Saravanan666/pypi-org huggingface dataset:
Package: intelliprove
IntelliProve Python SDKRequirementsPython ^3.9Installationpip install intelliproveDocumentationYou can find our SDK documentationhere.Release Notes1.21.2New features​AddedUnprocessableVideoResponsemodel andVideoProcessingFailedExceptionexception:The new modelUnprocessableVideoResponseprovides extra information about why a certain video did not successfully process.This new information is returned inside the newVideoProcessingFailedExceptionexception, which gets throw from theget_resultsmethod.
​UpdatesRefactor ofQualitydataclass:The following properties for theQualitymodel have been renamed:message=>prompt​Deprecated featuresHow to UpgradeEnsure you are using the new propertypromptfrom theQualitymodelEnsure you catch the newVideoProcessingFailedExceptionwhen calling theget_resultsmethod. Additionally you can read out theresponseproperty of this exception to provide feedback to the end-user.Example usageimportosfrompathlibimportPathfromintelliprove.apiimportIntelliproveApi,IntelliproveApiSettings,Biomarkers,Quality# define api key and settingsapikey=os.environ.get('apikey')settings=IntelliproveApiSettings(base_url='')# init apiapi=IntelliproveApi(apikey,settings)# define the path of the video you want to uploadvideo_path=Path('./mydir/example.mp4')image_path=Path('./mydir/example.png')# manually check quality of a video of imagequality:Quality=api.check_conditions(video_path)quality:Quality=api.check_conditions(image_path)# Optional: define the performer and patientperformer:str='performer-a'patient:str='patient-1'# upload video to IntelliProve# performer and patient are optionaluuid:str=api.upload(video_path)# oruuid:str=api.upload(video_path,performer=performer,patient=patient)# get the results of the uploaded videoresults:Biomarkers=api.get_results(uuid)DataclassesBiomarkerscontains the results of an uploaded videoQualitycontains the quality parameters of a video or imageExceptionsImageQualityExceptionMediaExceptionInvalidUuidExceptionVideoProcessingFailedExceptionApiExceptionApiNotFoundExceptionApiForbiddenExceptionApiErrorExceptionApiResultNotAvailable
Package: intellipush
intellipushA client for communicating with the intellipush API service for sending SMSes.pip install intellipushSending an SMS:from intellipush import client
intellipush = client.Intellipush(key=api_id, secret=api_secret)

result = intellipush.sms(
    countrycode='0047',
    phonenumber=phonenumber,
    message='test from intellipush',
)See the tests for current examples of how to perform most tasks available through the API.Running the testsThe tests are run withpytest, and accepts--api-idand--api-secretto set
proper API access keys for testing against the live interface. Do not do this with accounts
you care about.If not set, only the tests that do not depend on a live endpoint will be run.pytest tests/test_client.py --api-id=apiid --api-secret=secretTests that depend on a live phone numberCertain tests against the live system actually do stuff that ends up with a live
message going out to a phone. Since we don't want this to happen without proper
cause, you'll have to provide two additional settings if you want live messages
to be sent.--live-phone-number <phonenumber> --live-country-code <countrycode>Example:--live-phone-number 88888888 --live-country-code 0047The methods do have mocked versions of their tests attached, but to make sure that
the tests actually work against the server side, this allows you to run those tests
as well if necessary.Releasing a new version of the libraryCreate the archives:python setup.py sdist bdist_wheelUpload the packages to pypi:
Package: intelli-sms-gateway
OverviewInstallationAuthenticateAssign CSV file path to variable CSV for a bulk smsCall either method for sending bulk or single SMSs1. Installationpipinstallintelli-sms-gateway# For Windowspip3installintelli-sms-gateway# For LinuxShould dependencies fail to automatically installpipinstallrequests&&pipinstallpandas#ORpip3installrequests&&pip3installpandas2. AuthenticationInstantiate Client class byt passing the parameters email and password in that orderfromintelli_sms_gateway.clientimportClient# Import the client class from the client moduleclient=Client('foo@foo.com','password')# This line of code with Authenticate you. Should you not be authenticated, it will raise an exceptionShould the client be authenticated, this instantiation will return a property of value True. The reverse is true.3. Getting Details On a Bulk SMS from a CSVDefine a variable, assign to it the path of the csv file you want to useMake sure the first column in the CSV is named 'numbers' or the program will return errors. Phone numbers must be in the format 2637775342244. Sending the messagesBulk SMSesCall the single SMS method of the client instance and pass the required parameters namely: message, csv, titleThe CSV that is being passed is the variable that contains the path to the CSV. Should the path be incorrect, errors will be raised# We send a bulk SMS after authenticationclient.send_bulk_sms('Please attend the meeting at 1pm',csv_variable,'Meeting announcament')Single SMSesCall the single SMS method of the client instance and pass the required parameters namely: message, receiver, titlePhone numbers must be in the format 263777534224# We send a single SMS after authenticationclient.send_single_sms('Please attend the meeting at 1pm','263777534224','Meeting announcament')If your credentials are correct, your messages will not be sent
Package: intelliterm
IntellitermChat with AI from your terminal!NoteThis is a side project I occasionally work on in my free time,reportany bugs and/or desired features.Contributions are welcome!Features🧠 GPT-4— Chat with any ofOpenAI's language models!👾 Code Execution— Copy and run code [^1]⚙️ Configurations— Quickly switch between models (mid-chat!) and manage multiple configurations📂 File Input— Dump files into prompts!💬 Chat Manager— Create, save [^2] and load chats!!Command Palette— Do useful things with a variety ofbuilt-in commands!✍️ Auto-completion— Auto-complete commands withtab(and navigate history with↑/↓)InstallpipinstallintellitermHow to useNoteMake sure you've set yourOPENAI_API_KEYenvironment variable in~/.zshrcor~/.bashrc:exportOPENAI_API_KEY='YOUR-API-KEY'Basic usage:ai# or with a promptaiwriteapythonprogramthatprintsarandomchesspositionusingemojisInput a file:ai-ffile.py# -f or --file# or via pipescatfile.py|ai
gitdiff|aiNotePiping agit diffis aspecial casefor which Intelliterm one-shot generates a commit message inconventional format, summarizing the diff (for better UX).OptionsShortLongDescription-f--filePass a file as prompt-m--mini--oneshotComplete prompt without entering the Intelliterm CLI ("one-shot" usage)-c--copyAuto-copy entire response to clipboard-cc--copy-codeAuto-copy code block to clipboard-h--helpShow help message (this one)-v--versionShow Intelliterm version!Command PaletteNoteYou must be in a chat to useCommand Palette(start a chat viaaiorai <prompt>)Intelliterm comes with a set of handy commands, triggered by entering!followed by:Command AliasesCommand OptionsCommand DescriptionGeneral!help!hShow available commands!quit!qorCtrl+cQuit Intelliterm!shell!osRun basic shell commands within Intellitermusage:!shell <command>example:> !shell lsConfiguration!cfg!use!config!switcheditresetManage Intelliterm configurations!cfg— Show active config!cfg edit— Edit configs file!use <name>— Switch to a config (case-insensitive)example:!use gpt4!cfg reset— Reset configs file to defaults (GPT3andGPT4, defaulting toGPT3)Chat!new!nStart new chat / clear context!file!fInput a file as promptusage:!file <path> <prompt>example:> !file file.py optimize this code!info!iShow information about current chat!save!sSave chat (to:<DOCUMENTS_DIR>/intelliterm/chats!load!lLoad chatResponse!copy!ccodeCopy last response to clipboard!copy/!c— Copy all content!copy code/!c code— Copy code from contentNote:to auto-copy every new response:Quit Intelliterm (!quit/!q/Ctrl+c)Relaunch with:ai <prompt> --copyorai <prompt> --copy codeTip:-c(short alias)!run!rRun code block in last responseNote:currently supports:PythonJavaScriptTypeScriptContributingPull requests, suggestions and issue reports arevery welcome👽[^1]: Running generated code currently supported forPython,JavaScriptandTypeScriptcode snippets.
[^2]: Intelliterm usesplatformdirsto determine the file paths where configurations and chats are saved to and loaded from.CONFIG_DIRandDOCUMENTS_DIRdirectory locations will thus vary based on your OS (Intelliterm displays them when saving/loading things).
Package: intelliw
智能工场算法框架（Intelligent Workshop Algorithm Framework）欢迎使用用友AI工作坊脚手架，该脚手架提供了本地开发、调试、打包等基本功能，供开发者在本地开发调试。环境要求脚手架依赖 python >= 3.6，使用
pip进行包管理。开发者在本地调试时，需保证安装配置了正确的 python 环境。脚手架使用环境准备安装框架pip install intelliw -i https://pypi.python.org/simple/ -U初始化算法文件
生成算法文件框架，参数name为算法名称，默认值example,
参数output_path为生成算法文件的位置，默认当前文件下。intelliw init --name test --output_path=/root/workspace/test/

// 初始化之后生成以下结构 
      ├── algorithm.py          算法入口文件
      ├── algorithm.yaml        算法描述文件
      ├── README.md             项目描述文件
      ├── debug_controller.py    本地开发测试文件
      ├── requirements.txt      python 依赖包
      └── docs                  算法框架使用说明
      		├── README.md       框架使用说明
      		└── instructions.md 算法文件说明说明文档
请参考 使用说明(docs/instructions.md)-   algorithm.py   说明文档
-   algorithm.yaml 说明文档注： 以下功能使用框架包必须指定算法文件位置（--path/-p）算法导入intelliw import_alg --path ${algo_path}模型导入intelliw import_model --path ${algo_path}训练本地训练时，训练数据只支持读取本地的 csv 文件，使用--csv参数指定文件路径。训练集与验证集的划分比例使用--train_ratio指定，默认是 0.7， 即训练集占 0.7，验证集占 0.3。以读取/data/export.csv文件为例，给出训练命令如下：intelliw train --path ${algo_path} --csv /data/export.csv --train_ratio 0.5推理本地推理时，算法框架会启动 HTTP 服务器处理推理请求，监听的端口可以使用--port指定，默认是8888。以下示例会启动推理服务，并监听8000端口。intelliw infer --port 8000 --path ${algo_path}推理服务启动后，可以调用推理接口，进行推理。推理接口是一个POST json接口，监听路径是/predict，用户的请求需要封装在data字段中。
以下给出使用curl进行请求的示例命令，假设服务监听的是8000端口，需要传递的数据是[1.2, 2, true, "user"]。curl -H "Content-Type:application/json" -X POST -d '{"data":[1.2, 2, true, "user"]}' http://localhost:8000/predict打包本地调试无误后，可使用脚本进行打包，生成的算法包/模型包路径可以使用--output_path参数指定，若不指定，默认会生成到target目录下。生成算法包:intelliw  package_iwa --path ${algo_path} --output_path xxxx生成模型包:intelliw package_iwm --path ${algo_path} --output_path xxxx
Package: intel-map-client
IntelMapClient一个用于访问 intel.ingress.com 的 API 客户端Feature使用httpx进行异步请求支持使用代理RequirementPython >= 3.7httpx[socks] >= 0.22.0httpx-socks[asyncio] >= 0.7.3Installationpipinstallintel-map-clientUsage下面是获取指定区间内全部 Portals 的简单示例importasynciofromIntelMapClientimportAsyncClient,AsyncAPIfromIntelMapClient.typesimportMapTilescookies="<cookies>"# Put your cookies hereclient=AsyncClient()api=AsyncAPI(client)client.set_cookies(cookies)# Set Cookiesclient.set_proxy(proxy_url="socks5://127.0.0.1:7890")# Set proxy if you needasyncdefmain():lat,lng=23.105252,113.240577map_tiles=MapTiles.from_square(lat,lng,7000,zoom=15)# Build MapTilesasyncwithclient:awaitclient.authorize()tile_set=awaitapi.GetEntitiesByMapTiles(map_tiles)print(list(tile_set.portals()))# Portals Listif__name__=='__main__':asyncio.run(main())更多用法详情可以等待 API 文档更新API ListIntelMap Basic APIgetArtifactPortalsgetGameScoregetEntitiesgetPortalDetailsgetPlextssendPlextgetRegionScoreDetailsredeemRewardgetHasActiveSubscriptiongetTopMissionsInBoundsgetMissionDetailsgetTopMissionsForPortalgetInventorysendInviteEmailswipeAccountHigh-level APISearchPortalByLatLng- 通过经纬度搜索最近的 portalGetEntitiesByMapTiles- 下载 MapTiles 范围内的 GameEntitiesRoadmap该版本是原来分支的重构，更多更新将在以后版本发布LicenseMIT License
Package: intelmo
Quick Start with INTELMOInstallationINTELMO is available on PyPI and can be installed with pip:pipinstallintelmoBasic ConfigurationYou can useintelmo.create_server()to create a server.# example.pyfromintelmoimportcreate_serverif__name__=="__main__":server=create_server(name="Example Server",description="This is an example server.",tasks=[{"name":"Sentiment","description":"Show the sentiment of the text","task_type":"generation",# any function that takes article text and returns a string"task":...}],)server.run()Then you can start the server with:pythonexample.pyA development server will be started athttp://localhost:8080.
Package: intelmq
Welcome to IntelMQ!IntelMQis a solution for IT security teams (CERTs & CSIRTs, SOCs, abuse
departments, etc.) for collecting and processing security feeds (such as
log files) using a message queuing protocol. It’s a community driven
initiative calledIHAP(Incident Handling Automation Project) which
was conceptually designed by European CERTs/CSIRTs during several
InfoSec events. Its main goal is to give to incident responders an easy
way to collect & process threat intelligence thus improving the incident
handling processes of CERTs.IntelMQ can be used for
- automated incident handling
- situational awareness
- automated notifications
- as data collector for other tools
- etc.IntelMQ’s design was influenced byAbuseHelper,
however it was re-written from scratch and aims at:Reducing the complexity of system administrationReducing the complexity of writing new bots for new data feedsReducing the probability of events lost in all process with
persistence functionality (even system crash)Use and improve the existing Data Harmonization OntologyUse JSON format for all messagesProvide easy way to store data into Log Collectors like
ElasticSearch, Splunk, databases (such as PostgreSQL)Provide easy way to create your own black-listsProvide easy communication with other systems via HTTP RESTful APIIt follows the following basic meta-guidelines:Don’t break simplicity - KISSKeep it open source - foreverStrive for perfection while keeping a deadlineReduce complexity/avoid feature bloatEmbrace unit testingCode readability: test with unexperienced programmersCommunicate clearlyGetting HelpIntelMQ’s documentation is available atintelmq.readthedocs.io.For support questions please reach out on thethe intelmq-users mailing listIntelMQ Manager and more toolsSeveral pieces of software evolved around IntelMQ. For example, check outIntelMQ Managerwhich is a web based interface to easily manage an IntelMQ system.More tools can be found in theIntelMQ Universe chapter in the documentation.How to participateIntelMQ is a community project depending on your contributions. Please consider sharing your work.Have a look at ourDevelopers Guidefor documentation.Subscribe to theIntelmq-dev Mailing listto get answers to your development questions:TheGithub issueslists all the open feature requests, bug reports and ideas.Incident Handling Automation ProjectURL:http://www.enisa.europa.eu/activities/cert/support/incident-handling-automationMailing-list:ihap@lists.trusted-introducer.orgLicenceThis software is licensed under GNU Affero General Public License version 3FundingThis project was partially funded by the CEF framework
Package: intelmq-api
intelmq-api is aFastAPIbased API for theintelmqproject.Extensive documentation regarding the installation, configuration and usage of theintelmq-apican be found in theintelmq documentation.Development usageYou could create a preferred virtual environment, and then install the package using:pipinstall-e.For development purposes, you can run the API using thescripts/run_dev.shscript. It serves the
API on the local8000port and watches for file changes, with the automated reloading on change.The interactive documentation is served on the/docsendpoint.To set the API configuration, please export theINTELMQ_API_CONFIGenvironment variable with path
to the JSON config file in the shell you want to use. For the config reference, please check theintelmq_api/config.pyand the example fromcontrib/api-config.json.If you configured the session store, you will need to authorize every request. The/v1/loginreturns the authorization token. You can use the following command to register the user:./scripts/intelmq-api-adduser--userUserNameSecurity notePlease be careful when deploying the API. At the current stage, it is not designed to run on
publicly exposed endpoints without additional pre-cautions.Type checkingThe code can be typechecked withmypy. To run the type checker, use:mypyintelmq_api/
Package: intelmq-webinput-csv
# IntelMQ Webinput CSVA web interface for interactively inserting one-off CSV data into
[IntelMQ](https://intelmq.org/)’spipelines.It is implemented in Python with [hug](https://www.hug.rest/) in the backend
and Javascript with bootstrap-vue in the frontend.
This is a rewrite of the original Flask-based web interface by CERT.at.## Table of Contents1. [Installation](#how-to-install)
1. [User guide](#user-guide)
1. [Development](#development)
1. [Licence](#licence)
## How to InstallTo get the Webinput-CSV up and running, clone the repo and use`bash $ pip3 install . $ hug-fintelmq_webinput_csv/serve.py-p8002 `[//]: <> (TODO: Package installation)
[//]: <> (TODO: Apache integration)For more details see the [Installation guide](./docs/INSTALL.md).## User GuideThe Webinput-CSV can be started with default values and is fully usable (except
of the injection in the IntelMQ pipeline queue). Most
parameters for the input are available in the Frontend and are self explaining.For detailed description of configuration and parameters see the [user guide](./docs/User-Guide.md).## Developmenthug provides an auto-refresh development mode when starting the application
using`bash $ hug-fintelmq_webinput_csv/serve.py-p8002 `Like hug, yarn provides this for the client using`bash $ cd client $ yarn && yarn serve `For detailed developer information and how to develop with docker see [developer guide](./docs/Developers-Guide.md)## LicenceThis software is licensed under GNU Affero General Public License
version 3.
Package: intel-numpy
Optimized implementation ofnumpy, leveraging Intel® Math Kernel Library to achieve highly efficient multi-threading, vectorization, and memory management.It provides:a powerful N-dimensional array objectsophisticated (broadcasting) functionstools for integrating C/C++ and Fortran codeuseful linear algebra, Fourier transform, and random number capabilitiesand much moreBesides its obvious scientific uses, NumPy can also be used as an efficient
multi-dimensional container of generic data. Arbitrary data-types can be
defined. This allows NumPy to seamlessly and speedily integrate with a wide
variety of databases.All NumPy wheels distributed on PyPI are BSD licensed.Install instructionIntel optimized NumPy Pypi packages are now distributed via Anaconda Cloud.To install Intel optimized NumPy Pypi package please use following command:python -m pip install -i https://pypi.anaconda.org/intel/simple numpy
Package: intelowl-click-creds
click-credsPluggable credentials storage and management forclickCLI applications.Uses~/.netrcfile methodwhich is used by popular CLI applications likeHeroku CLI, AWS CLIs, etc.InstallationRequires python version>=3.6.$pipinstallclick-credsQuickstartHere's an examplecli.pyfile.#!/usr/bin/env python3importclickimportclick_creds@click.group(context_settings=dict(help_option_names=["-h","--help"]))@click_creds.use_netrcstore(name="myawesomeapp",mapping={"login":"username","password":"api_key","account":"url"},)defcli():pass# Register "config" groupcli.add_command(click_creds.config_group)# Entrypointif__name__=="__main__":cli()Now, if we execute./cli.py config,$./cli.pyconfig
Usage:cli.pyconfig[OPTIONS]COMMAND[ARGS]...Setorviewconfigvariables

Options:-h,--helpShowthismessageandexit.

Commands:getEchoconfigvariablessetUpdateconfigvariablesDocumentationPlease see theexample_project.Changelog / ReleasesAll releases should be listed in thereleases tab on GitHub.SeeCHANGELOGfor a more detailed listing.LicenseThis project is published with theBSD License. Seehttps://choosealicense.com/licenses/bsd/for more information about what this means.Creditstinynetrc
Package: intel-power-control
intel-power-controlGPU power management for Intel hardware on LinuxFunctionalityDisplays cpu online states, temperatures and gpu clock settings.Toggle cpu online statesThrottling of gpu clock (also automatic)intel-power-control-helper changes settings as root userRequirementsC compiler (i.e. gcc)Qt 6.xpython3Installation from sourcegit clone https://github.com/jmechnich/intel-power-control.git

# compile
cd intel-power-control
make

# install to /usr/local
sudo make install

# OR install to local prefix
make install PREFIX=$HOME/.local
sudo chown root:root $HOME/.local/bin/intel-power-control-helper
sudo chmod 4755 $HOME/.local/bin/intel-power-control-helper

# run program
intel-power-controlInstallation from PyPi# system-wide
sudo pip install intel-power-control
echo "%sudo ALL=(ALL) NOPASSWD: /usr/local/bin/intel-power-control-helper" | \
  sudo tee /etc/sudoers.d/intel-power-control

# OR as user
pip install intel-power-control --user
sudo chown root:root $HOME/.local/bin/intel-power-control-helper
sudo chmod 4755 $HOME/.local/bin/intel-power-control-helper

# run program
intel-power-controlChecking and fixing intel-power-control-helper permissionsThe scriptintel-power-control-setupis supplied to set up the
permissions forintel-power-control-helpercorrectly after
installation.Changing the look and feelUse the-stylecommandline option to select a different
theme. Available themes can be listed by calling the application with
an invalid style argument, e.g.-style help.Screenshots
Package: intel-scipy
Optimized implementation ofscipy, leveraging Intel® Math Kernel Library to achieve highly efficient multi-threading, vectorization, and memory management.SciPy (pronounced "Sigh Pie") is open-source software for mathematics,
science, and engineering. The SciPy library
depends on NumPy, which provides convenient and fast N-dimensional
array manipulation. The SciPy library is built to work with NumPy
arrays, and provides many user-friendly and efficient numerical
routines such as routines for numerical integration and optimization.
Together, they run on all popular operating systems, are quick to
install, and are free of charge.  NumPy and SciPy are easy to use,
but powerful enough to be depended upon by some of the world's
leading scientists and engineers. If you need to manipulate
numbers on a computer and display or publish the results,
give SciPy a try!Install instructionIntel optimized SciPy Pypi packages are now distributed via Anaconda Cloud.To install Intel optimized SciPy Pypi package please use following command:python -m pip install -i https://pypi.anaconda.org/intel/simple scipy
Package: intel-sgx-ra
Intel SGX DCAP Remote Attestation libraryOverviewPython library for Intel SGX DCAP remote attestation.Installation$pipinstallintel-sgx-raUsage$sgx-ra-verify--helpusage: sgx-ra-verify [-h] [--verbose] [--mrenclave MRENCLAVE] [--mrsigner MRSIGNER] {certificate,quote} ...Intel SGX DCAP Quote verificationpositional arguments:{certificate,quote}   sub-command helpcertificate         Remote Attestation from X.509 certificate used for RA-TLSquote               Remote Attestation of a raw SGX quoteoptional arguments:-h, --help            show this help message and exit--verbose             Verbose mode--mrenclave MRENCLAVEExpected MRENCLAVE value in SGX quote--mrsigner MRSIGNER   Expected MRSIGNER value in SGX quote$sgx-ra-utils--helpusage: sgx-ra-utils [-h] [--verbose] {extract} ...Intel SGX DCAP Quote toolspositional arguments:{extract}   sub-command helpextract   Extract Quote from X.509 certificate using RA-TLSoptional arguments:-h, --help  show this help message and exit--verbose   Verbose mode
Package: intel-tensorflow
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.