Saravanan666/pypi-org huggingface dataset:
Package: opengrokfs
opengrokfs implements a FUSE filesystem backed by the OpenGrok web interface.
A cscope clone which uses the OpenGrok web interface as the backend is also
included.Install with pip:pip3 install opengrokfsRunopengrokfswith the URL of the OpenGrok start page and the local
directory where the filesystem should be mounted:mkdir -p mnt
opengrokfs http://androidxref.com/7.1.1_r6/ mntNow you can access the files:$ ls mnt/
Android.bp  bootable        bionic  development  external
art         bootstrap.bash  dalvik  device       frameworks
...Useoggrepto search using OpenGrok’s “Full Search”:$ cd mnt/bionic/
mnt/bionic$ oggrep O_LARGEFILE
libc/kernel/uapi/asm-arm/asm/fcntl.h:#define O_LARGEFILE 0400000
libc/kernel/uapi/asm-arm64/asm/fcntl.h:#define O_LARGEFILE 0400000
libc/bionic/lfs64_support.cpp:// are already 64-bit ready. In particular, we don't have non-O_LARGEFILE
libc/bionic/open.cpp:  return flags | O_LARGEFILE;
libc/kernel/uapi/asm-mips/asm/fcntl.h:#define O_LARGEFILE 0x2000
libc/kernel/uapi/asm-generic/fcntl.h:#ifndef O_LARGEFILE
libc/kernel/uapi/asm-generic/fcntl.h:#define O_LARGEFILE 00100000
tests/stdlib_test.cpp:  ASSERT_EQ(O_LARGEFILE, fcntl(tf.fd, F_GETFL) & O_LARGEFILE);ogcscopeemulates cscope’s line-oriented interface but uses OpenGrok as the
backend:mnt/bionic$ ogcscope -dl
>> 1CLONE_CHILD_SETTID
cscope: 1 lines
libc/kernel/uapi/linux/sched.h <unknown> 41 #define CLONE_CHILD_SETTID 0x01000000
>> 0CLONE_CHILD_SETTID
cscope: 5 lines
libc/bionic/clone.cpp <unknown> 53   if ((flags & (CLONE_PARENT_SETTID|CLONE_SETTLS|CLONE_CHILD_SETTID|CLONE_CHILD_CLEARTID)) != 0) {
libc/bionic/clone.cpp <unknown> 56   if ((flags & (CLONE_SETTLS|CLONE_CHILD_SETTID|CLONE_CHILD_CLEARTID)) != 0) {
libc/bionic/clone.cpp <unknown> 59   if ((flags & (CLONE_CHILD_SETTID|CLONE_CHILD_CLEARTID)) != 0) {
libc/bionic/fork.cpp <unknown> 34 #define FORK_FLAGS (CLONE_CHILD_SETTID | CLONE_CHILD_CLEARTID | SIGCHLD)
libc/kernel/uapi/linux/sched.h <unknown> 41 #define CLONE_CHILD_SETTID 0x01000000ogcscopecan be used from within any editor which supports cscope.  For
example, forvim:mnt/bionic$ vim -c 'set cscopeprg=ogcscope' -c 'cs add .opengrokfs'
Package: opengsq
OpenGSQ Python LibraryThe OpenGSQ Python library provides a convenient way to query servers
from applications written in the Python language.Supported ProtocolsThe library supports a wide range of protocols. Here are some examples:fromopengsq.protocolsimport(ASE,Battlefield,Doom3,EOS,FiveM,GameSpy1,GameSpy2,GameSpy3,GameSpy4,Kaillera,KillingFloor,Minecraft,Quake1,Quake2,Quake3,RakNet,Samp,Satisfactory,Scum,Source,TeamSpeak3,Unreal2,Vcmp,WON,)RequirementsPython 3.7 or higherInstallationThe recommended installation method is usingpip:pipinstall--upgradeopengsqUsageHere’s an example of how to query a server using the Source protocol:importasynciofromopengsq.protocolsimportSourceasyncdefmain():source=Source(host='45.147.5.5',port=27015)info=awaitsource.get_info()print(info)asyncio.run(main())You can also use the Source Remote Console:importasynciofromopengsq.exceptionsimportAuthenticationExceptionfromopengsq.rcon_protocols.source_rconimportSourceRconasyncdefmain():withSourceRcon("123.123.123.123",27015)assource_rcon:try:awaitsource_rcon.authenticate("serverRconPassword")exceptAuthenticationException:print('Failed to authenticate')response=awaitsource_rcon.send_command("cvarlist")print(response)asyncio.run(main())Command-line interfaceThis library additionally provides anopengsqcommand-line utility
which makes it easy to query game servers from your terminal. Runopengsq -hfor usage.# query server using source protocolopengsqsource--host123.123.123.123--port27015--functionget_infoTests and ResultsYou can find information about tests and results athttps://python.opengsq.com/tests/protocolsStargazers over time
Package: opengui
openguiLibrary for building dynamic forms, forms whose options and even fields change based on values in other fields.This doesn't involve rendering dynamic forms, just making creating and altering the structures thereof.Example# Create a single multi select fieldfields=opengui.Fields(values=values,# Values of the fields in key:value dict.fields=[{"name":"types","options":["textarea","options","fields"],"multi":True,"trigger":True}])# If they select textarea, add itif"textarea"in(fields["types"].valueor[]):fields.append({"name":"people","style":"textarea"})fields.ready=True# If they selected option, add a format, then check what format they selectedif"options"in(fields["types"].valueor[]):fields.append({"name":"style","options":["radios","select"],"default":"radios","trigger":True})fields.append({"name":"stuff","options":["fee","fie","foe","fum"],"style":fields["style"].value})fields.ready=True# If they add subfields, add two, and make the second optionalif"fields"in(fields["types"].valueor[]):fields.append({"name":"things","fields":[{"name":"yin",},{"name":"yang","optional":True}]})fields.ready=True# Retrn as a dictreturnfields.to_dict(),200Morehere
Package: openhab-pythonrule-engine
OpenHAB python rule engineA Python 3.x rule engine for OpenHAB. This rule engine allows you to define rules using Python 3.x.Please note thatOpenHAB username/password authentication(basic authentication)
must be activated. To do so, openAPI security settingsand enable the advanced settingAllow Basic Authentication.To run this software, you can use Docker or thePIPpackage manager, as shown belowDocker approachsudo docker run -e openhab_uri=http://192.168.1.17:8080 -e user=me -e pwd=secret -v /etc/openhab2/automation/rules/python:/rules grro/pythonrule_enginePIP approachsudo pip install openhab-pythonrule-enginesudo pyrule --command listen --openhab_uri http://localhost:8080 --python_rule_directory /etc/openhab2/automation/rules/python --user me --pwd secretIn this case, the rules engine connects to the Openhab instance running on the local machine on port 8080. Also, the /etc/openhab2/automation/rules/python directory is used to search for python-based rulesIf you are using asystemd-based Linux distribution, you can use theregistercommand to register and start the rule engine as a systemd entity.
This automatically starts the rule engine at boot time. Starting the Rule Engine manually with thelistencommand is no longer necessary.sudo pyrule --command register --openhab_uri http://localhost:8080 --python_rule_directory /etc/openhab2/automation/rules/python --user me --pwd secretRulesTo trigger a rule method, the@whendecorator must be used. Currently the conditions listed below are supportedconditionexampledescriptioncron@when('Time cron */1 * * * *')fires based on cron expressionitem state change@when('Item PhoneLisaLastSeen changed')fires when the specified Item's State changesitem command@when('Item SelectDoorCam received command ON')@when('Item SelectDoorCam received command OFF')fires when the specified Item receives a CommandExample:my_rule.py(located within /etc/openhab2/automation/rules/python)fromopenhab_pythonrule_engine.conditionimportwhenfromopenhab_pythonrule_engine.item_registryimportItemRegistry@when("Time cron */1 * * * *")# each minute@when("Item PhoneLisaLastSeen changed")@when("Item PhoneTimLastSeen changed")defupdate_presence_based_on_phone_seen(item_registry:ItemRegistry):last_time_present=item_registry.get_state_as_datetime('LastDateTimePresence')forphone_nameinitem_registry.get_group_membernames('Phones'):last_seen=item_registry.get_state_as_datetime(phone_name)print(last_seen)iflast_seen>last_time_present:last_time_present=last_seenitem_registry.set_state('LastDateTimePresence',last_time_present)# uncomment the code below for local debugging#item_registry = ItemRegistry.new_singleton(openhab_uri="http://192.168.1.27:8080/", user="xxx", pwd="secret")#update_presence_based_on_phone_seen(item_registry)If the rule method defines a (single!) argument, the item_registry object is automatically injected.
The item_registry object provides methods for getting and setting the item state. When setting the state, the data value
is automatically converted to the item-specific data type
Package: open-helpdesk
Created bySimone DallaOverviewOpenHelpdesk is an helpdesk ticket management system for Django/Mezzanine projectThis project is under development.DependenciesOpenHelpdesk required:Django1.5+, 1.6+Mezzanine3.+DocumentationPlease head over toDocumentationfor all
the details on how to install, configurate and use the OpenHelpdesk.Live DemoLive demois online!LicenseOpenHelpdesk isBSD licensed.History0.4.1 (2014-08-25)First release on PyPI.
Package: openhexa.toolbox
Open-source Data integration platformOpenHEXA ToolboxOpenHEXA is an open-source data integration platform developed byBluesquare.The Toolbox is an utility library to acquire and process data from various data sources. It is installed by default
on OpenHEXA, and can be used both in notebooks and in data pipelines.See theUsing the OpenHEXA Toolboxsection of the
wiki for usage instructions.Installationpipinstallopenhexa.toolboxModulesopenhexa.toolbox.dhis2Acquire and process data from DHIS2 instances
Package: openhltest
# OpenHLTest
[![Validated](https://travis-ci.org/OpenHLTest/data-models.svg?branch=master)](https://travis-ci.org/OpenHLTest/data-models)
[![Package](https://img.shields.io/pypi/v/openhltest.svg)](https://pypi.org/project/openhltest)
[![Version](https://img.shields.io/pypi/pyversions/openhltest.svg)](https://pypi.org/project/openhltest)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](https://en.wikipedia.org/wiki/MIT_License)OpenHLTest is a collaborative effort by test vendors to develop agnostic programmatic interfaces and tools for managing test equipment.OpenHLTest’s focus is the following:
- a consistent set of YANG vendor-neutral data models
- continuous integration yielding a single set of client artifacts
- test vendor server implementations of the data models## Artifacts
On every commit to the repository a [Travis job](https://travis-ci.org/OpenHLTest/data-models) will do the following:
- validate the models
- generate the python package and the documentation browser artifacts
- upload the [python package](https://pypi.org/project/openhltest/) and the [documentation browser](https://openhltest.github.io/docs/index.html)## Contributing
This repository is primarily for publishing the models, documents, and other material developed by the OpenHLTest working group. We generally do not accept pull requests in this repository.Feedback and suggestions to improve OpenHLTest models is welcomed by opening a [GitHub issue](https://github.com/OpenHLTest/data-models/issues).
Package: openhltspirent
OpenHlTest Python ClientThe openhltest folder is the repository for the OpenHlTest python client module.Auto generated python clientThe auto generated library will follow these guidelines:It will be auto-generated every time there is a model change that has been successfully validated.It will be hierarchical and match the hierarchy of the yang model.It will consist of base infrastructure that encapsulates transport, serialization and errors.Once the library has been generated and unit tests pass it will be packaged and posted on pypi.python.org.The library will be available for install using the command:pip install openhltspirentBase infrastructure that is not auto-generatedHttps transport class encapsulated in HttpTransportCrudx and utility methods encapsulated in BaseThe pyang plugin python_client_plugin.py will auto generate the followingclasses for the following yang keywords:containerlistclass @property accessors for the following yang keywords:leafleaf-listcontainerlistclass methods for the following yang keywords:actionrpcclass @property accessors to access child container, list classesDocumentationupload thepython packageand thedocumentation browserSample codeSample code is located in the openhltest/samples folder.
Package: openhtf-docx-report
openhtf-docx-report PackageThis is a simple Openhtf/Spintop-Openhtf report callback to generate a MS-WORD
test report.To use it you have to add an import statement
from openhtf_docx_report import docx_report_callbackand then a callback in your main test sequence
test.add_output_callbacks(docx_report_callback)and to add the test description, etc fields you have to add them as metadatatest = htf.Test(hello_world, test_name='MyTest', test_description='OpenHTF Example Test',
test_version='1.0.0', user_id = 'operator', path = 'reports')And if you want the docx test documents in a certain directory then add a path field. (e.g. path = 'reports')Sample template is included in tests; template.docx
Package: openhtf-plugin
openhtf-plugin PackageThis is a simple Kiwi-TCMS automation plugin for OpenHTF and Spintop-OpenHTFFor more about Kiwi-TCMS please visithttps://kiwitcms.org/Fore more about OpenHTF and Spintop-OpenHTF please visithttps://github.com/google/openhtfhttps://gitlab.com/tackv/spintop-openhtfRequires MIT Kerberos for Windows if installed on Windows
Package: openhub-django
Integrate openhub APIs with DjangoDocumentationThe full documentation is athttps://openhub-django.readthedocs.io.QuickstartInstall openhub-django:pipinstallopenhub-djangoAdd it to yourINSTALLED_APPS:INSTALLED_APPS=(...'openhub_django.apps.OpenhubDjangoConfig',...)Add openhub-django’s URL patterns:urlpatterns=[...url(r'^',include('openhub_django.urls')),...]Configure following environment variables:OpenHub TokenHow to get OpenHub Token?Signup with GitHub onOpenHubGo to your profile settings to create a new API Key.Choose “API Keys” from the section and create a new one by clicking on button “Request New API Key”Fill in the required fields and Save it.Copy theAPI Keyfor the generated token to add it to your environment.Add it to your Linux (or Ubuntu) environment-Open the terminal and runcat ~/.bashrc.Find this peice of codeif[-f~/.bash_aliases];then.~/.bash_aliasesfiIn this case we will create a new file in$HOMEdirectory named.bash_aliasesto store our environment variables. As it will execute
everytime we open a new terminal window.vi~/.bash_aliasesEnter into the insert mode and add the value of token that you generated from OpenHub settings.exportOH_TOKEN=<PASTETHECOPIEDAPIKEYOFTOKEN>Exit the insert mode by pressingesckey and the editor.Restart your terminal and runprintenvto verify the API Key added.Organization nameSet the environment variableORG_NAMEfollowing the above mentioned steps like you
did adding the environment variableOH_TOKEN. Once you setup these two environment
variables, you’re ready to run a management command to fetch the organization related
information from OpenHub.Run management commandpythonmanage.pymigratepythonmanage.pyimport_openhub_dataView the fetched data:pythonmanage.pyrunserver# Open http://127.0.0.1:8000/openhub/ in browserFeaturesImport all the organizationportfolio projectsdataImport all the organizationoutside projectsdata such as dependenciesImport all theaffiliated committersdata who made contributions to the projectsImport all theoutside committersdataImport theorganizationrelated informationGenerate the static web-pages of all the imported data with an interactive UI/UX design.Running TestsDoes the code actually work?source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install tox
(myenv) $ toxCreditsTools used in rendering this package:Cookiecuttercookiecutter-djangopackageHistory0.1.0 (2018-08-14)First release on PyPI.
Package: openhuman
OpenHuman.ai - Relastic virtual human assistantHomepage:http://OpenHuman.ai/Github:https://github.com/OpenHuman-ai/python-openhumanOrgs:https://github.com/OpenHuman-aiInstallation StepsCreate a setup.py file for your package, which includes information about your package such as its name, version, author, and dependencies.Create a source distribution and a wheel distribution of your package by running the following commands in your terminal:python3setup.pysdistbdist_wheelCreate an account on PyPI if you haven't already done so. You can create an account by going tohttps://pypi.org/account/register/and filling out the form.Install the twine package, which is used to upload your package to PyPI:pip install twineUpload your package to PyPI using twine:twine upload dist/*Your package is now published on PyPI! Users can install it using pip install mypackage.
Package: open-humans-downloader
Installation$pipinstall--upgradeopen-humans-downloaderUsageUsage: oh-download [OPTIONS]

  Download data from Open Humans.

Options:
  -s, --source TEXT     the source to download files from
  -u, --username TEXT   the user to download files from
  -d, --directory TEXT  the directory for downloaded files
  -m, --max-size TEXT   the maximum file size to download
  --help                Show this message and exit.Examples# download all 23andMe files to 23andme/
$ mkdir 23andme
$ oh-download --source twenty_three_and_me --directory 23andme
# download all of beau's files to the current directory
$ oh-download --username beau
Package: openhunt
OpenHuntA python library used to expedite data analysis via statistical functions and visualizations.RequirementsPython 3+InstallationYou can install it via PIP:pip install openhuntOr you can also do the following:git clone https://github.com/hunters-forge/openhunt
cd openhunt
pip installAuthorJose Luis Rodriguez@Cyb3rPandaHContributorsRoberto Rodriguez@Cyb3rWard0g
Package: openi
OpenIPYPI package for 启智 AI 协作平台。安装适配 python3.6 及以上版本pipinstallopeni功能介绍提供命令行与代码使用具体请参考帮助文档-API参考fromopeni.datasetimportdownload_filedownload_file(file="my_data.zip",repo_id="user1/repo1",cluster="gpu",save_path="local_path/",)""" outputComplete( my_data.zip)(gpu): 100%|██████████████████████████████████████████| 22.0MB/22.0MB [00:01<00:00, 15.9MB/s]""">>>openi
usage:openi{login,whoami,dataset,...}[<args>][-h]OpenIcommandlinetool启智AI协作平台命令行工具

commands:{login,logout,whoami,dataset,d,model,m}login使用令牌登录启智并保存到本机logout登出当前用户并删除本地令牌文件whoami查询当前登录用户dataset(d){upload,download}上传/下载启智AI协作平台的数据集model(m){upload,download}上传/下载启智AI协作平台的模型>>>openilogin██████╗██████╗███████╗███╗██╗██████╗██╔═══██╗██╔══██╗██╔════╝████╗██║██╔═╝██║██║██████╔╝█████╗██╔██╗██║██║██║██║██╔═══╝██╔══╝██║╚██╗██║██║╚██████╔╝██║███████╗██║╚████║██████╗╚═════╝╚═╝╚══════╝╚═╝╚═══╝╚═════╝


点击链接获取令牌并复制粘贴到下列输入栏https://openi.pcl.ac.cn/user/settings/applications[WARNING]若本机已存在登录令牌，本次输入的令牌会将其覆盖粘贴前请先按退格键⇦删除多余空格🔒token:
Package: openi-beta
OpenIPYPI package for 启智 AI 协作平台。安装适配 python3.6 及以上版本pipinstallopeni功能介绍提供命令行与代码使用具体请参考帮助文档-API参考fromopeni.datasetimportdownload_filedownload_file(file="my_data.zip",repo_id="user1/repo1",cluster="gpu",save_path="local_path/",)""" outputComplete( my_data.zip)(gpu): 100%|██████████████████████████████████████████| 22.0MB/22.0MB [00:01<00:00, 15.9MB/s]""">>>openi
usage:openi{login,whoami,dataset,...}[<args>][-h]OpenIcommandlinetool启智AI协作平台命令行工具

commands:{login,logout,whoami,dataset,d,model,m}login使用令牌登录启智并保存到本机logout登出当前用户并删除本地令牌文件whoami查询当前登录用户dataset(d){upload,download}上传/下载启智AI协作平台的数据集model(m){upload,download}上传/下载启智AI协作平台的模型>>>openilogin██████╗██████╗███████╗███╗██╗██████╗██╔═══██╗██╔══██╗██╔════╝████╗██║██╔═╝██║██║██████╔╝█████╗██╔██╗██║██║██║██║██╔═══╝██╔══╝██║╚██╗██║██║╚██████╔╝██║███████╗██║╚████║██████╗╚═════╝╚═╝╚══════╝╚═╝╚═══╝╚═════╝


点击链接获取令牌并复制粘贴到下列输入栏https://openi.pcl.ac.cn/user/settings/applications[WARNING]若本机已存在登录令牌，本次输入的令牌会将其覆盖粘贴前请先按退格键⇦删除多余空格🔒token:
Package: openicl
Overview•Installation•Paper•Examples•Docs•CitationOverviewOpenICL provides an easy interface for in-context learning, with many state-of-the-art retrieval and inference methods built in to facilitate systematic comparison of LMs and fast research prototyping. Users can easily incorporate different retrieval and inference methods, as well as different prompt instructions into their workflow.What's Newsv0.1.8Support LLaMA and self-consistencyInstallationNote: OpenICL requires Python 3.8+Using Pippip install openiclInstallation for local development:git clone https://github.com/Shark-NLP/OpenICL
cd OpenICL
pip install -e .Quick StartFollowing example shows you how to perform ICL on sentiment classification dataset.  More examples and tutorials can be found atexamplesStep 1: Load and prepare datafromdatasetsimportload_datasetfromopeniclimportDatasetReader# Loading dataset from huggingfacedataset=load_dataset('gpt3mix/sst2')# Define a DatasetReader, with specified column names where input and output are stored.data=DatasetReader(dataset,input_columns=['text'],output_column='label')Step 2: Define the prompt template (Optional)fromopeniclimportPromptTemplatetp_dict={0:"</E>Positive Movie Review: </text>",1:"</E>Negative Movie Review: </text>"}template=PromptTemplate(tp_dict,{'text':'</text>'},ice_token='</E>')The placeholder</E>and</text>will be replaced by in-context examples and testing input, respectively. For more detailed information aboutPromptTemplate(such as string-type template) , please seetutorial1.Step 3: Initialize the RetrieverfromopeniclimportTopkRetriever# Define a retriever using the previous `DataLoader`.# `ice_num` stands for the number of data in in-context examples.retriever=TopkRetriever(data,ice_num=8)Here we use the popularTopKmethod to build the retriever.Step 4: Initialize the InferencerfromopeniclimportPPLInferencerinferencer=PPLInferencer(model_name='distilgpt2')Step 5: Inference and scoringfromopeniclimportAccEvaluator# the inferencer requires retriever to collect in-context examples, as well as a template to wrap up these examples.predictions=inferencer.inference(retriever,ice_template=template)# compute accuracy for the predictionscore=AccEvaluator().score(predictions=predictions,references=data.references)print(score)Docs(updating...)OpenICL DocumentationCitationIf you find this repository helpful, feel free to cite our paper:@article{wu2023openicl,title={OpenICL: An Open-Source Framework for In-context Learning},author={Zhenyu Wu, Yaoxiang Wang, Jiacheng Ye, Jiangtao Feng, Jingjing Xu, Yu Qiao, Zhiyong Wu},journal={arXiv preprint arXiv:2303.02913},year={2023}}
Package: openid2rp
This library helps writing OpenID applications (a.k.a relying
parties). See thedocumentationfor further usage information.
Bug reports should be sent to thebugtracker. The source code
is maintained onbitbucket.The main library is entire web-framework agnostic, and requires
the application to store any persistent data needed for the relying
party.The package also includes a Django authentication module,
which stores all necessary data; providing a user interface for
logging in is still left to the application.Furthermore, a WSGI middleware is also included which simplifies
integrating OpenID into WSGI-based web frameworks. Again, all UI
aspects are left to the application.Changes1.12 (2012-02-24):Fix bug in Django plugin1.11 (2011-11-25):Implement direct verification (Jack Bates)Wrap verification in verify function; deprecate authenticate()Introduce symbolic error codes1.10 (2011-09-01):Fix Python 3 incompatibilitiesFix OpenID 1.1 incompatibilities in Django moduleFix Django 1.3 warnings in Django module1.9 (2011-06-19):Support XRI resolution1.8 (2011-04-01):Fix Django session management1.7 (2010-09-17):Add WSGI middleware1.6 (2010-07-29):Updated Django backend: session + nonce persistency, documentation, many bug fixes1.5 (2010-07-27):Make openid2rp a package, move testapp into that package.Adding Django authentication backend (openid2rp.django)1.4 (2010-07-20):Fix AX requests.1.3 (2010-07-16):Add get_sreg. Deprecate get_username.Specify that response parameters are always literal QUERY_STRINGs;
parse_qs results are still supported for compatibility.Split test application into separate file testapp.pyDrop BeautifulSoup dependency, drop setuptools usageAllow applications to request specific SREG and AX values1.2 (2010-07-02):port to Python 31.1.1 (2010-06-30):Fix typo in DH usageAdd documentation to source distribution1.1 (2010-06-30):Drop reliance on M2CryptoAdd dependencies to setup.pySwitch to Sphinx1.0 (2010-06-29):Initial release