Saravanan666/pypi-org huggingface dataset:
Package: exordium
exordiumCollection of preprocessing functions and deep learning methods.Supported featuresAudiofrequently used io for audio filesopenSMILE feature extractionspectrogram calculationWav2Vec2 feature extractionVideofrequently used io for videos and framesbounding box manipulation methodsface detection with RetinaFaceface landmarks and head pose with 3DDFA_V2body pose estimation with max-human-pose-estimatorcategorical and dimensional emotion estimation with EmoNetiris and pupil landmark estimation with MediaPipe Irisfine eye landmark estimation with MediaPipe FaceMesheye gaze vector estimation with L2CS-Nettracking using IoU and DeepFaceFAb-Net feature extractionOpenFace feature extractionR2+1D feature extractionTextBERT feature extractionRoBERTa feature extractionUtilsparallel processingio decoratorsloss functionsnormalizationVisualizationgraphs3D headpose2D landmarksgazedataframes to imagesSetupInstall package with all base and optional dependencies from PyPIpip install exordium[all]Install package with base dependencies from PyPIpip install exordiumInstall optional dependencies for specific modulesThe following extras will install the base and specific dependencies for using TDDFA_V2.pip install exordium[tddfa]You can install multiple optional dependencies as well.pip install exordium[tddfa,audio]Supported extras definitions:extras tagdescriptionaudiodependencies to process audio datatextdependency to process textual datatddfadependencies of TDDFA_V2 for landmark and headpose estimation, or related transformationsdetectiondependencies for automatic face detection and tracking in videosvideodependencies for various video feature extraction methodsallall previously described extras will be installedNote: If you are not sure which tag should be used, just go with the all-mighty "all".Install package for developmentgit clone https://github.com/fodorad/exordium
cd exordium
pip install -e .[all]
pip install -U -r requirements.txt
python -m unittest discover -s testProjects using exordium(2023) BlinkLinMulTLinMulT is trained for blink presence detection and eye state recognition tasks.
Our results demonstrate comparable or superior performance compared to state-of-the-art models on 2 tasks, using 7 public benchmark databases.paper: BlinkLinMulT: Transformer-based Eye Blink Detection (accepted, available soon)code:https://github.com/fodorad/BlinkLinMulT(2022) PersonalityLinMulTLinMulT is trained for Big Five personality trait estimation using the First Impressions V2 dataset and sentiment estimation using the MOSI and MOSEI datasets.paper: Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures (pdf,website)code:https://github.com/fodorad/PersonalityLinMulTWhat's nextAdd support for Action Unit detection (OpenGraphAU)Add support for Blink estimation (DenseNet121, LinT, BlinkLinMulT)Add support for Personality trait estimation (PersonalityLinMulT)Updates1.2.0: Add support for L2CS-Net gaze estimation.1.1.0: PyPI publish.1.0.0: Release version.ContactÁdám Fodor (foauaai@inf.elte.hu)
Package: exoscale
python-exoscale: Python bindings for Exoscale APIThis library allows developpers to use theExoscalecloud platform API with
high-level Python bindings. API documentation and usage examples can be found
at this address:https://exoscale.github.io/python-exoscaleDevelopmentFirst create a new virtual environment and runmake installrequirements.
Then create a configuration file with yourproductionuser's API keys.
This file may be create for example injunk/config.toml. Here is an example:default_profile = "prod:you"

[[profiles]]
name       = "prod:you"
api_key    = "EXOxxxx"
api_secret = "yyyy"Once this file is created you can run pytest with the following command:EXOSCALE_CONFIG_FILE=./junk/config.toml pytest -x -s -vvv tests/*.py
Package: exoscale-dns
Python API for Exoscale’s DNS offeringPython API for Exoscale’s DNS offeringFree software: MIT licenseDocumentation:https://exoscale-dns.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.2 (2018-04-05)First automatically uploaded release.0.1.1 (2018-04-05)First release on PyPI.
Package: exoscene
exosceneInstallation:pip install exosceneexosceneis a library of classes and utility functions for simulating
direct images of exoplanetary systems. The package was developed by Neil
Zimmerman (NASA/GSFC), with source code contributions from Maxime Rizzo,
Christopher Stark, and Ell Bogat. This work was funded in part by a WFIRST/Roman Science
Investigation Team contract (PI: Margaret Turnbull).exoscenemakes significant use of the Astropy, NumPy, SciPy, and
Scikit-image packages.A jupyter notebook providing usage examples for much of the functionality is included under the docs subdirectory:exoscene/docs/notebooks/Roman-CGI_scene_demo.ipynbThe functions are organized in 3 modules:exoscene/planet.py,exoscene/star.py, andexoscene/image.py.1.exoscene/planet.pya Planet() class with a data structure for containing the basic physical
parameters of a planet, its orbit, its host star, and associated methods for
computing its relative astrometry ephemeris, its phase function, and flux ratio.A function for modeling the orbital position and the Lambert sphere phase function,
based on the Keplerian orbital elements and date of observation.A function for mapping the time-dependent sky-projected position and
Lambert phase factor.2.exoscene/star.pyFunctions for computing the band-integrated irradiance of a star based on its
apparent magnitude and spectral type, and instrument bandpass, using the built-in
Bruzual-Persson-Gunn-Stryker (BPGS) Spectral Atlas (underexoscene/data/bpgs/)A function for computing the approximate parallax and proper motion offset for
a star, based on the celestial coordinates and observing dates.3.exoscene/image.pyA function for accurately resampling an image model array to a detector array.Functions for translating a coronagraph PSF model to an arbitrary field point,
taking into account position-dependent properties included in the model.Functions for applying a noise model to a detector intensity map, to simulate
an image with photon counting noise, read noise, and dark current, for a given
integration time.Copyright © 2020 United States Government as represented by the Administrator of the National Aeronautics and Space Administration.  All Other Rights Reserved.
Package: exosim
ExoSim 2.0IntroductionExoSim 2 is the next generation of the Exoplanet Observation SimulatorExoSimtailored for spectro-photometric observations of transiting exoplanets from space, ground, and sub-orbital platforms. This software is a complete rewrite implemented in Python 3, embracing object-oriented design principles, which allow users to replace each component with their functions when required.Table of contentsExoSim 2.0IntroductionTable of contentsHow to installInstall from PyPIInstall from source codeTest your installationDocumentationBuild the html documentationBuild the pdf documentationHow to contributeHow to citeHow to installInstall from PyPIExoSim 2.0 is available on PyPI and can be installed via pip aspip install exosimInstall from source codeExoSim 2.0 is compatible (tested) with Python 3.8, 3.9 and 3.10To install from source, clone therepositoryand move inside the directory.Then usepipaspip install .Test your installationIf you have installed ExoSim from source-code, to test your ExoSim2 installation simply run from the main ExoSim2 folderpip install -e ".[test]"
python -m unittest discover -s testsDocumentationExoSim2 comes with an extensive documentation, which can be built using Sphinx.
The documentation includes a tutorial, a user guide and a reference guide.To build the documentation, install the needed packages first via:pip install -e ".[docs]"Build the html documentationTo build the html documentation, move into thedocsdirectory and runmake htmlThe documentation will be produced into thebuild/htmldirectory insidedocs.
Openindex.htmlto read the documentation.Build the pdf documentationTo build the pdf, move into thedocsdirectory and runmake latexpdfThe documentation will be produced into thebuild/latexdirectory insidedocs.
Openexosim2.pdfto read the documentation.Here is reported the use ofpdflatex, if have another compiler for LaTex, please refer tosphinx documentation.How to contributeYou can contribute to ExoSim 2.0 by reporting bugs, suggesting new features, or contributing to the code itself. If you want to contribute to the code, please follow the steps described in the documentation underDeveloper guide/Contributing guidelines.How to citeA dedicated publication has been submitted and the relative information will be published soon. In the meanwhile, please, send an email to the developers.
Package: exosip2ctypes
Source:http://github.com/tanbro/exosip2ctypes/Document:http://exosip2ctypes.readthedocs.org/Repo:http://pypi.org/project/exosip2ctypes/eXosip2CTypeseXosip2CTypes is apythonpackage, it wrapseXosip2.It is based onctypes, and loadseXosip2shared object on runtime.EnvironmentOS:LinuxMacOSPythonVersion:Python2.x:2.7Python3.x:3.4and laterInstallationInstalleXosip2.The libraryeXosip2depends onosip2, they are nativeClibraries.
You can compile these two libraries from source code, or install them by package managers if possible.
For example, ubuntu 16.04 users can install the libraries:sudo apt install libexosip2-11“develop” packages are not needed.InstallPythonInstallPython2.7 orPython3.4+.
Avoid this if it’s already in your OS.Install eXosip2CTypesIt’s advised to install eXosip2CTypes usingpip:pip install exosip2ctypesOr clone fromhttp://github.com/tanbro/exosip2ctypes, then executesetup.py:https://github.com/tanbro/exosip2ctypes.git
cd exosip2ctypes
path/of/your/python setup.py installDevelopmentNow, your can use it in your APP, Good luck!Visithttp://exosip2ctypes.readthedocs.org/for the api docs.The project’s API documentation is written inside the source code asDocstring,
you shall build the documentation from source, usingsphinx-doc.Check blow documentations for more infomations:http://www.antisip.com/doc/exosip2/index.htmlhttp://www.gnu.org/software/osip/doc/html/index.html
Package: exosite
# Exosite Murano Command Line Tool (exosite cli)The tool will upload all resources described in the Solutionfile.json to a solution in your business account. This includes static assets, endpoint definitons, event handlers and modules.## Requirements- python 2.7 or later```sudo pip install --upgrade exosite```If you are running OS X El Capitan, the following may be necessary:```sudo pip install --upgrade exosite --ignore-installed six --ignore-installed prompt-toolkit --ignore-installed requests```## Usage:This command will prompt you to input credential data and save it to file '.Solutionfile.secret' for latter use.```exosite --init```This command will perform upload, the target script/file will be read from config file 'Solutionfile.json'```exosite --deploy```#### Usage:usage: exosite [-h] [--host [HOST]] [-k] [-p] [-s] [-a] [-e] [-m] [--deploy] [--init] [-v]#### Arguments:```-h, --help            show this help message and exit--host [HOST]-k, --insecrue        Ignore SSL-p, --upload_productidUpload static file-s, --upload_static   Upload static file-a, --upload_api      Upload api-e, --upload_eventhandlerUpload event handler-m, --upload_modules  Upload modules-c, --update_cors     Update cors configuration--enable_identity <identity> Add identity--delete_identity <identity> Delete identity--logs [tail]         Script log information--read <identity> <alias>  Read data from resource--write <identity> <alias> <value>Write data to resource--tree                Listing resources--watch               Watch for modified files and deploy automatically--open [product|solution]Open solution/product url in browser--deploy              Upload all solution configurations--init                Configure for credential parameters-v, --version         Show Version number```#### Solutionfile.json format:```{"version": "0.2","default_page": "index.html","file_dir": "public","custom_api": "sample_api.lua","custom_api_hook": "_init","product_spec": {"lightbulb": "lightbulb.yaml"},"modules": {"auth": "modules/util.lua","debug": "modules/debug.lua"},"event_handler": {"device": {"datapoint": "event_handler/product.lua"},"timer": {"timer": "event_handler/timer.lua"}}}{"version": "0.3","default_page": "index.html","assets": "public","routes": "sample_api.lua","routes_hook": "_init","modules": {"auth": "modules/util.lua","debug": "modules/debug.lua"},"services": {"device": {"datapoint": "event_handler/product.lua"},"timer": {"timer": "event_handler/timer.lua"}}"cors": {"origin": ["http://localhost:*"],"methods": ["get", "post"],"headers": ["accept", "content-type", "authorization"],"expose_headers": ["X-Key"],"credentials": true}}```#### Custom API file format:```--#ENDPOINT <METHOD> <PATH>SCRIPT_CODE```where `--#ENDPOINT <METHOD> <PATH>` is also separator between endpoint definitions, for example:```--#ENDPOINT GET /keystore/{value}return Keystore.get({key = request.parameters.value})--#ENDPOINT POST /user/{email}/lightbulbsreturn User.listUserRoleParamValues({id = request.user_id,role_id = "lightbulb_owner",parameter_name = "serialnumber"})```
Package: exoskelegram
exoskelegram is an X11 key press/release catcher/sender wrapper with
abstractions. In a future release, in addition to being able to catch
and send X key events from Python programs, exoskelegram will allow
you to use simple config files to temporarily reprogram your entire
keyboard — or just a part of it.Licenseexoskelegram is free software under the terms of the GNU Affero General Public
License version 3 (or any later version). This is version 0.1.0 of the program.ContactThe author of exoskelegram is Niels Serup. Bug reports and suggestions should
be sent tons@metanohi.namefor the time being.InstallationDependenciesexoskelegram requires the X11 headers and the Xtst extension. If you’re using a
package manager, remember to install the development packages (*-devor*-develor something similar).Way #1Get the newest version of exoskelegram athttp://metanohi.name/projects/exoskelegram/or athttp://pypi.python.org/pypi/exoskelegramExtract exoskelegram from the downloaded file, cd into it and run this in a
terminal:# python3 setup.py installExamples are available in theexamplesdirectory.Way #2Just run this:# pip-3.1 install exoskelegramNote that this will not make any examples available.UseAs a command-line toolRunexoskelegramto use it. Runexoskelegram--helpto see how to use it.As a moduleTo find out how to use it, run:$ pydoc3 exoskelegram.xkeylibExamplesThere are a few examples in theexamplesdirectory. None of them apply to
the current few-features version.Developmentexoskelegram uses Git for code management. The newest (and perhaps unstable)
code is available at:$ git clone git://gitorious.org/exoskelegram/exoskelegram.gitThis documentCopyright (C) 2011 Niels SerupCopying and distribution of this file, with or without modification, are
permitted in any medium without royalty provided the copyright notice and this
notice are preserved.  This file is offered as-is, without any warranty.
Package: exoskeleton
ExoskeletonMachine Learning and other applications make it necessary to download thousands or sometimes hundreds of thousands of files.Using a high-speed-connection carries the risk to run an involuntary denial-of-service attack on the servers that provide those files and webpages.Exoskeleton is a Python framework that helps you build a crawler / scraper that avoids too high loads on the connection and instead runs permanently and fault tolerant to ultimately download all files.Its main functionalities are:Managing the download queue and document data within a MariaDB database.Avoid processing the same URL more than once.Working through the queue by eitherdownloading files to disk,storing the page source code into a database table,storing the page text,or making PDF-copies of webpages.Managing already downloaded files:Storing multiple versions of a specific file.Assigning labels to downloads, so they can be found and grouped easily.Sending progress reports to the admin.DocumentationHow To Use ExoskeletonInstallation and RequirementsCreate a BotDealing with result pagesAvoiding duplicatesThe Queue: Downloading files / Saving the page code / Creating PDFBot BehaviorProgress Reports via EmailFile Versions and LabelsUsing the BlocklistExample UsesDownloading an Archive: A quite complex use case requiring some custom SQL. This is the actual project that triggered the development of exoskeleton.Technical DocumentationContributingDatabase StructureTestingExample#!/usr/bin/env python3# -*- coding: utf-8 -*-importloggingimportexoskeletonlogging.basicConfig(level=logging.DEBUG)# Create a bot# exoskeleton makes reasonable assumptions about# parameters left out, like:# - host = localhost# - port = 3306 (MariaDB standard)# - ...exo=exoskeleton.Exoskeleton(project_name='Bot',database_settings={'database':'exoskeleton','username':'exoskeleton','passphrase':''},# True, to stop after the queue is empty, Otherwise it will# look consistently for new tasks in the queue:bot_behavior={'stop_if_queue_empty':True},filename_prefix='bot_',chrome_name='chromium-browser',target_directory='/home/myusername/myBot/')exo.add_file_download('https://www.ruediger-voigt.eu/examplefile.txt')# => Will be saved in the target directory. The filename will be the#    chosen prefix followed by the database id and .txt.exo.add_file_download('https://www.ruediger-voigt.eu/examplefile.txt',{'example-label','foo'})# => Duplicate will be recognized and not added to the queue,#    but the labels will be associated with the file in the#    database.exo.add_file_download('https://www.ruediger-voigt.eu/file_does_not_exist.pdf')# => Nonexistent file: will be marked, but will not stop the bot.# Save a page's code into the database:exo.add_save_page_code('https://www.ruediger-voigt.eu/')# Use chromium or Google chrome to generate a PDF of the website:exo.add_page_to_pdf('https://github.com/RuedigerVoigt/exoskeleton')# work through the queue:exo.process_queue()
Package: exos-okta
A Python library for the Okta Management APIs.Getting Started` $ pip install okta `What you needAn Okta account, called an _organization_ (sign up for a free developer organization if you need one)[An API token](https://developer.okta.com/docs/api/getting_started/getting_a_token)Look for our quickstart guide here!
-https://github.com/okta/okta-sdk-pythonOkta Developer ForumFind out more and look for help here:https://devforum.okta.com/
Package: exosomians
ExosomiansGetting StartedInstallationInstallation with pipTo install the latest version from PyPI, simply use the following bash script:pipinstallexosomiansor you can clone this repository and install via setup.py file:gitclonehttps://github.com/Exosomians/exosomianscdexosomians
pythonsetup.py-qExamplesInferenceYou can use pre-trained models to make predictions on your own datasetsimportexopyasexo# Load the pre-trained modelmodel=exo.ml.ExoGRU.load('./saved_models/ExoGRU/exogru_best-v2.ckpt')# Prepare datasetdata=model.prepare_data('/path/to/fasta/data.fasta',seq_key='seq')# Get the predictionsdf_results=model.predict(data,batch_size=128)# Results will be stored in a pandas dataframeTrain from scratchimportexopyasexoexo.ml.ExoGRU.setup_dataset(path='/path/to/dataset/design.mat.csv',seq_key='seq',target_key='label',fraction=1.0)config={'activation_fn':'relu','batch_size':32,'bidirectional':False,'dropout_rate':0.1,'lr':0.00011342016019358544,'n_head_hidden':512,'n_head_layers':2,'n_hidden':1024,'n_layers':1,'network':'exogru','use_batch_norm':True,'use_layer_norm':False}model=exo.ml.ExoGRU(**config)model.fit(max_epochs=2000,train_size=0.8,batch_size=128,early_stopping_patience=5,check_val_every_n_epoch=3,save_path='./saved_models/ExoGRU/',)Sample NotebooksModelPathExoGRUnotebooks/training_demos/ExoGRU.ipynbExoCNNnotebooks/training_demos/ExoCNN.ipynbExoLSTMnotebooks/training_demos/ExoLSTM.ipynb
Package: exotel
exotel-pyPython module for exotels call and sms api’sinstallationpip install exotelUsageInitializefrom exotel import Exotelclient = Exotel(sid,token)make call to connect a number to anotherclient.call_number('from_number','exophone','to_number')make call to connect a number to a flowclient.call_flow('from_number','exophone','flow_id')send an smsclient.sms('from_number',to_number',"sms_body")get details of an smsclient.sms_details('sms_sid')get details of a callclient.call_details('call_sid')
Package: exotic-options
EOPricer ModelThis simple Python package calculates the price of some barrier and exotic options.
This consists on an adptation of the Black and Scholes Model to the Bjerksund e Stensland Studies.Installpipinstallexotic-optionsImportfromexotic_optionsimportEOPricerClass and Function argumentsArgs:
S (float): Current price of the underlying asset.
K (float): Strike price of the option.
r (float): Risk-free interest rate most appropriate for this option.
T (float): Number of days till the expiration date.
H (float): Barrier Value.
type (str): Type of the option. Either 'call' or 'put'. Defaults to 'call'.Create an instance of EOPricer Classpricer=EOPricer(S=None,K=None,r=None,T=None,H=None,option_type='call')pricer.call_down_and_out(sigma=None)
Package: exotx
exotxexotx is a Python wrapper for theQuantLib library, a powerful open-source library for
quantitative finance. exotx provides a simple and user-friendly interface for pricing and analyzing financial
derivatives using QuantLib's advanced numerical methods.InstallationTo install exotx, simply use pip:pipinstallexotxUsageDefine the productimportexotxnotional=100strike=100.0autocall_barrier_level=1.0# 100%annual_coupon_value=0.03# 3.00%coupon_barrier_level=0.75# 75%protection_barrier_level=0.75# 75%my_autocallable=exotx.Autocallable(notional,strike,autocall_barrier_level,annual_coupon_value,coupon_barrier_level,protection_barrier_level)Define the static dataThe object that represents static data such as the calendar, the day counter or the business day convention used.From the constructormy_static_data=exotx.StaticData(day_counter='Actual360',business_day_convention='ModifiedFollowing')From JSONmy_json={'day_counter':'Actual360','business_day_convention':'ModifiedFollowing'}my_static_data=exotx.StaticData.from_json(my_json)Define the market dataFrom the constructorreference_date='2015-11-06'spot=100.0risk_free_rate=0.01dividend_rate=0.0black_scholes_volatility=0.2my_market_data=exotx.MarketData(reference_date,spot,risk_free_rate,dividend_rate,black_scholes_volatility=black_scholes_volatility)From JSONmy_json={'reference_date':'2015-11-06','spot':100,'risk_free_rate':0.01,'dividend_rate':0,'black_scholes_volatility':0.2}my_market_data=exotx.MarketData.from_json(my_json)Price the productVanilla Optionfromexotx.instrumentsimportOptionTypefromexotx.enumsimportPricingModel,NumericalMethodstrike=90option_maturity='2016-05-04'my_vanilla_option=exotx.VanillaOption(strike,option_maturity,OptionType.CALL)my_pricing_config=exotx.PricingConfiguration(PricingModel.BLACK_SCHOLES,NumericalMethod.ANALYTIC,compute_greeks=True)exotx.price(my_vanilla_option,my_market_data,my_static_data,my_pricing_config)>>> { 
    'price': 13.83328710, 
    'delta': 0.77183751, 
    'gamma': 0.01609460, 
    'theta': -7.01024983
}Auto-Callableexotx.price(my_autocallable,my_market_data,my_static_data,model='black-scholes')>>> 96.08517973497098ContributingWe welcome contributions to exotx! If you find a bug or would like to request a new feature, please open an issue on
theGithub repository.
If you would like to contribute code, please submit a pull request.Licenseexotx is released under theMIT License.