Saravanan666/pypi-org huggingface dataset:
Package: tfx-addons
TFX AddonsSIG TFX-Addons is a community-led open source project. As such, the project depends on public contributions, bug fixes, and documentation. This project adheres to theTensorFlow Code of Conduct. By participating, you are expected to uphold this code.MaintainershipThe maintainers of TensorFlow Addons can be found in theCODEOWNERSfile of the repo. If you would
like to maintain something, please feel free to submit a PR. We encourage multiple
owners for all submodules.InstallationTFX Addons is available on PyPI for all OS. To install the latest version,
run the following:pip install tfx-addonsTo ensure you have a compatible version of dependencies for any given project,
you can specify the project name  as an extra requirement during install:pip install tfx-addons[feast_examplegen,schema_curation]To use TFX Addons:fromtfximportv1astfximporttfx_addonsastfxa# Then you can easily load projects tfxa.{project_name}. Ex:tfxa.feast_examplegen.FeastExampleGen(...)TFX Addons projectstfxa.feast_examplegentfxa.feature_selectiontfxa.firebase_publishertfxa.huggingface_pushertfxa.message_exit_handlertfxa.mlmd_clienttfxa.model_card_generatortfxa.pandas_transformtfxa.samplingtfxa.schema_curationtfxa.xgboost_evaluatorCheck outproposalsfor a list of existing or upcoming projects proposals for TFX Addons.Tutorials and examplesSeeexamples/for end-to-end examples of various addons.ContributingTFX Addons is a community-led project. Please have a look at our contributing and development guides if you want to contribute to the project:CONTRIBUTING.mdMeeting cadence:We meet bi-weekly on Wednesday. Check out ourMeeting notesand joinsig-tfx-addons@tensorflow.comto get invited to the meeting.Package releasesCheck outRELEASE.mdto learn how TFX Addons is released.Resourcessig-tfx-addons@tensorflow.org– Join our Google grouptfx@tensorflow.org– General TFX mailing listTFX Addons Slack-  joinhereSIG Repository(this repo)SIG Charter
Package: tfyolo
Documentation|Tutorials|Release Notes|中文tfyolo is a YOLO (You only look once) library implemented by TensorFlow2Key Featuresminimal Yolov5 by pure tensorflow2yaml file to configure the modelcustom data trainingmosaic data augmentationlabel encoding by iou or wh ratio of anchorpositive sample augmentmulti-gpu trainingdetailed code commentsfull of drawbacks with huge space to improveTutorialprepare the data$ bash data/scripts/get_voc.sh
$ cd yolo
$ python dataset/prepare_data.pyClone and install requirements$ git clone git@github.com:LongxingTan/Yolov5.git
$ cd Yolov5/
$ pip install -r requirements.txtTrain$ python train.pyInference$ python detect.py
$ python test.pyTrain on custom dataIf you want to train on custom dataset, PLEASE note the input data should like this:image_dir/001.jpg x_min, y_min, x_max, y_max, class_id x_min2, y_min2, x_max2, y_max2, class_id2And maybe new anchor need to be created, don't forget to change the nc(number classes) in yolo-yaml.$ python dataset/create_anchor.pyPerformanceModelSizeAPvalAP50valAP75valcfgweightsYOLOV5s67247.7%52.6%61.4%cfgweightsYOLOV5m67247.7%52.6%61.4%cfgweightsYOLOV5l67247.7%52.6%61.4%cfgweightsYOLOV5x67247.7%52.6%61.4%cfgweightsCitationIf you find tf-yolo project useful in your research, please consider cite:@misc{tfyolo2021,
    title={TFYOLO: yolo series benchmark in tensorflow},
    author={Longxing Tan},
    howpublished = {\url{https://github.com/longxingtan/tfyolo}},
    year={2021}
}
Package: tf-yolov4
tf-yolov4YOLOv4implementation with Tensorflow 2.Installpipinstaltf-yolov4ExamplePredictionimportnumpyasnpimportPIL.Imageimportyolov4# Default: num_classes=80yo=yolov4.YOLOv4(num_classes=80)# Default: weights_path=None# num_classes=80 and weights_path=None: Pre-trained COCO model will be loaded.# num_classes!=80 and weights_path=None: Pre-trained backbone and SPP model will be loaded.# Otherwise: User-defined weight file will be loaded.yo.load_weights(weights_path=None)img=np.array(PIL.Image.open('./data/sf.jpg'))# The image with predicted bounding-boxes is created if `debug=True`boxes,classes,scores=yo.predict(img,debug=True)Load Darknet weightimportyolov4yo=yolov4.YOLOv4(num_classes=10)yo.load_darknet_weights('/path/to/darknet_weight')TODOPredictionLoad Darknet weight filePre-trained modelBasic training function and Loss definitionLabel-smoothed BCE lossc-IoU lossTraining data augmentation
Package: tg263
TG263Basic TG263 implementation. This library could be used as a
structure name filter in your program.Installation$pipinstalltg263Or from current master branch:$pipinstallgit+https://github.com/gacou54/tg263UsageValidating the structure nameimporttg263result=tg263.is_structure_name_allowed('Prostate')# Result is `True`Finding a structureimporttg263result=tg263.find_structure('SpinalCord_PRV12')print(result.description)print(result.anatomic_group)print(result.target_type)print(result.major_category)print(result.minor_category)print(result.fmaid)AcknowledgementsThis work is a basic implementation of theTG263(https://www.aapm.org/pubs/reports/RPT_263.pdf)The allowed structure names (and corresponding information) were taken fromhttps://www.aapm.org/pubs/reports/RPT_263_Supplemental/.The initial allowed structure names were taken from theESAPIXproject (https://github.com/rexcardan/ESAPIX), made byRex Cardan.
The ESAPIX license is included in the LICENSE file.
Package: tg2exe
tg2exe is a "tg-admin" command line utility extensionwhich could help you converting the TurboGears project to anStand Alone Application on windows platform.Advantage--------------The benifit is you can use TurboGears' power to writeOff-line applications with web interface and AJAX.When the application is running, your computer host asa web server (Actually it is).Your user can quickly evaluate your application without installpython or related modules.Install--------------You could use::$ easy_install tg2execommand to install this modulepywin32 is requiredhttp://sourceforge.net/projects/pywin32/Usage----------Once you installed tg2exe, you'll got an 'makexe' command withinthe "tg-admin" command line utility.Enter your project folder, type::tg-admin makexeand tg2exe will produce a dist folder for you.This utility does not make an *.exe, it assembles all thedependencies and runtime libraries into a single directory,so that it's easy to wrap everything into a single setup package.You are free to package 'dist' folder into an installer (such as InnoSetup).Notice that the stand alone file are equivalent to production project,so you need to have a setted prod.cfg. The simple approach is torename the sample-prod.cfg to prod.cfg.Note-----------The owe is its distribution size. The raw dist with quickstartproject is about 12 MB.After a quick remove, 10MB is possible.Current Stage: make it work(next stages are make it better, then make it small)Credit------------tg2exe is based on the script written by Volodymyr Orlenko,he post the original script on TurboGears MailList in Jul 2006.If you have any question, please go to google turbogears group.History------------0.1 2006/11/14initial workable0.2 2006/11/15reduce 70% distribution size (30MB->12MB)
Package: tg2fa-translit
tg2fa_translitA Tajik-to-Persian transliteration model.
It shows a Levenshtein ratio of 0.988.Installationpip install tg2fa_translitDependencynumpytorchAPIfromtg2fa_translitimportconverttg_text='То ғами фардо нахӯрем!'fa_text=convert(text)print(fa_text)'تا غم فردا نخوریم!'# Depending on your setup, the resulting string can be displayed incorrectly.Datasets and Training DetailsFor more details visithttps://github.com/stibiumghost/tajik-persian-transliteration
Package: tg585scripts
Thomson TG585 v7 router administration scripts.tg585scripts v.alpha 2015-12-10 by hexufo.If you still have an old Thomson TG585 v7 router you might
be intrested in these scripts:tg585catlog: cat the log of the router.tg585catlog2html: create a html page with the log.tg585disablewifi: disable wifi until the next reboot.tg585enablewifi: enable wifi until the next reboot.tg585envlist: print the environment variables.tg585externalip: print the external ip.tg585flushlog: delete - flush the log.tg585getuserini: get the user.ini.tg585hostdevices: print all host devices.tg585listassignedservices: list assigned services.tg585listservices: list services.tg585logo: print the logo.tg585reboot: reboot the router.tg585uptime: get the uptime.Pexpect python library is used to connect to the telnet server.
Telnetlib can be used instead of pexpect.Get the latest versions here:https://github.com/hexufo/tg585scriptsYou can find me in various places on the internet:
hexufo @ {wordpress, twitter, github, gmail}WARNING:THESE SCRIPTS ARE INTENDED FOR ADVANCED USERS AND EDUCATIONAL PURPOSES ONLY!
DO NOT USE THEM UNLESS YOU KNOW WHAT YOU ARE DOING, YOU MAY DAMAGE YOUR ROUTER.
Package: tga-data-analysis
PyTGA - Thermogravimetric Analysis in PythonOverviewPyTGA is a Python module designed for conducting and analyzing Thermogravimetric Analysis (TGA) experiments. It offers tools for loading, processing, and analyzing TGA data, focusing on proximate and oxidation analysis, solid distillation analysis, and deconvolution analysis.FeaturesData Loading: Supports loading TGA data from files.Proximate Analysis: Calculates moisture, ash, and volatile matter.Oxidation Analysis: Computes Ti, Tp, Tb, etc.Solid Distillation Analysis: Analyzes solid distillation over time.Deconvolution Analysis: Deconvolutes TGA curves for peak analysis.Plotting and Visualization: Functions for TGA data visualization.ExampleThe example is available in the example folder (with necessary data) and demonstrates how to use the tga_data_analysis package for basic TGA data analysis and visualization.
Package: tgadmin
tgadminA command line tool for managing your projects in theTextGrid repositorywithout TextGridLab.InstallYou may use this with venv or with pipx. With pipx you have the benefit of having the command available  in your shell without further manual venv creation and activation.Installpipx, e.g. on Debian/Ubuntuapt install pipxAnd the this tool frompypi.orgpipx install tgadminUpgrade to a new version withpipx upgrade tgadminIf you do not want to use pipx have a look at the section "Development".UsageExport sessionID (SID)get fromhttps://textgridlab.org/1.0/Shibboleth.sso/Login?target=/1.0/secure/TextGrid-WebAuth.php?authZinstance=textgrid-esx2.gwdg.deand set as env var:export TEXTGRID_SID=your_secret_sid_hereor set with--sidfor every commandGet helptgadminList projectslist your projects:tgadmin listCreate projectif there is no suitable project, create one:tgadmin create lab-import-test-20230605Advanced UsageYou may use the development or the test instance of the TextGrid Server.To use tgadmin with the test instance dotgadmin --server https://test.textgridlab.org listfor the dev system there is a shortcut, you may calltgadmin --dev listDevelopmentclone repogit clone https://gitlab.gwdg.de/dariah-de/textgridrep/tgadmin.git
    cd tgadminand create venvpython3 -m venv venv
    . venv/bin/activate
    pip install --editable .[dev]afterwards tgadmin is in your venv as command and can be executedtgadminBadges
Package: tgalice
tgaliceThis is yet another common Python wrapper for Alice and Salut skills
and bots in Telegram*, VK,
and Facebook Messenger.Currently, it provides:An (almost) unified interface between your bot and Alice/Telegram/Facebook/VK:DialogConnectorA number of simple dialogue constructors:BaseDialogManagerand its flavors, including:a simple FAQ dialog managera simple form-filling dialog managera flexible finite state automaton dialog manageran even more flexible turn-based dialog managerA wrapper for storing dialogue state:BaseStorageand its flavors (specifially,MongoBasedStorage)Yet another wrapper for serving your bot as a Flask applicationThispackagemay be installed withpip install tgalice.A brief how-toTo create your own bot, you need either to write a config for an existing dialog manager,
or to inherit your own dialog manager fromBaseDialogManager.The components oftgalicemay be combined into a working app as follows:importtgaliceclassEchoDialogManager(tgalice.dialog_manager.BaseDialogManager):defrespond(self,ctx:tgalice.dialog.Context):returntgalice.dialog.Response(text=ctx.message_text)connector=tgalice.dialog_connector.DialogConnector(dialog_manager=EchoDialogManager(),storage=tgalice.session_storage.BaseStorage())server=tgalice.flask_server.FlaskServer(connector=connector)if__name__=='__main__':server.parse_args_and_run()Now, if your app is hosted on address{BASE_URL},
then webhooks for Alice, Salut and Facebook will be available,
respectively, at{BASE_URL}/alice/,{BASE_URL}/salut/, and{BASE_URL}/fb/(and you can reconfigure it, if you want).
The webhook for Telegram will be set automatically, if you set theTOKENenvironment variable to the token
given to you by the@BotFather.If you want to test your app locally, you can run it with command line args:--cli- to read and type messages in command line, completely offline--poll- to run a Telegram bot locally, in long polling mode (in some countries, you need a VPN to do this)--ngrok- to run the bot locally, using thengroktool**to create a tunnel from your machine into the internet. This is probably the simplest way to test Alice skills
without deploying them anywhere .Theexamplesdirectory contains more detailed examples
of how to create dialogs and serve the bot.If you have questions, you can ask them in the Telegram chat@tgalice_support.*The Telegram wrapper is based on thepyTelegramBotAPIpackage.**The ngrok connector was taken from theflask-ngroklibrary. It will be refactored to a dependency,
as soon as the library is updated on PyPI.
Package: tg-api
Tg APIБиблиотека Tg API упрощает работу с веб-API Telegram. Она предоставляет тонкую обёртку над веб API Telegram и библиотекойHTTPX. Библиотека Tg API добавляет к обычным возможностям HTTPX свои схемы данных и удобные часто используемые функции, но не мешает, при необходимости, спускаться ниже на уровень HTTP-запросов.Пример отправки пользователю текстового сообщения:fromtg_apiimportSyncTgClient,SendMessageRequestwithSyncTgClient.setup(token):tg_request=SendMessageRequest(chat_id=tg_chat_id,text='Message proofs high level usage.')tg_request.send()Ключевые возможности библиотекиКлючевые возможности библиотеки Tg API:Поддержка синхронных и асинхронных запросов к APIShortcuts для часто используемых запросовЛёгкий доступ к боту из любого места в кодеНаглядные схемы данных для всех типов запросов и ответов APIАннотация типов для удобства работы с IDEПростое низкоуровневое API для кастомизации запросов к APIНабор инструментов для удобной работы с исключениямиКак установитьБиблиотека доступна на PyPI:$python-mpipinstalltg-apiДокументацияДокументация доступна на Read the Docs:https://tg-api.readthedocs.io/en/latest/.Разработчикам библиотеки Tg APIИнструкции и справочная информация для разработчиков библиотеки Tg API собраны в отдельном документеCONTRIBUTING.md.
Package: tg-api-autoreg
tg-api-autoregDeveloped by:Alexand MacheckMy telegramPythonmodule for automatic registrationTelegram User APIpip install tg-api-autoregExamples:Short:phone='+123456789'proxy='127.0.0.1:8888'session=Reigister_api(phone,proxy,send_code=True)# initializingdata=session.get_api(input('code: '))print(data)# -> dict with all avliable info (with api_id, api_hash)Detailed:phone='+123456789'proxy='127.0.0.1:8888'session=Reigister_api(phone,proxy)# initializingrandom_hash=seesion.send_code()# Send code requestcookies=session.login(input('confirmation code:'))# passing conf codesession.create_api()# creating new apisession.parse_api_data()# parsing & saving all api_id, api_hashdata=session.api_data()print(data)# -> dict with all avliable info (with api_id, api_hash)
Package: tg-apicore
Thorgate API CoreOpinionated API framework on top of Django REST frameworkFree software: ISC licenseSupports Python 3.5+, Django 1.11+, Django REST framework 3.6+FeaturesAPI documentation automatically generated from your viewsGeneral intro can be addedYou can add example request/response dataAutogenerated Pythonrequests-based examplesNot interactive yetIntegratesJSON APICursor pagination with configurable page sizeViewset classes for using different serializers and querysets for list/detail/edit endpointsAPI-specific 404 viewTest utilities, e.g. for response validationVersioning (WIP)Transformer-based approach, inspired bydjangorestframework-version-transformsandStripeUsagepip installtg-apicoreAddtg_apicoretoINSTALLED_APPSEnsure yourREST_FRAMEWORKsetting containsALLOWED_VERSIONS, e.g:# In your Django project settings:REST_FRAMEWORK={'ALLOWED_VERSIONS':('2018-01-01',),}Note that the default paginator requires that your models havecreatedfieldCreate API documentation view by subclassingAPIDocumentationViewand making necessary modifications.
Seeexample/example/views.pyfor example.Add main API urls plus 404 view (as fallback).Here’s an exampleurls.py:fromtg_apicore.viewsimportPageNotFoundViewfrommyproject.viewsimportMyProjectAPIDocumentationViewurlpatterns=[# The documentation viewurl(r'^api-docs/',MyProjectAPIDocumentationView.as_view(),name='api-docs'),# myproject.urls_api should contain your API urls patternsurl(r'^api/(?P<version>(\d{4}-\d{2}-\d{2}))/',include('myproject.urls_api')),# API-specific 404 for everything under api/ prefixurl(r'^api/',include(PageNotFoundView.urlpatterns())),]Seeexampledirectory for a more in-depth demo.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.3.0 (2018-05-23)Add Usage section to README (to make starting up easier)MostREST_FRAMEWORKsettings are now automatically configured by Thorgate API Core.
Users only need to specifyALLOWED_VERSIONS, the rest is optional.0.2.1 (2018-04-14)Fix packaging (tg_apicore subdirs weren’t included)0.2.0 (2018-04-14)Added PageNotFoundView (JSON-based 404 views)Added DetailSerializerViewSet (different serializers and queryset for list/detail/edit views)Added CreateOnlyFieldsSerializerMixin, ModelValidationSerializerMixin and BaseModelSerializerRenamed APIDocumentationView.get_patterns() to .urlpatterns()Improved example app a lot. It now also includes tests that partially test tg-apicore itself0.1.0 (2018-03-08)First release on PyPI.
Package: tgapp
tgBoosttgBoost is apipelineenglobing QSPR model optimized for the prediction of the glass transition temperature (Tg) of monomer organic compounds. The pipeline is based onmol2vec, amachine learning(ML) algorithm converting molecular SMILES into molecular embeddings. The pipeline can be exapanded to include further QSAR/QSPR models developed from SMILES notation.MotivationtgBoost is a kickstart project aiming at expanding the use of ML, Data Engineering and QSAR/QSPR models in atmospheric and physical chemistry. The pipeline comes with a pretrained and ML powered QSPR model predictingTgof monomer organic compounds. The model is based on a Extreme Gradient Boosting framework (XGBoost) and it is developed from the largest dataset of experimentalTgof monomer organic molecules (Koop et al., 2011).RequirementsPython >=3.6.0(Python 2.x isnot supported)NumPypandasscikit-learngensimRDKitmol2vecxgboostInstallationpip install https://github.com/U0M0Z/tgpipetgBoost library needs the independent installation of mol2vec via pip within the working environment:pip install git+https://github.com/samoturk/mol2vecBuild statusBuild status of continus integration i.e. travis, appveyor etc. Ex. -DocumentationDetails on the statistical analysis performed to develop the model and pipeline are found in the supportingarticle.UsageBasic useThis code uses the tgPipeline to train tgBoost a QSPR model forTgprediction. The QSPR model is based on rdkit, mol2vec and xgboost. In order to use the model on your machine, you need to retrain the model to be conform to the C++ signature of your processor.The tgBoost model is built, trained, and saved in./trained_modelswith the command:python tgPipeline/tgboost/train_pipeline.pyCheck for the following message to confirm successful model training:*** EXTRACTION step
n_input SMILES:  415 

*** TRANSFORMING step
n_output SMILES:  298 

~~ DATA info
Xtrain:  298 ytrain:  298 Xtest:  0 ytest:  0 

*** REGRESSION step

PIPELINE completed:
_ ~ ^ ~ _ ~ _ ~ ^ ~ _ ~ _ ~ ^ ~ _ ~ ^ ~ _ ~ ^ ~ _
  __       ___                __ 
 / /____ _/ _ )___  ___  ___ / /_
/ __/ _ `/ _  / _ \/ _ \(_-</ __/
\__/\_, /____/\___/\___/___/\__/ 
   /___/                         
_ ~ ^ ~ _ ~ _ ~ ^ ~ _ ~ _ ~ ^ ~ _ ~ ^ ~ _ ~ ^ ~ _As python modulefromtgboostimporttgboost.processing.smiles_managerassmfromtgboostimportpredictThefirst lineimports functions to open and preprocess files containing SMILES used for predictions, and thesecond lineimports functions for predictingTgof SMILES.Check notebooksrepositoryfor examples and details.How to cite?✨ 🍰 ✨@Article{D1EA00090J,author="Galeazzo, Tommaso and Shiraiwa, Manabu",title="Predicting glass transition temperature and melting point of organic compounds via machine learning and molecular embeddings",journal="Environ. Sci.: Atmos.",year="2022",volume="2",issue="3",pages="362-374",publisher="RSC",doi="10.1039/D1EA00090J",url="http://dx.doi.org/10.1039/D1EA00090J"}ContributeContact attommaso.galeazzo@gmail.comCreditsInitial development was supported byAirUCI, Irvine, CA.LicenseBSD 3-clause ©Tommaso Galeazzo
Package: tgapp-contactform
About contactformtgapp-contactform is a Pluggable application for TurboGears2 that
shows a simple contact form.Installingcontactform can be installed both from pypi or from bitbucket:pip install tgapp-contactformshould just work for most of the usersPlugging contactformIn your applicationconfig/app_cfg.pyimportplug:from tgext.pluggable import plugThen at theend of the filecall plug with contactform:plug(base_config, 'contactform')You will be able to access the plugged application athttp://localhost:8080/contactform.Optionscontactform.senderandcontactform.recipientscan
be used to specify the mail sender and receiver. They are required
but can be overwritten using thecontactform.before_sendhook.Available Hookscontactform makes available a some hooks which will be
called during some actions to alter the default
behavior of the appplications:contactform.before_send(maildata)->maildata-> called before sending email (the hookmust returnthe maildata)