Saravanan666/pypi-org huggingface dataset:
Package: zenodopy
zenodopyProject under active deveopment, not production readyA Python 3.6+ package to manageZenodorepositories.Functions Implemented.create_project(): create a new project.upload_file(): upload file to project.download_file(): download a file from a project.delete_file(): permanently removes a file from a projectInstallingPyPipipinstallzenodopy==0.2.0GitHubpipinstall-egit+https://github.com/lgloege/zenodopy.git#egg=zenodopyUsing the PackageCreate a Zenodo access tokenby first logging into your account and clicking on your username in the top right corner. Navigate to "Applications" and then "+new token" under "Personal access tokens".  Keep this window open while you proceed to step 2 becausethe token is only displayed once.Store the tokenin~/.zenodo_tokenusing the folowing command{echo'ACCESS_TOKEN: your_access_token_here'}>~/.zenodo_tokenstart using thezenodopypackageimportzenodopy# always start by creating a Client objectzeno=zenodopy.Client()# list projectszeno.list_projects# list fileszeno.list_files# create a projectszeno.create_project(title="test_project",upload_type="other")# your zeno object now points to this newly created project# create a file to uploadwithopen("~/test_file.txt","w+")asf:f.write("Hello from zenodopy")# upload file to zenodozeno.upload_file("~/test.file.txt")NotesThis project is under active development. Here is a list of things that needs improvement:more tests: need to test uploading and downloading filesdocumentation: need to setup a readthedocsdownload based on DOI: right now you can only download from your own projects. Would be nice to download fromasyncronous functions: useasyncioandaiohttpto write async functions. This will speed up downloading multiple files.
Package: zenodo-rest
# zenodo-restA python wrapper of Zenodo’s REST API for python and the command line.## InstallationFor usage as a module, install with your preferred env manager (pip, pipenv, conda, … ).
For CLI usage [pipx](https://pypa.github.io/pipx/) is recommended.` bash pip--userpipx pipx installzenodo-rest`## UsageThe CLI has built in documentation.` bashzenodo-rest--helpzenodo-restdepositions--help`For information about the module or more details about the CLI, see the source-code or [readthedocs](https://zenodo-rest.readthedocs.io/en/latest/zenodo_rest.depositions.html).
Package: zenodo-search
Zenodo SearchPerform zenodo searches with python.Note: Zenodo updated the backend. This resulted in the searches failing with the old version of this package. For
example, the doi string should not be the full doi anymore, but only the id. Some of the old search strings also don't
work anymore. This is hopefully a temporary issue and will be fixed soon.Installationpipinstallzenodo_searchDependenciesrequestsUsageimportzenodo_searchaszsearchsearch_string='doi:8357399'records=zsearch.search(search_string)More examples can be found in theexamplesfolder.
Package: zeno-evals
Zeno 🤝 OpenAI EvalsUseZenoto visualize the results ofOpenAI Evals.https://user-images.githubusercontent.com/4563691/225655166-9fd82784-cf35-47c1-8306-96178cdad7c1.movExample usingzeno-evalsto explore the results of an OpenAI eval on multiple choice medicine questions (MedMCQA)Usagepipinstallzeno-evalsRun an evaluation following theevals instructions. This will produce a cache file in/tmp/evallogs/.Pass this file to thezeno-evalscommand:zeno-evals/tmp/evallogs/my_eval_cache.jsonlExampleSingle example looking at US tort law questions:zeno-evals./examples/example.jsonlAnd an example of comparison between two models:zeno-evals./examples/crossword-turbo.jsonl--second-results-file./examples/crossword-turbo-0301.jsonlAnd lastly, we can pass additionalZeno functionsto provide more context to the results:pipinstallwordfreq
zeno-evals./examples/crossword-turbo.jsonl--second-results-file./examples/crossword-turbo-0301.jsonl--functions_file./examples/crossword_fns.py
Package: zenoh
Zenoh Python APIZenohis an extremely efficient and fault-tolerantNamed Data Networking(NDN) protocol
that is able to scale down to extremely constrainded devices and networks.The Python API is for pure clients, in other terms does not support peer-to-peer communication, can be easily
tested with our demo instace available atdemo.zenoh.io.DependenciesThe zenoh-python API depends on thezenoh-cAPI. Thus the first thing to do is to ensure thatzenoh-cin installed on your machine. To do so, please follow the instructions providedhere.Installing the Python API from SourcesTo install the API you can do:$ python3 setup.py installNotice that on some platforms, such as Linux, you will need to do this assudo.Running the ExamplesTo run the bundled examples without installing any additional software you can thezenohdemo instance
available atdemo.zenoh.io. To do so, simply run as follows:$ cd zenoh-python/example
$ python3 sub.py -z demo.zenoh.ioFrom another terminal:$ cd zenoh-python/example
$ python3 sub.py -z demo.zenoh.io
Package: zenoh-cli
zenoh-cliA command line tool for interacting with a Zenoh sessionTypical use cases include:TestsInvestigationsProbingAs part of a bash pipelinezenoh-climakes use ofcodecsfor encoding and decoding to and from the wire payloads on the zenoh network bus. By default,zenoh-clibundles the following codecs:textbase64JSONA plugin system is in place to allow for easily extending the available codecs inzenoh-cli, see below.Installationpip install zenoh-cliUsageusage: zenoh [-h] [--mode {peer,client}] [--connect CONNECT] [--listen LISTEN] [--config CONFIG] [--log-level LOG_LEVEL] {info,network,scout,delete,put,subscribe,get} ...

Zenoh command-line client application

positional arguments:
  {info,network,scout,delete,put,subscribe,get}

options:
  -h, --help            show this help message and exit
  --mode {peer,client}
  --connect CONNECT     Endpoints to connect to. (default: None)
  --listen LISTEN       Endpoints to listen on. (default: None)
  --config CONFIG       A path to a configuration file. (default: None)
  --log-level LOG_LEVELExample output ofzenoh networkExtending with codecs for encoding/decoding valueszenoh-clicomes with a plugin system for easily extending it with custom encoders and decoders (codecs) for the data values. The plugin system makes use of the entrypoints provided by setuptools, seeherefor details.zenoh-cligather plugins from two custom "groups":zenoh-cli.codecs.encoderszenoh-cli.codecs.decodersFor an example, seeexample_plugin
Package: zenoh-ros-type
zenoh-ros-type-pythonThe repository contains common class for ROS 2 messages used by Zenoh.The messages come from:common_interface: Common-used ROS messagercl_interface: Common interface in RCLautoware_auto_msgs: messages used in Autowaretier4_autoware_msgs: messages used in AutowareUsageYou can download the packages viaPyPI.python3-mpipinstallzenoh-ros-type
Package: zenomatic
Shows the Zen of Python in your ApplicationsAuthor:Chris MaillefaudVersion:1.0.0Python Version:2.7, 3.5 and 3.6Date:October, 16, 2017Install and UseInstall with pip:pip install zenomaticUseRun from command line:Options:‘random’: show a random quote‘seed <number>’: show numbererd quote‘all’: show all quotesExample:zenomatic randomRun inside application:from zenomatic import get_quote()ret, quote = get_quote(1)if ret:print(quote)Avaliable commandsget_quote(number)Get specific quote from 0 to 18get_quotes()Get all quotes
Package: zenoml
Zeno is a general-purpose framework for evaluating machine learning models.
It combines aPython APIwith aninteractive UIto allow users to discover, explore, and analyze the performance of their models across diverse use cases.
Zeno can be used for any data type or task withmodular viewsfor everything from object detection to audio transcription.DemosImage ClassificationAudio TranscriptionImage GenerationDataset ChatbotSensor ClassificationImagenetteSpeech Accent ArchiveDiffusionDBLangChain + NotionMotionSensecodecodecodecodecodehttps://user-images.githubusercontent.com/4563691/220689691-1ad7c184-02db-4615-b5ac-f52b8d5b8ea3.mp4QuickstartInstall the Zeno Python package from PyPI:pipinstallzenomlCommand LineTo get started, run the following command to initialize a Zeno project. It will walk you through creating thezeno.tomlconfiguration file:zenoinitTake a look at theconfiguration documentationfor additionaltomlfile options like adding model functions.Start Zeno withzeno zeno.toml.Jupyter NotebookYou can also run Zeno directly from Jupyter notebooks or lab. Thezenocommand takes a dictionary of configuration options as input. Seethe docsfor a full list of options. In this example we pass the minimum options for exploring a non-tabular dataset:importpandasaspdfromzenoimportzenodf=pd.read_csv("/path/to/metadata/file.csv")zeno({"metadata":df,# Pandas DataFrame with a row for each instance"view":"audio-transcription",# The type of view for this data/task"data_path":"/path/to/raw/data/",# The folder with raw data (images, audio, etc.)"data_column":"id"# The column in the metadata file that contains the relative paths of files in data_path})You can pass a list of decorated function references directly Zeno as you add models and metrics.CitationPlease reference ourCHI'23 paper@inproceedings{cabrera23zeno,author={Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},title={Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},year={2023},isbn={978-1-4503-9421-5/23/04},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3544548.3581268},doi={10.1145/3544548.3581268},booktitle={CHI Conference on Human Factors in Computing Systems},location={Hamburg, Germany},series={CHI '23}}CommunityChat with us on ourDiscord channelor leave an issue on this repository if you run into any issues or have a request!
Package: zenopy
CI/CDCode Coverage and QualityTechnical SupportFoundationzenopyAn open-source Python library for Zenodo REST APIOVERVIEWzenopy provides a comprehensive Python library for productivity
allowing scientists and engineers to publish their work on Zenodo
in compliance with FAIR principles.INSTALLATIONThe installation procedure can be as easy as running the following
command in a terminalpipinstallzenopy
Package: zeno-sliceline
Sliceline is a Python library for fast slice finding for Machine
Learning model debugging.It is an implementation ofSliceLine: Fast, Linear-Algebra-based Slice
Finding for ML Model
Debugging,
from Svetlana Sagadeeva and Matthias Boehm of Graz University of
Technology.👉 Getting startedGiven an input datasetXand a model error vectorerrors,
SliceLine finds the top slices inXthat identify where a ML model
performs significantly worse.You can use sliceline as follows:fromsliceline.slicefinderimportSlicefinderslice_finder=Slicefinder()slice_finder.fit(X,errors)print(slice_finder.top_slices_)X_trans=slice_finder.transform(X)We invite you to check thedemo
notebooksfor a more thorough tutorial:Implementing Sliceline on Titanic datasetImplementing Sliceline on California housing dataset🛠 InstallationSliceline is intended to work withPython 3.7 or above. Installation
can be done withpip:pipinstallslicelineThere arewheels
availablefor Linux,
MacOS, and Windows, which means that you most probably won’t have to
build Sliceline from source.You can install the latest development version from GitHub as so:pipinstallgit+https://github.com/DataDome/sliceline--upgradeOr, through SSH:pipinstallgit+ssh://git@github.com/datadome/sliceline.git--upgrade🔗 Useful linksDocumentationPackage releasesSliceLine paper👐 ContributingFeel free to contribute in any way you like, we’re always open to new
ideas and approaches.Open a
discussionif you have any question or enquiry whatsoever. It’s more useful to
ask your question in public rather than sending us a private email.
It’s also encouraged to open a discussion before contributing, so
that everyone is aligned and unnecessary work is avoided.Feel welcome toopen an
issueif
you think you’ve spotted a bug or a performance issue.Please check out thecontribution
guidelinesif you want to bring modifications to the code base.📝 LicenseSliceline is free and open-source software licensed under the3-clause BSD license.
Package: zenoss-hipchat
Command suitable in use for Zenoss notification commands for sending events to hipchat.InstallationSimply install using your preferred python package manager with
either:pip installzenoss-hipchatfor the latest release, orpip install-egit+https://github.com/carsongee/zenoss-hipchat#egg=zenoss-hipchatfor the latest development version.ConfigurationIn Zenoss go toEvents->Triggersand create a trigger with
the rules for which you want to send events to hipchat.  Of course you
can use an existing trigger as well.  For more detailed guide on
triggers and notifications see thecommunity documentation.After you have a trigger you wish to use, go tonotificationsand
create a new notification.  Set theIdto something memorable likeHipChatErrorsor similar and chooseCommandas the action.After creating the notification, edit it.  On theNotificationtab
configure it as you see fit, but you are generally going to want to
make sure it is enabled, and that you have added the Trigger you
created earlier.  The command does support clear messages, so go ahead
and check that option if you like.Now on theContenttab of the notification paste the following
into theCommandfield:zenoss-hipchat--device="${evt/device}"--info=${evt/summary}--component="${evt/component}"--severity=${evt/severity}--url="${urls/eventUrl}"--message=${evt/message}And if you want to use the clear option, for the clear command:zenoss-hipchat--device="${evt/device}"--info=${evt/summary}--component="${evt/component}"--severity=${evt/severity}--url="${urls/eventUrl}"--message=${evt/message}--cleared-by="${evt/clearid}"--clearYou also need to provide the room and API token using theEnvironment variablesfield with something like:HIPCHAT_TOKEN=<APIv1Token>;HIPCHAT_ROOM=<RoomName(orID)topostto>replacing the values with ones appropriate for you.Additional Environment VariablesIn addition toHIPCHAT_TOKENandHIPCHAT_ROOMwhich are
required, you can also override other options with the following
optional environment variables:HIPCHAT_API_V1_ENDPOINT- Allows you to override the API
endpoint if you are using private HipChatHIPCHAT_FROM- Defaults to Zenoss, and determines who the
messages appear to be coming from.HIPCHAT_TIMEOUT- Defaults to 3 seconds, but if you have a slow
connection to the HipChat server it can be increased or decreased.HIPCHAT_NOTIFY_SEVERITY- Defaults to Error and above (4), but
can raised or lowered and determines which events trigger the
HipChat notification.
Package: zenpad
InstallThe most easy way via pypi:pip install zenpador:easy_install zenpadOr you can install from sources:python setup.py build
sudo python setup.py installOr run directly from sources without any install:./zenpad.pyAlso you need PyQt4 to be installed.UsageSimply run:zenpadLinkssite:http://zenpad.ru/repo:http://bitbucket.org/imbolc/zenpad/google group:http://groups.google.com/group/zenpadrussian docs:http://about.zenpad.ru/
Package: zenpass
ZenpassTo generate random and strong passwords.Installationpip install -U zenpassUsageusage: zenpass [options]

optional arguments:
  -h, --help         show this help message and exit
  -v, --version      show version number and exit.

to customize Password:
  -l , --length      to set length to the password
  -n , --ignore      to ignore unwanted characters to the password
  -i , --include     to include characters to the password
  -o , --only        to create password only using wanted characters
  -s , --separator   the separator character
  -c , --seplen      the length of characters between separator
  --repeat           to repeat the characters in the password (default : False)
  --separation       to separate password characters using separator (default : False)
  --show             to show password (default : False)
  
keywords: [alphabets, uppercase, lowercase, numbers, symbols] 
can be given as input for following params: ignore, include, onlyPython ScriptTo generate a random password.from zenpass import PasswordGenerator

pg = PasswordGenerator()
pg.generate()Command LineTo generate a random password.$ zenpass
Password copied to clipboard.To set the password length, Default password length is8-16.$ zenpass -l 10 --show
Password: Q3m/vro|uR
Password copied to clipboard.Whether the characters in passwords repeat or not,
Default value ofrepeatisFalse.$ zenpass -r --show
Password: 96Ndl;1D$jQu4Z2
Password copied to clipboard.To include, ignore or use only'alphabets','numbers','uppercase','lowercase','symbols'andrandom charactersin generating password.To ignorenumbersin passwords.$ zenpass -n numbers --show
Password: uyMXP‘$!ZSCYqzj
Password copied to clipboard.To ignore charactersa,b,c,d,e$ zenpass -n abcde --show
Password: ~}t"R‘jF'ksG8~E
Password copied to clipboard.To create a password only usingspecial characters.$ zenpass -o symbols -l 15 --show
Password: ?)".=-_^[_‘~{.)
Password copied to clipboard.To includea,b,c,d,echaracters in a password.$ zenpass -o numbers -i abcde -l 15 --show
Password: 78713d1e3d926a3
Password copied to clipboard.To separate characters in a password using separator.$ zenpass -o uppercase --separation -l 16 --show
Password: YNQC-RKBF-DMAT-UVIP
Password copied to clipboard.To separate characters in a password using separator_with5characters between each separator.$ zenpass -o uppercase --separation -l 15 -s _ -c 5 --show
Password: YNQCR_KBFDM_ATUVI
Password copied to clipboard.Issues:If you encounter any problems, please file anissuealong with a detailed description.
Package: zenq
CLV packageFree software: MIT licenseInstallationTo install Zenq CLV Models Library, simply run the following command:pipinstallzenq-clv-modelsThe StoryIn order to provide marketing analysts and data scientists with a useful tool, the ZENQ package was developed. Because it is connected to a database, our product can be utilized by a wider variety of customers, including those who have a limited understanding of coding. Users are able to run scripts derived from the ZENQ package while the package works on data pertaining to customers. The data may be inserted into the database by users. It gives users the ability to study the behaviors of consumers based on how they engage with the company. Computations of CLV and RFM, in addition to forecasts, are the primary objective of the package. It features a Machine Learning component that makes an assumption as to whether the client will “die” or still be alive after a certain amount of time has passed. For the purpose of developing assumptions about the customers’  loyalness, ZENQ relies on the Pareto/NBD model. Because the package offers a number of different visualizations, it simplifies the process of comprehending the statistics and basing business decisions on those findings.Usage - Simple ExampleOnce installed, you can use the library in your Python scripts as follows:#run in terminal for postgres url creationdockerrun--namemy-postgres-db-ePOSTGRES_USER=master-ePOSTGRES_PASSWORD=pass-ePOSTGRES_DB=GLOBBING-p5432:5432-dpostgres# Initialize database with tablesfromzenq.api.prepare_dbimportdbm=db()m.main()# Insert data into databasefromzenq.api.endpointsimportinsert_factsinsert_facts('globbing.csv','Customer','Gender','InvoiceId','Date','Product_weight','Product_price')# Insert data of logging into LOGS tablefromzenq.api.endpointsimportupdate_logupdate_log()#define modelfromzenq.clvmodels.paretoimportModelmodel=Model()cltv=model.cltv_df()rfm=model.rfm_score()parameters=model.model_params()alive=model.customer_is_alive()#define Visualizationsfromzenq.visualizations.plotimportVisualsgender_price=visuals.gender_price()CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: zen-quotes
zen_quotesA simple library that returns the Zen of Python quotes.Installationpip install zen_quotesUsage# import lib 
from zen_quotes.quote import Quotes# That will return you all quotes in English.
Quotes().get_quotes()# Altogether there are 19 quotations, but if you need only one quotation, 
# then pass the quotation position number on the list.

Quotes().get_quotes(quote=2) # this will return you the second quote on the list in English.

Quotes().get_quotes(lang='pt', quote=2) # this will return you the second quote on the list in Portuguese.This lib searches the project data:https://github.com/ymussi/zen_of_pythonTODO[] add other languages
Package: zensectfpy
simple-vuln-py-libPython library that contains a snarky storage secret.Installationpip install --upgrade zensectfpyPublic-facing functionalityfromzensectfpy.rickrollimportprint_rickroll# Importing the `print_rickroll` functionnum_iterations:int=10# Number of times the Rickroll lyrics are to be printedprint_rickroll(num_iterations)# Calling the functionZense CTF ChallengeThere is some private functionality hidden in this repo. Access it, and you shall find the flag!
Package: zenserp
Zenserp Python ClientZenserp Python Client is the official Python Wrapper around the zenserpAPI.InstallationInstall from pip:pipinstallzenserpInstall from code:pipinstallgit+https://github.com/zenserp/zenserp-python.gitUsageAll zenserp API requests are made using theClientclass. This class must be initialized with your API access key string.Where is my API access key?In your Python application, importzenserpand pass authentication information to initialize it:importzenserpclient=zenserp.Client('API_KEY')Retrieve Statusstatus=client.status()print(status['remaining_requests'])Retrieve SERPsparams=(('q','Pied Piper'),('location','United States'),('search_engine','google.com'),)result=client.search(params)print(result)Contact usAny feedback? Please feel free tocontact our team.
Package: zensols.amr-coref
amr_corefA python library / model for creating co-references between AMR graph nodes.InstallTo install:pipinstallzensols.amr_corefAboutamr_coref is a python library and trained model designed to do co-referencing
betweenAbstract Meaning Representationgraphs.The project follows the general approach of theneuralcoref projectand it's excellentblog on the co-referencing.
However, the model is trained to do direct co-reference resolution between graph nodes and does not depend on
the sentences the graphs were created from.The trained model achieves the following scoresMUC   :  R=0.647  P=0.779  F₁=0.706
B³    :  R=0.633  P=0.638  F₁=0.630
CEAF_m:  R=0.515  P=0.744  F₁=0.609
CEAF_e:  R=0.200  P=0.734  F₁=0.306
BLANC :  R=0.524  P=0.799  F₁=0.542
CoNLL-2012 average score: 0.548Project Status!! The following papers have GitHub projects/code that are better scoring and may be a preferable solution.See the uploaded file in#1for a quick view of scores.VGAE as Cheap Supervision for AMR Coreference ResolutionEnd-to-end AMR Coreference ResolutionThis is a fork ofBrad Jascob'samr_corefrepository, and modified to address the multiprocessing issues on
non-Debian style OSs.  See#3for details on the issue.UsageTo turn multi-threading off, create theInterfaceinstance withuse_multithreading=False.Installation and usageThere is currently no pip installation. To use the library, simply clone the code and use it in place.The pre-trained model can be downloaded from the assets section inreleases.To use the model create adatadirectory and un-tar the model in it.The script40_Run_Inference.py, is an example of how to use the model.TrainingIf you'd like to train the model from scratch, you'll need a copy of theAMR corpus.
To complete training, run the scripts in order.10_Build_Model_TData.py12_Build_Embeddings.py14_Build_Mention_Tokens.py30_Train_Model.py.You'll needamr_annotation_3.0andGloVe/glove.6B.50d.txtin yourdatadirectoryThe first few scripts will create the training data indata/tdataand the model training
script will createdata/model. Training takes less than 4 hours.