need to provide both boat and environment configuration filesWork in progress... (will write this one in PT-BR first)
Package: saildb
SailDBThe succesor to ReflectDB. Sail (Space and Integer Interaction Language) is a custom made language than uses seperate spaces as a type of directory system. The code to SailDB is fully open-source and light weight.PerformanceAll tests were run on a 2017 MacBook Pro with 256gb of Flash StorageDatabaseBenchmark ScoreSAIL (Python)250000/sReflect9975/sJSON400000/sJSON Indented (2)142857/sNOTE: JSON is a builtin library that probably is running C instead of just Python
Package: sailenv
SAILenv Client - SAILab Virtual EnvironmentSAILenv is a Virtual Environment powered by Unity3D, developed by SAILab in Siena. This repository contains a Python client for SAILenv.Have a look at the project page -SAILenvfor more details.Technical report here-  accepted at ICPR2020.Agent APISAILenv is accessible through an instance of the Agent class, which acts as the API between Python scripts and the environment. The API is designed to be as easy as possible to integrate with most known computer vision frameworks.Through the API, a Python script can access many views of the environment with configurable resolution scaling (aspect ratio is fixed, at the moment):Realistic rendering of the environmentPixel-wise segmentation at category levelPixel-wise segmentation at instance levelPixel-wise depthPixel-wise optical flowA script can also:Obtain the list of object categories available in the sceneObtain the list of available scenesTransfer the Agent in one of the available scenesManually set the Agent position and orientationEnable automatic movement through a predetermined track inside the sceneOptical FlowThe optical flow is obtained directly from the Unity Physics Engine, meaning that it is both highly accurate while still achieving greats speed.Agent and Environment communicationThe Agent and the Environment communicate through low-level sockets, allowing highly performant exchange of information.Cite@article{meloni2020sailenv,
  title={SAILenv: Learning in Virtual Visual Environments Made Simple},
  author={Meloni, Enrico and Pasqualini, Luca and Tiezzi, Matteo and Gori, Marco and Melacci, Stefano},
  journal={arXiv preprint arXiv:2007.08224},
  year={2020}
}Download
Package: sailfish
sailfishSailfish provides data engineering and management utilities to apply common optimizations to Pandas Dataframes.Sailfish primarily works by identifying the optimal data type for each column. For example, for integer-only columns,
it may be possible to have a datatype of int32 versus int64 which can save roughly have the memory. On big datasets, these
types of optmizations can provide tremendous savings.This project replaces thepd-helper projectas ofpd-helper 1.0.1; sailfish is basically pd-helper 2.0.0.Installpipinstallsailfish
Package: sail-on-client
Sail-On Client and ProtocolsClient and Protocols for DARPA sail-onProtocols present in the repositoryONDCONDDARequirementsPython 3.7Poetry >= 1.1.0InstallationInstall Poetry following the instructions available in theinstallation pageClone the repositories associated with different components in a working directorygit clone https://github.com/darpa-sail-on/sail-on-client.gitThis would create sail-on-client directories in your working directoryInstall the different components in a virtual environmentcd sail-on-client
  poetry install
  poetry shellRunning Client and Server with Different AlgorithmsNote: If you are using the server setup by PAR, update theurltohttp://3.32.8.161:5000in sail_on_client/protocol/configuration.json and skip step 1 of running the server.Instructions for running M6 AlgorithmsInstructions for running M12 AlgorithmsInstructions for running M18 AlgorithmsPublishing on PYPIsail-on-client uses github actions to publish packages on pypi. The action is triggered when a semver tag is pushed to the repository.We support the following version format .. and ..-alpha. for tags.
To publish a package on pypi, the tag must match with the version maintained in pyproject.toml.
This is implemented as a mandatory check in the workflow. Poetry provides support for both querying and bumping version via cli.
Please refer toversionfor more details.Thus to publish sail-on-client on pypi use the following commandsBump the version in pyproject.toml usingpoetry version <version_rule>.Use poetry version --short to determine the version that would be used in the tag.Generate and push the tag usinggit tag <package-version>
  git push origin --tagsAcknowledgement of Support and DisclaimerThis material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0055. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA.
Package: sailplay
Python client for API sailplay.ruDocs are available at https://sailplay.readthedocs.org/. Pull requests
with documentation enhancements and/or fixes are awesome and most welcome.ContentsRequirementsInstallationUsageInitialize API clientAdditional paramsGet API tokenWorking with apiContext managerRaw api requestBug trackerContributingContributorsLicenseRequirementspython >= 2.6Installationsailplaycould be installed using pip:pip install sailplayUsageInitialize API clientYou should havepin,store_department_idandstore_department_keyfrom
the service.from sailplay import SailPlayClient

client = SailPlayClient(pin, store_department_id, store_department_key)Additional paramsclient = SailPlayClient(
    pin, store_department_id, store_department_key,
    token="token-here", # Set token manually (default "")
    silence=True,       # Dont fail on API errors (default False)
    loglevel="debug",   # Set log level (default INFO)
)Get API tokenNoteNot required. Client will get token automatically on API requests.client.login()
print client.tokenWorking with apiSailplay have nice and easy syntax. Just have a look:# Get events list  http://sailplay.ru/api/v2/events/list/
client.api.events.list()

# Create a new user http://sailplay.ru/api/v2/users/add/?...
client.api.users.add(user_phone='...', first_name='...', last_name='...')

# Get info about user http://sailplay.ru/api/v2/users/info/?...
client.api.users.info(user_phone='...')

# Create purchase http://sailplay.ru/api/v2/purchases/new/?...
client.api.purchases.new(**params)

# You also could use getitem syntax for params
client.api.users['custom-vars'](**params)
# Same as
client.api['users']['custom-vars'](**params)

# And etc. I hope you make decision how the client works :)For now client chooses API version automaticaly.Context managerYou could redefine the client settings in context:with client.ctx(silence=True):
    # Errors will not be raised here
    client.api.users.add(user_phone='...', first_name='...', last_name='...')Raw api requestYou could make raw request to sailplay API:client.request(method='GET', url='/users/info', data={...})Have a nice codding!Bug trackerIf you have any suggestions, bug reports or
annoyances please report them to the issue tracker
athttps://github.com/klen/sailplay/issuesContributingDevelopment of starter happens at github:https://github.com/klen/sailplayContributorsklen(Kirill Klenov)LicenseLicensed under aBSD license.
Package: sails
Spectral Analysis In Linear SystemsThis repository contains the Spectral Analysis In Linear Systems tools
for python (sails)InstallationIf you just want to install sails to use it, the easiest way is to get it from pip
using`pip install sails`.If you are reading this file, you probably have a source distribution of sails and
may be interested in working on the software.  In that case, as there is no
compiled code, you can simply set your`PYTHONPATH`to include this directory.
Alternatively, you can use`python3 setup.py build`or run`make`or`make doc`to install the software into the normal`build/`directory
and/or build the documentation using sphinx.DocumentationDocumentation for the current release can be found athttps://sails.readthedocs.orgDependenciesSee the requirements.txt file.Some of these aren’t strict dependencies, but are instead what we develop
against (i.e. we don’t guarantee not to use features which only exist from that
release onwards).RepositoryThe main git repository can be found athttps://gitlab.com/sails-dev/analysis/sailsContributingIf you wish to raise issues on, or contribute patches to SAILS, you will need
an account on gitlab.com.  If you wish to be able to create MRs please tag
@ajquinn or @mhy in the relevant issue and we will give you permission to do
so.  Alternatively, please feel free to email one of us.AuthorsAndrew Quinn <andrew.quinn@psych.ox.ac.uk>
Mark Hymers <mark.hymers@ynic.york.ac.uk>LicenseThis project is currently licensed under the GNU General Public Licence 2.0 or
higher.  For the avoidance of doubt, the authors intend that if the code is
imported into a project which is not licensed under a GPL 2.0 compatible
license, an alternative license arranged must be acquired.  For alternative
license arrangements such as use in commercial products for which you do not
which to release the source code under a GPL compatible license, please contact
the authors.  The authors are also available for consultancy regarding this
toolbox and associated methods.
Package: sailsim
sailsim- A sail boat simulatorThis sailboat simulation program is meant to test sailing algorithms. Provide the simulation a sailboat, some wind and a sailing algorithm and the boat will sail in this virtual world.This project is developed by Tillman Keller & Michael Behrens.InstallationThis package can be downloaded fromPyPiusingpipby using the following command:pip install sailsimTo install the package for development download the repository fromGitHub. Navigate to the folder and add the-eoption to the pip install command:pip install -e .UsageIntests/basictest.pyis an example use of the module. For now the GUI is not reachable that easily, but updates will come!DocumentationThe class diagram can be fond in thedocsfolder. The folder contains a class diagram that displays the structure of the project. Additionally, a sequence diagram explains how the simulation of in step is working.ContributingWe are happy for any support from other people. Especially if you are into QT GUI development or design we truly need your help!