Saravanan666/pypi-org huggingface dataset:
Package: relaystory
relay-story 多人接力故事指由读者/作者参与后续剧情撰写与推进。采用多分支方式以满足不同读者/作者对于不同情节的偏好。背景本项目由BobAnkh发起，旨在搭建多分支接力小说的框架。从任何一个已有节点，所有读者/作者都可以在新文件中以符合要求的方式续写你所想的剧情使用说明目前支持生成为markdown格式的结果和纯html格式的结果，可以搭配自己的css或其他模板使用，后续计划支持自定义css和模板安装说明使用pip install relaystory进行安装，使用方式如下：usage: relaystory [-h] [-i INPUT] [-o OUTPUT] [-f FORMAT [FORMAT ...]]optional arguments:-h, --help            show this help message and exit-i INPUT, --input INPUTdirectory contains the src markdown files. Default to story.-o OUTPUT, --output OUTPUTdirectory to output all the generated stories. Default to output.-f FORMAT [FORMAT ...], --format FORMAT [FORMAT ...]select the output format. Options: all, markdown, html. Default to markdown.例如运行relaystory -i story -o output -f markdown命令，可将story文件夹下的按一定格式组织的文件组织成故事，并以markdown的格式输出在文件夹output/markdown下。具体可参见本仓库下story和output文件夹中的示例，也欢迎为此示例贡献。文件规范所有文件将全部存放于同一个文件夹中，并按照一下规范进行组织。文件命名对于每一段故事，采用<级别>.<序号>的命名方式，其后无需跟随任何内容。<级别>指的是故事的层级，例如初始节点即为级别1，接着初始节点故事所写的故事即为级别2，以此类推。<序号>仅作为同一级别中区分各文件用。如第二级的故事(指其上游已有两次情节)，命名形如2.1。文件内容故事采用markdown进行撰写，需在文中任意一些行以下述注释形式指出以下元数据:上游编号(即被续接的故事的文件名):<!--upstream: x.y-->. 如<!--upstream: 2.1-->作者:<!--author: YourName-->. 如<!--author: BobAnkh-->书名(最终合并完成的书籍以最后一个节点的书名为准):<!--book name: YourBookName-->. 如<!--book name: DarkSouls-->章节名(会覆盖掉内容中首个一级标题的内容, 如'# title', 若无则添加为一级标题. 即章节标题以此为准, 建议内容中一级标题与此相同):<!--chapter name: YourChapterName-->.<!--chapter name: ch01-->维护者@BobAnkh如何贡献我们非常欢迎任何人为本项目贡献自己的力量，为这个仓库添加新的内容，只要它对于这个仓库是具有意义的，并且是符合规范的。欢迎随时提出issue或者提交pull request，但是它们需要按照各自的template进行填写。可以优先考虑具有help wanted标签的issue。同时请注意，参与本项目需要遵守Code of Conduct。对于本仓库的示例故事可以按照使用说明任意进行贡献。使用许可Apache-2.0© BobAnkh
Package: relay-tunnel
RelayTunnel用于建立中继隧道的TurboTunnel插件。功能特性WebSocket Relay TunnelHTTP(S) Relay TunnelIRC Relay Tunnel（开发中）使用方法安装方法$pip3installrelay-tunnelWebSocket Relay Tunnel中继服务端$turbo-tunnel-lws+relay://0.0.0.0:8080/relay/-prelay_tunnel也可以使用Docker方式来运行：$sudodockerbuild-trelay-server-fdocker/Dockerfile.
$sudodockerrun-it-p8080:80relay-server中继节点$relay-tunnel-s"ws://10.0.0.1:8080/relay/?client_id=${node_id}"其中${node_id}是中继节点的ID，可以为任意字符串，但必须保持唯一。客户端$turbo-tunnel-ltcp://127.0.0.1:7777-t"ws+replay://10.0.0.1:8080/relay/?client_id=${node_id}&target_id=${target_id}"-ttcp://private.com:80这条命令表示将中继节点所在网络中的private.com:80服务映射到本地的127.0.0.1:7777地址。
Package: relazioni
RelazioniRelazioni is a lightweight package for strength of the relationship between variables analysis.Documentation:https://chicodelarosa.com/relazioniSource code:https://github.com/chicodelarosa/relazioniBug reports:https://github.com/chicodelarosa/relazioni/issuesIt provides easy to use functions for measuring the relationship between variables of the following natures:Two ContinuousA variable that can reasonably take on any value within a range. Examples of continuous variables include height, weight, exam scores, income, salary, etc.Two CategoricalA variable that is a category without a natural order. Examples of categorical variables are eye color, city of residence, type of dog, etc.At least One OrdinalA variable with categories that have an inherent order. For instance, education level (GDE/Bachelors/Masters/PhD), income level (if grouped into high/medium/low) etc.One Binary and One ContinuousA variable that is a category with only two possible values. Examples of binary variables include gender (male/female) or any True/False or Yes/No variable.Relazioni currently supports 8 different association functions for investigating the relationship between variables in the following cases:Two Continuous and CovariatesPartial Correlation (R)Two Continuous and No CovariatesPearson CorrelationTwo Categorical and Two Values per VariablePhi CoefficientTwo Categorical and More than Two Values per VariableCramer’s VTheil's UAt Least One OrdinalKendall’s TauSpearman’s RhoOne Continuous and One BinaryPoint-biserial CorrelationRequirementsscipy
numpy
pandas
scikit-learnInstallationInstalling via pypipip install relazioniInstalling via pippip install .Installing via setup.pypython setup.py installInstalling via Gitpython -m pip install git+https://github.com/chicodelarosa/relazioni.gitExampleimport numpy as np
from relazioni import associations

v1, v2 = np.array([1, 1, 2]), np.array([1, 1, 2])

matth_corr = associations.matthews_corr(v1, v2)
print(matth_corr) # 1.0

v1, v2 = np.array([1, 1, 2]), np.array([2, 1, 2])

matth_corr = associations.matthews_corr(v1, v2)
print(matth_corr) # 0.5Call for ContributionsThe relazioni package welcomes your expertise and enthusiasm!
Package: relbench
Website|Vision Paper|Mailing ListOverviewRelational Deep Learning is a new approach for end-to-end representation learning on data spread across multiple tables, such as in arelational database(see ourvision paper). RelBench is the accompanying benchmark which seeks to facilitate efficient, robust and reproducible research in this direction. It comprises of a collection of realistic, large-scale, and diverse datasets structured as relational tables, along with machine learning tasks defined on them. It provides full support for data downloading, task specification and standardized evaluation in an ML-framework-agnostic manner. Additionally, there is seamless integration withPyTorch Geometricto load the data as a graph and train GNN models, and withPyTorch Frameto encode the various types of table columns. Finally, there is a leaderboard for tracking progress.RelBench is in its beta release stage, and we are planning to increase datasets and benchmarking in the near future. Datasets in the current version are subject to change.InstallationYou can install RelBench usingpip:pip install relbenchPackage UsageHere we describe key functions of RelBench. RelBench provides a collection of APIs for easy access to machine-learning-ready relational databases.To see all available datasets:fromrelbench.datasetimportdataset_namesprint(dataset_names)For a concrete example, to obtain therel-stackexrelational database, do:fromrelbench.datasetsimportget_datasetdataset=get_dataset(name="rel-stackex")To see the tasks available for this dataset:print(dataset.task_names)Next, to retrieve therel-stackex-votespredictive task, which is to predict the upvotes of a post it will receive in the next 2 years, simply do:task=dataset.get_task("rel-stackex-votes")task.train_table,task.val_table,task.test_table# training/validation/testing tablesThe training/validation/testing tables are automatically generated using pre-defined standardized temporal split. You can then build your favorite relational deep learning model on top of it. After training and validation, you can make prediction from your model ontask.test_table. Suppose your predictiontest_predis an array following the order oftask.test_table, you can call the following to retrieve the unified evaluation metrics:task.evaluate(test_pred)Additionally, you can evaluate validation (or training) predictions as such:task.evaluate(val_pred,task.val_table)DemosList of working demos:NameDescriptionrel-stackexexploringrel-stackexdataset and tasksrel-amazonexploringrel-amazondataset and tasksCite RelBenchIf you use RelBench in your work, please cite our paper:@article{relbench,
  title={Relational Deep Learning: Graph Representation Learning on Relational Tables},
  author={Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec},
  year={2023}
}
Package: rele
Relé makes integration with Google PubSub straightforward and easy.Motivation and FeaturesThePublish-Subscribe patternand specifically the Google CloudPub/Sub libraryare very powerful tools but you can easily cut your fingers on it. Relé makes integration
seamless by providing Publisher, Subscriber and Worker classes with the following features:Powerful Publishing APIHighly Scalable WorkerIntuitive Subscription ManagementEasily Extensible MiddlewareReady to go Django/Flask integrationCLIAnd much more!What it looks like# Publish to the topicimportrelerele.publish(topic='photo-uploaded',data={'customer_id':123})# Subscribe to the Pub/Sub topicfromreleimportsub@sub(topic='photo-uploaded')defphoto_uploaded(data,**kwargs):print(f"Customer{data['customer_id']}has uploaded an image")What's in the name"Relé" is Spanish forrelay, a technology thathas played a key rolein
history in the evolution of communication and electrical technology, including the telegraph,
telephone, electricity transmission, and transistors.InstallRelé supports Python 3.6+ and installing viapippip install releor with Django integrationpip install rele[django]or with Flask integrationpip install rele[flask]QuickstartPlease see our documentation to get started.You can also read more about ithereRunning TestsDoes the code actually work?make test
Package: relearn
ReLearnIntroducing a dynamic package designed specifically for the development and implementation of reinforcement learning (RL) projects. This toolkit includes essential classes for modeling Markov Decision Processes (MDPs), agents, and environments, laying the foundation for RL systems. With our package, users have the flexibility to create custom environments, allowing for the exploration and testing of various RL algorithms. Whether you're a researcher, educator, or developer, this package provides the necessary tools to implement reinforcement learning applications.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.InstallingPython: This project requires a Python version >=3.9 and <3.13. You can download it frompython.org.This project can be installed using pip directly from PyPI or by cloning the repository from GitHub. Follow the instructions below based on your preferred method.Installing from PyPIFirst, consider creating a virtual environment:python-mvenvvenvsourcevenv/bin/activate# On Unix/macOS.\venv\Scripts\activate# On WindowsTo install the package from PyPI, run the following command in your terminal. This is the simplest way to install the latest stable version of the project:pipinstallrelearnMake sure you have pip installed and updated to the latest version to avoid any issues.Installing from GitHubIf you prefer to install the latest development version or want to contribute to the project, you can clone the repository from GitHub and install it manually:Clone the repository:gitclonehttps://github.com/umilISLab/relearn.gitNavigate to the project directory:cdrelearnConsider creating a virtual environment:python-mvenvvenvsourcevenv/bin/activate# On Unix/macOS.\venv\Scripts\activate# On WindowsInstall the project and its dependencies using the preferred method:Dependency Management with Poetry: This project usesPoetryfor dependency management and package handling. Ensure you have Poetry installed on your system. For installation instructions, visit theofficial Poetry documentation.To check if you have Poetry installed, run the following command in your terminal:poetry--versionIf Poetry is installed, you should see the version number in the output. If not, please follow the installation guide provided in the link above.
-Installing Dependencies: With Poetry installed, you can install project dependencies by running:shell poetry installIf the project uses arequirements.txt:pipinstall-rrequirements.txtVerify the InstallationAfter installation, you can verify that the project is installed correctly by running:python-c"import relearn; print(relearn.__version__)"Usage ExampleYou can find aRecycling Robotexamplehere.Built Withpoetry- Dependency ManagementVersioningWe useSemVerfor versioning.AuthorsElisabetta Rocchetti-Initial work-ISLabLicenseThis project is licensed under the GNUv3 - see theLICENSE.mdfile for details
Package: release-auditor
release_auditorA tool for checking if GitHub release assets were modified after publication.For more information,please read our blog post here.RequirementsPython 3 is required and you can find all required modules in therequirements.txtfile.
Only tested on Python 3.7 but should work on other 3.x releases.InstallationYou can install this via PIP as follows:pip install release_auditor
release_auditor --versionTo download and run manually, do the following:git clone https://github.com/nightwatchcybersecurity/release_auditor.git
cd release_auditor
pip install -r requirements.txt
python -m release_auditor.cliHow to useThis utility is intended to check if a GitHub release was modified afer publication. This utility
will not check source code archives included with a release because they are immutable. It does
two checks on release assets:Whether the asset was created/modified by someone else other than the release author.Whether the asset was created/modified after initial publication.By default, the 5 most recent releases are checked and the time interval checked is 24 hours. You can
override both via the "--max" and "--hours" options.A non-zero error code will be returned after execution if any issues are found.Additional optionsBy default, this utility accesses GitHub anonymously, which can result in API rate limiting. Consider
running this less often or pass in a GitHub username/password/access token via the "--login_or_token"
and "--password" parameters.You can use this with self-hosted GitHub instances by passing the instance URL via the "--base-url"
parameter. However, this has not been tested.The "--verbose" option shows additional information during checking.Example useRun as following:release_auditor github nightwatchcybersecurity/truegazeThe following results will be returned:Retrieving repository and release information
Checking the first 5 releases

Checking release: Version 0.1.7 released

Checking release: Version 0.1.6 released

Checking release: Version 0.1.5 released

Checking release: Version 0.1.4 released

Checking release: Version 0.1.3 releasedDevelopment InformationReporting bugs and feature requestsPlease use the GitHub issue tracker to report issues or suggest features:https://github.com/nightwatchcybersecurity/release_auditorYou can also send emai toresearch /at/ nightwatchcybersecurity [dot] comWishlistAdd unit testsTBD
Package: releasecmd
releasecmdSummaryreleasecmdis areleasesubcommand forsetup.py(setuptools.setup).
The subcommand creates a git tag and pushes and uploads packages toPyPI.The subcommand class (releasecmd.ReleaseCommand) is implemented as a subclass ofsetuptools.Commandclass.
Thereleasesubcommand will do the followings:Find a file that defined the package version (__version__variable)Create a git tag from the package version informationGPG signing to the git tag if--signoption is specifiedPush git tagsUpload package files to PyPI by usingtwineInstallationpip install releasecmdUsagesetup.py:importsetuptoolsfromreleasecmdimportReleaseCommandsetuptools.setup(...cmdclass={"release":ReleaseCommand},)Example$ python3 setup.py release
running release
[get the version from ./releasecmd/__version__.py]
[pull git tags]
Already up to date.
[check existing git tags]
[create a git tag: v0.0.15]
[push git tags]
[upload the package to PyPI]
...prerequisite: package binaries must be in thedist/directory.Specify version manuallyYou can specify a version manually by--versionoption:$ python3 setup.py release --version 0.1.0
[create a git tag: v0.1.0]
[pull git tags]
Already up to date.
[check existing git tags]
[push git tags]
[upload packages to PyPI]Create a GPG signed tag and upload packages$ python3 setup.py release --sign
running release
[get the version from ./releasecmd/__version__.py]
[pull git tags]
Already up to date.
[check existing git tags]
[create a git tag with gpg signing: v0.1.0]
[push git tags]
[upload packages to PyPI]
...Skip create a git tag and upload packages$ python3 setup.py release --skip-tagging
running release
[get the version from ./releasecmd/__version__.py]
skip git tagging
[upload packages to PyPI]
...release command optionsOptions for 'ReleaseCommand' command:
  --skip-tagging    skip a git tag creation
  --skip-uploading  skip uploading packages to PyPI
  --dry-run         do no harm
  --sign            make a GPG-signed git tag
  --verbose         show verbose output
  --search-dir      specify a root directory path to search a version file.
                    defaults to the current directory.
  --tag-template    specify git tag format. defaults to 'v{version}'.
  --version         specify version manuallyDependenciesPython 3.8+Git
Package: release-exporter
Release ExporterBlog:https://www.gollahalli.com/blog/export-your-github-and-gitlab-releases-as-a-changelog/This CLI exports your project releases to a markdown based onkeep a
changelogandmarkdownlintand JSON,
it currently supports GitHub and GitLab.Installpipinstallrelease-exporterOr download this repository and type in the following in your
terminal/cmdpythonsetup.pyinstallUsageIn your terminal/cmd, change to the folder where your repository is
located and do the following:rex--token<yourtoken>markdownThat's it. You should see aCHANGELOG.mdin your folder.Advance UsageRelease exporter has the following optionsUsage:rex[OPTIONS]COMMAND[ARGS]...

Options:--tokenTEXTTokennumberifitsaprivaterepository.--urlTEXTURLofyourrepository.Thisisoptionalifyourcurrentdirectoryhas.gitfolderwithremoteurl.--locationTEXTLocallocationofyourrepository.--version--universalTEXTCreateaglobalsettingsfile.DefaultstoTrue.--helpShowthismessageandexit.

Commands:allCreateschangelogforallformats.initCreates.rexfile.jsonCreatesJSONfile.markdownCreatesmarkdownfile.rstCreatesreStructuredTextfile.If you don't have a repository on your computer, but you still want to
generate a change log you can manually add your repository URL as
follows:rexmarkdown--token<yourtoken>--url<yoururl>If you have your repository in a different location and you are lazy
(like me) to change into that directory, get the absolute path of your
repository add it to the--location <location>.rex--token<yourtoken>--location<absolutepath>You can also export your releases to JSON file by just replacingmarkdownwithjson. The output looks something like this:{"repositoryName":"release-exporter","provider":"github.com","owner":"akshaybabloo","repoUrl":"https://github.com/akshaybabloo/release-exporter","totalTags":2,"data":[{"tagName":"Unreleased","description":"","createdAt":"","compareUrl":"https://github.com/akshaybabloo/release-exporter/compare/1.0.1...HEAD"},{"tagName":"v1.0.1","description":"### Added\n- Unreleased tag added to the template and GitHub\n- Unreleased tag added to GitHub\n\n### Fixed\n- Tag missing in GitHub JSON fixed\n- Tag missing in GitLab JSON fixed","createdAt":"2018-01-16","compareUrl":"https://github.com/akshaybabloo/release-exporter/compare/v1.0...v1.0.1"},{"tagName":"v1.0","description":"Initial release.","createdAt":"2018-01-15","compareUrl":null}]}Note:TheUnreleasedtag is not counted intotalTags.Problems you might encounterIf you are usingrexon your repository folder or if you are using--locationyou might get an error saying that there are duplicate
keys, this is because sometimes the file.git/confighas more than
one[remote "origin"]. In such case, it is best to give the URL of
your repository by giving--url <repo URL>.ReferenceMarkdown logo fromhttps://github.com/dcurtis/markdown-mark/
Package: release-feed-mediola
release-feed-mediolaA tool that generates Atom feeds for software updates ofMediolaproducts.Not affiliated with Mediola – connected living AG.InstallationInstalling from PyPITo installrelease-feed-mediolafrom PyPI, open a shell and run:pipinstallrelease_feed_mediolaIf that doesn’t work, try:python3-mpipinstallrelease_feed_mediolaInstalling from the AURDirect your favoriteAUR helperto therelease-feed-mediolapackage.UsageTo generate an Atom feed for a given Mediola product, runrelease-feed-mediolawith the product name as an argument.To see a list of supported Mediola products, runrelease-feed-mediolawithout arguments.Updating your feed automatically with systemdYou may want to runrelease-feed-mediolaperiodically to generate
and update a feed file using a systemd timer and service.
This requires Linux.Installing the unit filesFirst, install the systemd timer and service unit files.If you have installedrelease-feed-mediolafrom the AUR, the
unit files have already been installed.Otherwise, download the unit filesrelease-feed-mediola@.serviceandrelease-feed-mediola@.timerfrom thecontrib/systemddirectory on the GitHub repository. Place the files into your/etc/systemd/user/directory. You may have to edit the
paths inside theExecStartdirective to match your Linux distro.Enabling the timerTo enable the systemd timer for a given Mediola product, run:# Replace PRODUCT with the Mediola product name you want to tracksystemctl--userenable--nowrelease-feed-mediola@PRODUCT.timerYou can enable and run multiple timers for different Mediola
products at the same time.Each service will generate onefeed.atomfile per product once a
day.Setting up your feed readerFor each product you want to track, locate the generatedfeed.atomfile in the~/.local/share/feeds/release-feed-mediola/directory
hierarchy. Point your feed reader software to that file.You may want to use afile:///URL if your reader doesn’t support
feeds from the local filesystem directly. For example:file:///home/yourusername/.local/share/feeds/release-feed-mediola/neo/feed.atomDisabling the timerTo disable the systemd timer for a given Mediola product, run:# Replace PRODUCT with the Mediola product name you want to tracksystemctl--userdisable--nowrelease-feed-mediola@PRODUCT.timerContributing to release-feed-mediolaSeeCONTRIBUTING.md.Legal noticeThis tool and repository are not affiliated with Mediola – connected
living AG. Products mentioned in the Atom feed generated by this
tool may be subject to Mediola’s end-user license agreement (EULA).
For your convenience, each entry of the generated feed includes a
link to that EULA.LicenseCopyright (c) 2022 – 2024 Claudia PellegrinoLicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
For a copy of the License, seeLICENSE.
Package: release-finder
release-finderrelease-finderis a Python package that helps you find the latest releases information for a given GitHub repository.InstallationInstall therelease-finderusing the package managerpip.pipinstallrelease-finderUsageimportrelease_finder# returns 'v4.1.6'release_finder.latest_release("https://github.com/utmapp/UTM")# returns 'v4.1.6'release_finder.latest_release("https://github.com/utmapp/UTM","YOUR_ACCESS_TOKEN")# returns '2023-02-27T02:28:31Z'release_finder.latest_release_date("https://github.com/utmapp/UTM")# returns '2023-02-27T02:28:31Z'release_finder.latest_release_date("https://github.com/utmapp/UTM","YOUR_ACCESS_TOKEN")ContributingPull requests are welcome. Please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: releaselog
releaselog is a tool for generating release notes from git logs fortaskotron. Currently it only supports a very small and fragile setup specific to Taskotron. This script might however be useful for other projects as well.InstallationpipinstallreleaselogOrpythonsetup.pyinstallUsagereleaselog was originally meant to generate RST files for use withSphinx, but could be used for pretty much anything else that wants RST. Adding this to your project should be simple.Assumptions:project uses sphinx for documentationsphinx uses a ‘docs’ directoryFrom your ‘docs’ directory, run “releaselog”. The default is to drop down into the parent directory and examine git logs. You can optionally pass in a path “releaselog <path>” and it will add the docs to <path>/docs/source.