Saravanan666/pypi-org huggingface dataset:
Package: ospgrillage
Overviewospgrillageis a python wrapper of theOpenSeesPypackage to speed up the creation of bridge deck grillage models.OpenSeesPyis a python interpreter of the well-know Open System for Earthquake Engineering Simulation (OpenSees) software framework.ospgrillageprovides a simple python API which allows users to:Quickly generate and analyze a bridge deck grillage model in theOpenSeesPymodel space, including many forms of loading and load cases;Export a py file containing theOpenSeesPycommands, which on execution, creates the prescribedOpenSeesPygrillage model.The ability to useospgrillagedirectly to do bridge deck analysis, or to export theOpenSeesPycommand file for further editing, facilites an enormous range of use cases in both practice and research.Documentationospgrillage's full documentation can be foundhere.InstallationInstall using pip:pipinstallospgrillageRefer toinstallationfor more information.ContributionsCheck out ourcontributing guideto learn more on contributing, coding rules, community Code of Conduct and more.CitingIf you useospgrillagein you work, please cite the corresponding JOSS paper:Ngan J.W. and Caprani, C.C. (2022), "ospgrillage: A bridge deck grillage analysis preprocessor for OpenSeesPy",Journal of Open Source Software, 7(77), 4404,doi.org/10.21105/joss.04404CapabilitiesModellingBridge Deck ModelsBeam elements only - a traditional form of modelBeam elements with rigid links - a modification of the traditional form for box sectionsShell and beam elements - the modern form of modelling, but with more complex results interpretationMeshingSingle-span decksSkewed (Oblique) and Orthogonal meshesPositive and negative skew anglesAllows for skew mesh to be set up to 30 degreesAllows for orthogonal mesh to be set no less than 11 degreesGrillage elements grouped automatically for easy assignment of propertiesAutodetect edges of spans as supporting nodesAllows for diaphragms / end slabsAllows for unit width properties for transverse slab/membersPinned and roller supportsElement typesThe followingOpenSeeselement types are supported:elasticBeamColumnTimoshenkoBeamColumnShell elementsMaterialsA JSON materials library file for codified common material propertiesUtilitiesLoad typesNodal loadsPoint loadsLine loadsPatch loadsCompound loads (any combination of the above load types)AnalysisLoad cases contain arbitrary multiple load types, including compound loadsMoving load analysis of arbitrary load types through compound loadsPost-processingOutput results utilise python'sxarrayformatRetrieve result envelopes fromxarrayresultsQuery options for moving loadPlotting displacement and force component from the resultsCreditsMayer Melhem for producing the documentation illustrations and the design of theospgrillagelogo.
Package: osplo
OSPLO: OneSentencePerLineOpener===============================- Viele Computerlinguistische Verfahren profitieren davon, wenn der Text im one-sentence-per line Format vorliegt (z.b die Erstellung von dünnbesetzten Scipy Matrizen für Dokument-Term Matrizen, oder auch einfach Textauswertungen bis hin zu dem Input Format für Gensim.- Oft ist das nicht der Fall, und in vielen Fällen ist es zu speicherintensiv, ein komplettes Korpus einzulesen, um dann eine Tokenisierung und Satztrennung durchzuführen.- OSPLO ist die naivste Lösung für dieses Problem.Es greift auf smart_open (Mit dem man auch komprimierte Archive und auch Webressourcen öffnen kann) zurück und verwendet den sehr guten Tokenizer und Satzsplitter SoMaJo.Das Endergebnis ist nicht besonders schnell, erlaubt aber direkte Iteration über eine Datei als wäre sie im One-Sentence-per-Line Format, ohne die Datei gleichzeitig einlesen zu müssen.z.B:```pythonfrom osplo import OSLOpenfor sentence in OSLOpen("https://www.gnu.org/licenses/gpl.txt"):print(sentence)```Die ersten Zeilen des Outputs würden dann so aussehen:```GNU GENERAL PUBLIC LICENSE Version 3 , 29 June 2007 Copyright ( C ) 2007 Free Software Foundation , Inc. < https://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document , but changing it is not allowed .Preamble The GNU General Public License is a free , copyleft license for software and other kinds of works .The licenses for most software and other practical works are designed to take away your freedom to share and change the works .By contrast , the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users .We , the Free Software Foundation , use the GNU General Public License for most of our software ; it applies also to any other work released this way by its authors .You can apply it to your programs , too .When we speak of free software , we are referring to freedom , not price .Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software ( and charge for them if you wish ) , that you receive source code or can get it if you want it , that you can change the software or use pieces of it in new free programs , and that you know you can do these things .To protect your rights , we need to prevent others from denying you these rights or asking you to surrender the rights .Therefore , you have certain responsibilities if you distribute copies of the software , or if you modify it : responsibilities to respect the freedom of others .For example , if you distribute copies of such a program , whether gratis or for a fee , you must pass on to the recipients the same freedoms that you received .You must make sure that they , too , receive or can get the source code .```Hinweis:Die Software ist unter GPL3.0 lizensiert, ist allerdings nur ein Demo-Projekt für die Nutzung von Packaging.
Package: osprey-design
Osprey DesignProvides a workflow to help you create reproducable OSPREY designsFree software: GNU General Public License v3Documentation:https://osprey-design.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theelgertam/cookiecutter-pipenvproject template, based onaudreyr/cookiecutter-pypackage.History0.1.0 (2019-06-13)First release on PyPI.
Package: osprofile
OSProfile这是一个用来在跨平台开发应用时，解决应用的配置文件在Windows及类Unix系统中存放路径的问题。
Quick Start直接使用：from osprofile import OSProfile# 构造
# 将在 类Unix的～/.config/myappname/myappname.json 文件中，
# 或者 在Windows下的 c:\users\<username>\appdata\Local\myappname\myapp.json 文件中，
# 写入默认配置 options1:’1234’
osp = OSProfile(appname=’myappname’, profile=’myappname.json’, options=dict(‘options1’:’1234’))# 从文件获取配置, 返回字典
osp.read_profile()# 更新配置文件, 传入字典，对已存在的键值进行覆盖，不存在的进行添加
kwargs = dict(options1=’12345’, options2=’11111’)
osp.update_profile(kwargs)
Package: osprofiler
OSProfiler provides a tiny but powerful library that is used by
most (soon to be all) OpenStack projects and their python clients. It
provides functionality to be able to generate 1 trace per request, that goes
through all involved services. This trace can then be extracted and used
to build a tree of calls which can be quite handy for a variety of
reasons (for example in isolating cross-project performance issues).Free software: Apache licenseDocumentation:https://docs.openstack.org/osprofiler/latest/Source:https://opendev.org/openstack/osprofilerBugs:https://bugs.launchpad.net/osprofilerRelease notes:https://docs.openstack.org/releasenotes/osprofiler
Package: ospx
ospxospx is an extension package tofarn, adding support to buildOSP(co-)simulation cases using functional mockup units (FMUs).ospx supportsbuilding of case-specificOSP(co-)simulation configuration fileswatching the progress of cosim, and saving final simulation results as a pandas dataframe.Installationpipinstallospxospx requires the following (sub-)package:dictIO: foundation package, enabling ospx to handle configuration files in dictIO dict file format.However, dictIO gets installed automatically with ospx.Usage Exampleospx provides both an API for use inside Python as well as a CLI for shell execution of core functions.Reading a caseDict file and building the case-specific OSP (co-)simulation configuration files:fromospximportOspCaseBuilderOspCaseBuilder.build('caseDict')The above task can also be invoked from the command line, using the 'ospCaseBuilder' command line script installed with ospx:ospCaseBuildercaseDictFor more examples and usage, please refer toospx's documentation.File FormatA caseDict is a file in dictIO dict file format used with farn.For a documentation of the caseDict file format, seeFile Formatinospx's documentationon GitHub Pages.For a detailed documentation of the dictIO dict file format used by farn, seedictIO's documentationon GitHub Pages.Development SetupInstall Python 3.9 or higher, i.e.Python 3.10orPython 3.11Update pip and setuptools:python-mpipinstall--upgradepipsetuptoolsgit clone the dictIO repository into your local development directory:gitclonehttps://github.com/dnv-opensource/ospxpath/to/your/dev/ospxIn the ospx root folder:Create a Python virtual environment:python-mvenv.venvActivate the virtual environment:..on Windows:>.venv\Scripts\activate.bat..on Linux:source.venv/bin/activateUpdate pip and setuptools:(.venv)$python-mpipinstall--upgradepipsetuptoolsInstall ospx's dependencies:(.venv)$pipinstall-rrequirements-dev.txtThis should return without errors.Finally, install ospx itself, yet not as a regular package but as aneditablepackage instead, using the pip install option -e:(.venv)$pipinstall-e.Test that the installation works (in the ospx root folder):(.venv)$pytest.MetaCopyright (c) 2024DNVopen sourceFrank Lumpitzsch –@LinkedIn–frank.lumpitzsch@dnv.comClaas Rostock –@LinkedIn–claas.rostock@dnv.comSeunghyeon Yoo –@LinkedIn–seunghyeon.yoo@dnv.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/dnv-opensource/ospxContributingFork it (https://github.com/dnv-opensource/ospx/fork)Create your branch (git checkout -b myBranchName)Commit your changes (e.g.git commit -m 'place a descriptive commit message here')Push to the branch (e.g.git push origin myBranchName)Create a new Pull Request in GitHubFor your contribution, please make sure you follow theSTYLEGUIDEbefore creating the Pull Request.
Package: ospylib
os.py - Python library as well as command prompt tool to read and manipulate machine information đź’»InstallationYou can install os.py using pip:
pip install ospylibUsageTo use os.py, simply import it at the beginning of your Python script:import ospylibContributingIf you would like to contribute to os.py, please submit a pull request on GitHub.Licenseos.py is released under the BSD-3-Clause license. See LICENSE.md for more information.
Package: os-qdb-protocal
# os-qdb-protocal[![Build Status](https://www.travis-ci.org/cfhamlet/os-qdb-protocal.svg?branch=master)](https://www.travis-ci.org/cfhamlet/os-qdb-protocal)[![codecov](https://codecov.io/gh/cfhamlet/os-qdb-protocal/branch/master/graph/badge.svg)](https://codecov.io/gh/cfhamlet/os-qdb-protocal)[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/os-qdb-protocal.svg)](https://pypi.python.org/pypi/os-qdb-protocal)[![PyPI](https://img.shields.io/pypi/v/os-qdb-protocal.svg)](https://pypi.python.org/pypi/os-qdb-protocal)qdb protocal.Support qdb commands: ``get``, ``test``# Install`pip install os-qdb-protocal`# Usage* create a protocal object from cmd and key```from os_qdb_protocal import create_protocalproto = create_protocal('get', b'test-key')```* use upstream method to generate data to be send```# s is something which used to write data to, like socket, file, etc.for data in proto.upstream():s.send(data)```* use downstream method to parse data and get size will be recived```downstream = proto.downstream()read_size = next(downstream)while read_size >= 0:# s is something which used to recieve data from, like socket, file, etc.data = s.read(read_size)read_size = downstream.send(data)```* access key and value```proto.keyproto.value```# Unit Tests`$ tox`# LicenseMIT licensed.
Package: osql-cli
osql-cliWe’re excited to introduceosql-cli, a new and interactive command line query tool for SQL Server. This open source tool works cross-platform and proud to be a part of thedbcli.org community.FeaturesAuto-completionSyntax highlightingQuery historyConfiguration file supportMulti-line queriesGet osql-cliSupported PlatformDownloads (stable)Downloads (preview)How to InstallWindows (x64).whl windows (x64).whl windows (x64) (dev)Install on windowsWindows (x86).whl windows (x86).whl windows (x86) (dev)Install on windowsmacOS 10.12+.whl.whl (dev)Install on macosUbuntu 17.04.deb.deb (dev)Install on ubuntu17Ubuntu 16.04.deb.deb (dev)Install on ubuntu16Ubuntu 14.04.deb.deb (dev)Install on ubuntu14Debian 8.7+.deb.deb (dev)Install on debian8Debian 9.deb.deb (dev)Install on debian9CentOS 7.rpm.rpm (dev)Install on centosRed Hat Enterprise Linux 7.rpm.rpm (dev)Install on rhel7OpenSUSE 42.2+.rpm.rpm (dev)Install on opensuse42Fedora 25.rpm.rpm (dev)Install on fedora25Fedora 26.rpm.rpm (dev)Install on fedora26Installation via apt, yumPlease refer toGet osql-clifor detailed install instructions per platform.Installation via pip$pipinstallosql-cliPlease refer toInstall via pipfor detailed install instructions via pip on each platformUsagePlease refer to theusage guidefor details on options and example usage. If you are having any issues using osql-cli, please see thetroubleshooting guide.For general help content, pass in the-hparameter:$osql-cli--helpThe osql-cli tool includes a telemetry feature.  Please refer to thetelemetry guidefor more information.ContributingIf you would like to contribute to the project, please refer to thedevelopment guide.Reporting issues and feedbackIf you encounter any bugs with the tool please file an issue in theIssuessection of our GitHub repo.Code of ConductThis project has adopted theMicrosoft Open Source Code of Conduct. For more information see theCode of Conduct FAQor contactopencode@microsoft.comwith any additional questions or comments.Licenseosql-cli is licensed under theBSD-3 license.
Package: osqp
Python wrapper forOSQP: the Operator
Splitting QP Solver.The OSQP (Operator Splitting Quadratic Program) solver is a numerical
optimization package for solving problems in the formminimize        0.5 x' P x + q' x

subject to      l <= A x <= uwherex in R^nis the optimization variable. The objective function
is defined by a positive semidefinite matrixP in S^n_+and vectorq in R^n. The linear constraints are defined by matrixA inR^{mx n}and vectorsl in R^m U{-inf}^m,u in R^m U{+inf}^m.InstallationTo installosqpfor python, make sure that you’re using a recent version ofpip(pip install--upgradepip)
and then usepip install osqp.To installosqpfrom source, clone the repository (git clone--recurse-submoduleshttps://github.com/osqp/osqp-python)
and runpip install .from inside the cloned folder.DocumentationThe interface is documentedhere.PackagingThis repository performs the tests and builds the pypi wheels. Conda packages are onconda-forge.
Package: osqp-mkl
Python wrapper forOSQP: the Operator
Splitting QP Solver.The OSQP (Operator Splitting Quadratic Program) solver is a numerical
optimization package for solving problems in the formminimize        0.5 x' P x + q' x

subject to      l <= A x <= uwherex in R^nis the optimization variable. The objective function
is defined by a positive semidefinite matrixP in S^n_+and vectorq in R^n. The linear constraints are defined by matrixA inR^{mx n}and vectorsl in R^m U{-inf}^m,u in R^m U{+inf}^m.InstallationTo installosqpfor python, make sure that you’re using a recent version ofpip(pip install--upgradepip)
and then usepip install osqp.To installosqpfrom source, clone the repository (git clone--recurse-submoduleshttps://github.com/osqp/osqp-python)
and runpip install .from inside the cloned folder.DocumentationThe interface is documentedhere.PackagingThis repository performs the tests and builds the pypi wheels. Conda packages are onconda-forge.
Package: osr2mp4
Getting startedfromosr2mp4.osr2mp4importOsr2mp4data={"osu! path":"/Users/yuitora./osu!/","Skin path":"/Users/yuitora./osu!/Skins/-#Whitecat#-","Beatmap path":"/Users/yuitora./osu!/Songs/123456 Hachigatsu, Bou/",".osr path":"/Users/yuitora./osu!/Replays/yuitora_12317423.osr","Default skin path":"/Users/yuitora./Downloads/Default Skin/","Output path":"output.avi","Width":1920,"Height":1080,"FPS":60,"Start time":0,"End time":-1,"Video codec":"XVID","Process":2,"ffmpeg path":"Users/yuitora./ffmpeg/bin/ffmpeg.exe"}settings={"Cursor size":1,"In-game interface":True,"Show scoreboard":True,"Background dim":90,"Always show key overlay":True,"Automatic cursor size":False,"Rotate sliderball":False,"Score meter size":1.25,"Song volume":50,"Effect volume":100,"Ignore beatmap hitsounds":True,"Use skin's sound samples":True,"Global leaderboard":False,"Mods leaderboard":"(HD)HR","api key":"lol"}converter=Osr2mp4(data,settings)converter.startall()converter.joinall()Or you can savedataandsettingsin a json file.
config.json:{"osu! path":"/Users/yuitora./osu!/","Skin path":"/Users/yuitora./osu!/Skins/-#Whitecat#-","Beatmap path":"/Users/yuitora./osu!/Songs/123456 Hachigatsu, Bou/",".osr path":"/Users/yuitora./osu!/Replays/yuitora_12317423.osr","Default skin path":"/Users/yuitora./Downloads/Default Skin/","Output path":"output.avi","Width":1920,"Height":1080,"FPS":60,"Start time":0,"End time":-1,"Video codec":"XVID","Process":2,"ffmpeg path":"Users/yuitora./ffmpeg/bin/ffmpeg.exe"}settings.json:{"Cursor size":1,"In-game interface":true,"Show scoreboard":true,"Background dim":90,"Always show key overlay":true,"Automatic cursor size":true,"Rotate sliderball":false,"Score meter size":1.25,"Song volume":50,"Effect volume":100,"Ignore beatmap hitsounds":true,"Use skin's sound samples":true,"Global leaderboard":false,"Mods leaderboard":"(HD)HR","api key":"lol"}And to load it in codefromosr2mp4.osr2mp4importOsr2mp4converter=Osr2mp4(filedata="config.json",filesettings="settings.json")converter.startall()converter.joinall()DocumentationOsr2mp4.startvideo()Start video without audio.Osr2mp4.joinvideo()Wait for video to finish.Osr2mp4.startaudio()Start audio.Osr2mp4.joinaudio()Wait for audio to finish.Osr2mp4.startall()Start video and audio.Osr2mp4.joinall()Wait for all to finish.Osr2mp4.getprogress()Return a value from 0 to 100 corresponding to the estimated progress of the conversion.
Package: os-refresh-config
Team and repository tagsos-refresh-configos-refresh-configusesdib-run-partsto run scripts in a
pre-defined set of directories:/opt/stack/os-config-refresh/pre-configure.d
/opt/stack/os-config-refresh/configure.d
/opt/stack/os-config-refresh/post-configure.d
/opt/stack/os-config-refresh/migration.d
/opt/stack/os-config-refresh/error.d/opt/stack/os-config-refreshis the default base directory. You can
setOS_REFRESH_CONFIG_BASE_DIRenvironment variable to override the
default one.Its intended purpose is to separate scripts execution into 4 phases:Quiesce(pre-configure.d),Configure(configure.d),Activate(post-configure.d).Migrate(migration.d),It runs through all the phases above to ensure configuration is applied
and enabled on a machine. It will run the scripts in error.d and then
exit with a non-zero exit status if any phase has a problem. The scripts
in each phase should not depend on each other having worked properly.Note: Earlier versions of os-refresh-config ran migration before
post-configure. This was an oversight in the initial design, as
migrations are intended to be online migrations after the host is
fully configured.For things which must happen while the service is quiesced, that should
be done in the post-configure scripts which control the service state.
Package: os-release
os-releaseos-releaseis a simple Python module for reading systemd'sos-releaseinformation on modern Linux distributions.It parses the contents of systemd'sos-release(os-release(5)) files:/etc/os-releaseor/usr/lib/os-release.Installationos-releaseis available onPyPI.Add it to your projectssetup.pyinstall_requires:install_requires=['os-release']or install it directly viapip:$ pip install os-releaseAlternatively, clone this Git repository and run:$ python setup.py installDocumentation:Documentation foros-releaseis available onRead the Docs.
Package: os-resource-classes
A list of standardized resource classes for OpenStack.A resource class is a distinct type of inventory that exists in
a cloud environment, for exampleVCPU,DISK_GB. They are
upper case with underscores. They often include a unit in their
name.This package provides a collection of symbols representing those
standard resource classes which are expected to be available in
any OpenStack deployment.There also exists a concept of custom resource classes. These
are countable types that are custom to a particular environment.
The OpenStackplacement APIprovides a way to create these. A
custom resource class always begins with aCUSTOM_prefix.Free software: Apache licenseDocumentation:https://docs.openstack.org/os-resource-classes/latestSource:https://opendev.org/openstack/os-resource-classesBugs:https://storyboard.openstack.org/#!/project/openstack/os-resource-classes
Package: osri
OSRI: OS Release InfoA CLI that shows parsed data from an linux os release file, usually located at /etc/os-release.InstallationInstall from a git branch:pipinstallgit+https://github.com/odra/osri.git@masterUsageShowing the CLI version:osriversionDisplaying an os release info (path defaults to/etc/os-release):osridisplay
osridisplay--path/etc/another-os-releaseLicenseMIT
Package: osrl-lib
OSRL (Offline Safe Reinforcement Learning)offers a collection of elegant and extensible implementations of state-of-the-art offline safe reinforcement learning (RL) algorithms. Aimed at propelling research in offline safe RL, OSRL serves as a solid foundation to implement, benchmark, and iterate on safe RL solutions.The OSRL package is a crucial component of our larger benchmarking suite for offline safe learning, which also includesDSRLandFSRL, and is built to facilitate the development of robust and reliable offline safe RL solutions.To learn more, please visit ourproject website.StructureThe structure of this repo is as follows:├── examples
│   ├── configs  # the training configs of each algorithm
│   ├── eval     # the evaluation escipts
│   ├── train    # the training scipts
├── osrl
│   ├── algorithms  # offline safe RL algorithms
│   ├── common      # base networks and utilsThe implemented offline safe RL and imitation learning algorithms include:AlgorithmTypeDescriptionBCQ-LagQ-learningBCQwithPID LagrangianBEAR-LagQ-learningBEARLwithPID LagrangianCPQQ-learningConstraints Penalized Q-learning (CPQ))COptiDICEDistribution Correction EstimationOffline Constrained Policy Optimization via stationary DIstribution Correction EstimationCDTSequential ModelingConstrained Decision TransformerBC-AllImitation LearningBehavior Cloningwith all datasetsBC-SafeImitation LearningBehavior Cloningwith safe trajectoriesBC-FrontierImitation LearningBehavior Cloningwith high-reward trajectoriesInstallationPull the repo and install:git clone https://github.com/liuzuxin/OSRL.git
cd osrl
pip install -e .
pip install OApackage==2.7.6How to use OSRLThe example usage are in theexamplesfolder, where you can find the training and evaluation scripts for all the algorithms.
All the parameters and their default configs for each algorithm are available in theexamples/configsfolder.
OSRL uses theWandbLoggerinFSRL. The offline dataset and offline environments are provided inDSRL, so make sure you install both of them first.TrainingFor example, to train thebcqlmethod, simply run by overriding the default parameters:pythonexamples/train/train_bcql.py--taskOfflineCarCirvle-v0--param1args1...By default, the config file and the logs during training will be written tologs\folder and the training plots can be viewed online using Wandb.You can also launch a sequence of experiments or in parallel via theEasyRunnerpackage, seeexamples/train_all_tasks.pyfor details.EvaluationTo evaluate a trained agent, for example, a BCQ agent, simply runpython example/eval/eval_bcql.py --path path_to_model --eval_episodes 20It will load config file frompath_to_model/config.yamland model file frompath_to_model/checkpoints/model.pt, run 20 episodes, and print the average normalized reward and cost.