Saravanan666/pypi-org huggingface dataset:
Package: iam-sarif-report
IAM SARIF ReportValidate your IAM Policies and SCPs with AWS Policy Validator, and convert those results into SARIF documents for reporting.Use MeTo generate findings, iam-sarif-report makes AWS API requests. The AWS Principal you use must be allowed to use theaccess-analyzer:ValidatePolicycommand.{"Effect":"Allow","Action":"access-analyzer:ValidatePolicy","Resource":"*"}GitHub ActionSee theaction.yamlfor detailed usage information.on:[push]jobs:example:permissions:id-token:writesecurity-events:write# When using GitHub Advanced Securityactions:readcontents:readchecks:write# When using SARIF annotatorruns-on:ubuntu-lateststeps:-uses:actions/checkout@v3# setup aws access-uses:aws-actions/configure-aws-credentials@v3with:role-to-assume:arn:aws:iam::111111111111:role/my-github-actions-role-testaws-region:eu-west-1# validate some policies and write a SARIF result file-uses:georgealton/iam-sarif-report@v2with:policies:policies/result:results/iam.sarif# Public repositories and Organizations with GitHub Advanced Security# can upload sarif files using CodeQL-uses:github/codeql-action/upload-sarif@v2with:sarif_file:results# Without GitHub Advanced Security use sarif-annotator-uses:SirYwell/sarif-annotator@v0.2.1with:report-path:results/iam.sarifsource:qodanaLocallypipxruniam-sarif-reporttests/data/policy_checks/policies/*
Package: iam-sdk-python
iam-sdk-pythonGetting StartedAssuming that you have a supported version of Python installed, you can first
set up your environment with:$python-mvenv.venv
...
$..venv/bin/activateThen, you can install boto3 from PyPI with:$python-mpipinstalliam-sdk-pythonUsing the SDKMore examplesdocs/example.mdimportiam_sdkusername="fcd1e1..."password="dd16b1..."client=iam_sdk.client(api_access_key=username,api_secret_key=password,)client.login()
Package: iam-ssh-cli
This tool provide a method to keep the IAM ssh keys and Linux users in sync.How to InstallYou can download the updated release version from pypi repo usingpipor directly from our githubreleases. andunzipthe content.$ python -m pip install iam-ssh-cliRequirementsThe aws-cli package works on Python versions:3.7.x and greater3.8.x and greater3.9.x and greater3.10.x and greater3.11.x and greaterConfigurationBefore using the iam-ssh-cli, you need to configure your AWS credentials.
Package: iamstew-excel-parser
Библиотекаiamstew-excel-parserЧто она делает?Библиотека открывает xls/xlsx файл по ссылке, и возвращает двумерный массива со значениями ячеек.Быстрый гайдfromiamstew_excel_parserimportXlsxParser# если вам нужно парсить xlsx файлfromiamstew_excel_parserimportXlsParser# если вам нужно парсить xls файл...path="/your/absolute/path/to/excel/file"excel_parser.open_book(file_path=path)result=excel_parser.parse_sheet(sheet_name=sheet_name,# передаем название листа книги, если не указан, будет парсить 1 лист книгиrow_start=row_start,# передаем номер строки с которой начинать парсинг, если не указан, то начнется с 1 строкиcolumns_letters=columns_letters# передаем массив со строковыми буквами колонок, например ["a","b","ac"])print(result)# пример выходных данных{"Название листа":[["Имя","Фамилия","Отчество"],["Иван","Иванов","Иванович"],["Петр","Петров","Петрович"],["Алексей","Алексеев","Алексеевич"]]}# если ячейка пустая - возвращает None[["Иван","Иванов",None]]
Package: iamsystem
iamsystemA python implementation of IAMsystem algorithm, a fast dictionary-based approach for semantic annotation, a.k.a entity linking.InstallationpipinstalliamsystemUsageYou provide a list of keywords you want to detect in a document,
you can add and combine abbreviations, normalization methods (lemmatization, stemming) and approximate string matching algorithms,
IAMsystem algorithm performs the semantic annotation.See thedocumentationfor the configuration details.Quick examplefromiamsystemimportMatchermatcher=Matcher.build(keywords=["North America","South America"],stopwords=["and"],abbreviations=[("amer","America")],spellwise=[dict(measure="Levenshtein",max_distance=1)],w=2,)annots=matcher.annot_text(text="Northh and south Amer.")forannotinannots:print(annot)# Northh Amer	0 6;17 21	North America# south Amer	11 21	South AmericaAlgorithmThe algorithm was developed in the context of aPhD thesis.
It proposes a solution to quickly annotate documents using a large dictionary (> 300K keywords) and fuzzy matching algorithms.
No string distance algorithm is implemented in this package, it imports and leverages external libraries likespellwise,pysimstringandnltk.
Its algorithmic complexity isO(n(log(m)))with n the number of tokens in a document and m the size of the dictionary.
The formalization of the algorithm is available in thispaper.The algorithm was initially developed in Java (https://github.com/scossin/IAMsystem).
It has participated in several semantic annotation competitions in the medical field where it has obtained satisfactory results,
for example by obtaining the best results in theCodiesp shared task.
A dictionary-based model can achieve close performance to a transformer-based model when the task is simple or when the training set is small.
Its main advantage is its speed, which allows a baseline to be generated quickly.Citation@article{cossin_iam_2018,
	title = {{IAM} at {CLEF} {eHealth} 2018: {Concept} {Annotation} and {Coding} in {French} {Death} {Certificates}},
	shorttitle = {{IAM} at {CLEF} {eHealth} 2018},
	url = {http://arxiv.org/abs/1807.03674},
	urldate = {2018-07-11},
	journal = {arXiv:1807.03674 [cs]},
	author = {Cossin, Sébastien and Jouhet, Vianney and Mougin, Fleur and Diallo, Gayo and Thiessard, Frantz},
	month = jul,
	year = {2018},
	note = {arXiv: 1807.03674},
	keywords = {Computer Science - Computation and Language},
}
Package: iamtokenizing
Tokenization for language processingThis package contains some basic tools allowing to cut a string in sub-parts (cf.Wikipedia), calledToken.iamtokenizingclasses allow basic tokenization of text, such asword splitting, n-gram splitting, (usingNGramsclass)char-gram splitting of arbitrary size (usingCharGramsclass).NGramsalso accepts any REGular EXpression (REGEX) to match pattern that will serve as splitting string. The classRegexDetectoralso allows to extract the REGEX pattern as token. In addition,ContextDetectorallow to split text on some REGEX, and to detect inside these splits an other REGEX, keeping some organisation (called context) of the text between the two detection and splitting scales.InstallationThe documentation is available onhttps://nlp.frama.io/iamtokenizing/The PyPi package is available onhttps://pypi.org/project/iamtokenizing/The official repository is onhttps://framagit.org/nlp/iamtokenizingFrom Python Package Index (PIP)Simply runpipinstalliamtokenizingis sufficient.From the repositoryThe official repository is onhttps://framagit.org/nlp/iamtokenizingOnce the repository has been downloaded (or cloned), one can install this package usingpip:gitclonehttps://framagit.org/nlp/iamtokenizing.gitcdiamtokenizing/
pipinstall.Once installed, one can run some tests usingcdtests/
python3-munittest-v(verbosity-vis an option).Basic examplesBasic examples can be found in thedocumentation.VersionsVersions before 0.4 only present theTokenandTokensclasses. They have been splitted after in three classes, namedSpan,TokenandTokens. Importantly, the methodsToken.appendandToken.removeno longer exist in the next version. They have been replaced byToken.append_range,Token.append_ranges,Token.remove_rangeandToken.remove_ranges.Version 0.4 add the classSpantoTokenandTokens.Spanhandles the sub-parts splitting of a given string, whereasTokenandTokensnow consumesSpanobjects and handle the attributes of theToken.From version 0.5, one has split the basic toolsSpan,TokenandTokensfrom theiamtokenizingpackage (seehttps://pypi.org/project/iamtokenizing/). Only the advanced tokenizer are now present in the packageiamtokenizing, which depends on the packagetokenspan. The objectsSpan,TokenandTokenscan be called as before from the newly deployed packagetokenspan, available onhttps://pypi.org/project/tokenspan/.About usPackage developped for Natural Language Processing at IAM : Unité d'Informatique et d'Archivistique Médicale, Service d'Informatique Médicale, Pôle de Santé Publique, Centre Hospitalo-Universitaire (CHU) de Bordeaux, France.You are kindly encouraged to flag any trouble, and to propose ameliorations and/or suggestions to the authors, via issue or merge requests.Last version : August 6, 2021
Package: iam-tools
IAM_toolsThe IAM_tools is command line utility to query data fromAMPERE Scenario database.
Input data are comma separated value [csv] file.For testing purpose there is small test file following IAM structure:
tests/iam_test.csvInstallationIAM_tools can be installed directly from gitlab repository:pip install iam_tools --upgradeUsageThe IAM_tools can be used to filter, query and export data from IAM.
The command line tool isfilter_IAMThe filter useregexpwhen selecting values from columns.
The columns to filter are: mode, region, scenario, variable.
The utility can be used to show unique values of this columns.
Selection can be exported to csv, mat and h5 output file.
If the output arg is omitted the result will be printed to console.ExamplesRun help:filter_IAM-hIAM_tools is designed to work with the AMPERE project model output.
To follow the examples described below,
you can use the small subsample (with modified numbers) used for testing.You can downlowd this file here.Viewing unique entries for a specific indexPrint all unique models:filter_IAMiam_input.csv-uniquemodelPrint to console unique regions of rows where model is GCAM:filter_IAMiam_input.csv-model"GCAM"-uniqueregionSelecting rows based on the indexPrint to console all rows where variables starts with "Capacity|Electricity|":filter_IAMiam_input.csv-variable"^Capacity\|Electricity\|.*"Note, that the vertical bar '|' has a special meaning in regex (as the or symbol). Thus to match
the literal character '|' you have to escape it with a backslash ('\').Export all rows where variable column start with Secondary Energy|Electricity|
to my_output.mat [cell array] file:filter_IAMiam_input.csv-variable"^Secondary Energy\|Electricity\|.*"-outputmy_output.matExport all rows where scenario column contains 450- and -OPT  to my_output.csv file:filter_IAMiam_input.csv-scenario"450-.*.-OPT"-outputmy_output.csvPrint to console all rows where models are either GCAM or REMIND:filter_IAMiam_input.csv-model"GCAM|REMIND"Export all rows where variable column start with Emissions|CO2 to my_output.csv file:filter_IAMiam_input.csv-variable"^Emissions\|CO2.*"-outputmy_output.csvContributingIf you find a bug or have suggestions for improvement please use theIssue Trackerfor communication.
Development follows a gitlab centric workflow, thus if you want to contribute fork the repository
and file a merge request when you are finished.AuthorsRadek LonkaKonstantin StadlerLicenseBSD 3-Clause LicenseCopyright (c) 2018,Industrial Ecology Digital Labat NTNU
Package: iam-to-sqlite
iam-to-sqliteLoad Amazon IAM data into a SQLite databaseInstallationInstall this tool usingpip:$ pip install iam-to-sqliteYou will also need theawscommand-line tool. Here arethe installation instructionsfor that.UsageRun this tool like so:iam-to-sqlite iam.dbThis will create a SQLite database callediam.dbcontaining the following tables:GroupsPoliciesUsersRolesDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd iam-to-sqlite
python -mvenv venv
source venv/bin/activateOr if you are usingpipenv:pipenv shellNow install the dependencies and tests:pip install -e '.[test]'To run the tests:pytest
Package: iamzero
iamzero-pythonPython client library forIAM Zero🚀 Get Started|📖 SupportThis package makes it easy to create least-privilege IAM policies for your Python scripts or applications.What is IAM Zero?IAM Zero detects identity and access management issues and automatically suggests least-privilege policies. It does this by capturing errors in applications you build or commands that you run which use. By detecting the error and matching it against our Access Advisory lists IAM Zero can instantly provide a least-privilege policy recommendation, customised to your cloud environment.IAM Zero currently works for AWS but our roadmap includes other cloud platforms like GCP, Azure, and Kubernetes.DocumentationGet started byreading our documentation.ContributingSeeCONTRIBUTING.mdfor information on how to contribute. We welcome all contributors -join our Slackto discuss the project!SecurityYou can view our full security documentation on theIAM Zero website.
Package: ianalyzer-readers
I-analyzer Readersianalyzer-readersis a python module to extract data from XML, HTML, CSV or XLSX files.This module was originally created forI-analyzer, a web application that extracts data from a variety of datasets, indexes them and presents a search interface. To do this, we wanted a way to extract data from source files without having to write a new script "from scratch" for each dataset, and an API that would work the same regardless of the source file type.The basic usage is that you will use the utilities in this package to create a "reader" class. You specify what your data looks like, and then call thedocuments()method of the reader to get an iterator of documents - where each document is a flat dictionary of key/value pairs.State of development:this module is currently under development and lacks proper unit tests and documentation.PrerequisitesRequires Python 3.8 or later.Contentsianalyzer_readerscontains the source code for the package.testscontains unit tests.When to use this packageThis package isnota replacement for more general-purpose libraries likecsvor Beautiful Soup - it is a high-level interface on top of those libraries.Our primary use for this package is to pre-process data for I-analyzer, but you may find other uses for it.Using this package makes sense if you want to extract data in the shape that it is designed for (i.e., a list of flat dictionaries).What we find especially useful is that all subclasses ofReaderhave the same interface - regardless of whether they are processing CSV, XML, HTML, or XLSX data. That common interface is crucial in an application that needs to process corpora from different source types, like I-analyzer.UsageUsage documentation is not yet complete.Typical use is that, for each dataset you want to extract, you create a subclass ofReaderand define required properties. See theCSV test corpusfor an example.After defining the class for your dataset, you can call thedocuments()method to get a generator of document dictionaries.LicenceThis code is shared under an MIT licence. SeeLICENSEfor more information.
Package: ianes-bbb-dl
Big Blue Button (BBB) DownloaderDownloads a BBB lesson as MP4 video, including presentation, audio, webcam and screenshare.Nevertheless, I would definitely recommend you to record a BBB meeting withOBS, it's more efficient and you have more control over the outcome.SetupInstallPython>=3.7InstallffmpegRun:pip install bbb-dlas administratorUsageusage: bbb-dl [-h] [-aw] [-aa] [-kt] [-v] [-ncc] [--version] [--encoder ENCODER] [--audiocodec AUDIOCODEC] [-f FILENAME] URL

Big Blue Button Downloader that downloads a BBB lesson as MP4 video

positional arguments:
  URL                   URL of a BBB lesson

optional arguments:
  -h, --help            show this help message and exit
  -aw, --add-webcam     add the webcam video as an overlay to the final video
  -aa, --add-annotations
                        add the annotations of the professor to the final video
  -kt, --keep-tmp-files
                        keep the temporary files after finish
  -v, --verbose         print more verbose debug informations
  -ncc, --no-check-certificate
                        Suppress HTTPS certificate validation
  --version             Print program version and exit
  --encoder ENCODER     Optional encoder to pass to ffmpeg (default libx264)
  --audiocodec AUDIOCODEC
                        Optional audiocodec to pass to ffmpeg (default copy the codec from the original source)
  -f FILENAME, --filename FILENAME
                        Optional output filenameLicenseThis project is licensed under the terms of theGNU General Public License v2.0. For further information, please lookhereorhere(DE).This project is based on the work ofCreateWebinar.com,Stefan WallentowitzandOlivier Berger.
Parts of this code have already been published under MIT license and public domain. These parts are re-released in this project under the GPL-2.0 License.
Package: iann
iann交互式标注软件，暂定名iann。安装按照官网介绍安装paddle。
安装其他依赖pipinstall-rrequirements.txt运行gitclonehttps://github.com/PaddleCV-SIG/iann/cdiann
pythoniann/__main__.pyTODO整理创建网络/推理部分代码，简化main分支下代码不同标签允许不同模型APPNAME全局变量训练代码整理和训练配置的抽出（初步实现）重新审查修改按键图标，确保图标完整清晰界面配色以paddle蓝色为主，同时做一个ico窗口图标标签可修改？include iann/weight/sky_resnet/*
include iann/weight/aorta/*
include iann/weight/human_resnet/*
Package: iaparc-inference
iaparc_inferenceThe IA Parc inference plugin allows developers to easily integrate their inference pipeline into IA Parc's production module.Installationpipinstalliaparc-inferenceUsageIf your inference pipeline support batching:fromiaparc_inferenceimportIAPListener# Define a callback to query your inference pipeline# To load your model only once it is recommended to use a class:classMyModel:def__init__(self,model_path:str):## Load your model in pytorch, tensorflow or any other backenddefbatch_query(batch:list)->list:## execute your pipeline on a batch inputif__name__=='__main__':# Initiate your model classmy_model=MyModel("path/to/my/model")# Initiate IAParc listenerlistener=IAPListener(my_model.batch_query)# Start the listenerlistener.run()If your inference pipelinedo notsupport batching:fromiaparc_inferenceimportIAPListener# Define a callback to query your inference pipeline# To load your model only once it is recommended to use a class:classMyModel:def__init__(self,model_path:str):## Load your model in pytorch, tensorflow or any other backenddefsingle_query(one_input):## execute your pipeline on a single inputif__name__=='__main__':# Initiate your model classmy_model=MyModel("path/to/my/model")# Initiate IAParc listenerlistener=IAPListener(my_model.single_query,batch=1)# Note that batch size is forced to 1 here# Start the listenerlistener.run()FeaturesDynamic batchingAutoscallingSupport both synchronous and asynchronous queriesData agnosticLicenseThis project is licensed under the Apache License Version 2.0  - see the ApacheLICENSEfile for details.
Package: iap-auth
Google IAP authenticationThis module contains a helper class to make requests to an app running behind a Google Identity-Aware Proxy. The code was obtained from the GoogleProgrammatic authenticationdocument.Installpipinstalliap-authUsageWith application default credentialsIf running outside Google Cloud Platform you need to specify env var GOOGLE_APPLICATION_CREDENTIALS to point to your authorized service account.fromiap_authimportIapClientCLIENT_ID='<your-project-client-id>.apps.googleusercontent.com'URL='https://your-iap-protected-website.com.br'METHOD='GET'kwargs={}client=IapClient(CLIENT_ID)resp=client.make_iap_request(URL,method=METHOD,**kwargs)# resp is a requests.Response object.Authenticating a user accountThis way users do not need to have a service account or Google SDK installed. You'll need tocreate an OAuth 2.0 client IDand then use this lib as follows:fromiap_auth.user_clientimportUserAuth,UserIapClientOAUTH_ID="<desktop-app-oauth-id>.googleusercontent.com"OAUTH_SECRET="z6..desktop-app-oauth-secret..Ys1"KEY_PATH="/where/to/store/your/user-credentials.json"IAP_OAUTH_ID='<your-project-iap-client-id>.apps.googleusercontent.com'URL='https://your-iap-protected-website.com.br'user_auth=UserAuth(OAUTH_ID,OAUTH_SECRET,KEY_PATH)client=UserIapClient(user_auth,IAP_OAUTH_ID)resp=client.make_iap_request(URL,method=METHOD)# resp is a requests.Response object.
Package: iapp
:rocket: App :facepunch:Installpipinstalliapp-UUsageRest ApiimportjiebafromiappimportApppred1=lambda**kwargs:kwargs['x']+kwargs['y']pred2=lambdax=1,y=1:x-ypred3=lambdatext='小米是家不错的公司':jieba.lcut(text)app=App(verbose=True)app.add_route("/",pred1,result_key='result')app.add_route("/f1",pred1,version="1")app.add_route("/f2",pred2,version="2")app.add_route("/f3",pred3,version="3")app.run()Schedulerfromiapp.schedulerimportSchedulerdeftask1(x):print(x)importloggingimporttimelogging.warning(f'Task1:{time.ctime()}')deftask2():importloggingimporttimelogging.warning(f'Task2:{time.ctime()}')deftask3():return1/0defmy_listener(event):ifevent.exception:print(event.traceback)print('任务出错了！！！！！！')else:print('任务照常运行...')scheduler=Scheduler()scheduler.add_job(task1,'interval',seconds=3,args=('定时任务',))scheduler.add_job(task2,'interval',seconds=5)scheduler.add_job(task3,'interval',seconds=1)scheduler.add_listener(my_listener)scheduler.start()while1:pass
Package: iapp-ai
iApp AI API pip packageiApp AI API pip packageFree software: MIT licenseDocumentation:https://docs.iapp.co.thRequest API Key:https://ai.iapp.co.thFeaturesiApp Image Recognition ServiceThai National ID Card OCR (Support Card) [NEW]Thai National ID Card OCR (Support Photocopied)[NEW]Passport OCR [NEW]Thai Vehicle License PlateBook Bank OCRFace Verification (Comparison)Face RecognitionFace DetectionFace Passive Liveness DetectionThai Document OCR [NEW]Thai Driver License Card OCR [NEW]Power Meter OCRWater Meter OCRImage Background RemoveriApp Thai NLP ServiceThai Auto Question AnsweringQuestion GeneratorThai-English Machine TranslationThai Text ParserThai Text SummarizationiApp Voice and Speech ServiceThai Automatic Speech Recognition (ASR) [NEW]Thai Text to Speech (TTS)Music Source SeperatorNoise SubtractionRunning Unit TestSlient Test$ pytestVerbose Test$ pytest -sFilter Testpytest -rP tests/test_thai_asr.pyFilter Test with specific methodpytest -rP tests/test_idcard.py::test_idcard_frontCreditsCookiecutteraudreyr/cookiecutter-pypackage=======
History0.1.0 (2022-05-17)First release on PyPI.