Saravanan666/pypi-org huggingface dataset:
Package: onetrigger
OneTriggerA command-line tool to detect Onedata file events in order to trigger a webhook.InstallationOneTrigger requirespython3and thepython3-piptool. A package is available at the Python Package Index (PyPI) under the nameonetrigger, so you can easily install it executing:pip3installonetriggerYou can also download a binary in thereleasessection.UsageParameters can be passed via arguments or environment variables. All available commands and his parameters are described below:CommandrunSubscribe to file events.ArgumentEnvironment variableDescription-H HOST,--oneprovider-host HOSTONEPROVIDER_HOSTOneprovider hostname or IP.-t TOKEN,--token TOKENONEDATA_ACCESS_TOKENOnedata access token.-s SPACE,--space SPACEONEDATA_SPACEOnedata space.-w WEBHOOK,--webhook WEBHOOKONETRIGGER_WEBHOOKWebhook to send events.-f FOLDER,--folder FOLDERONEDATA_SPACE_FOLDERFolder to listen events (Optional).-i,--insecureONEPROVIDER_INSECUREConnect to a provider without a trusted certificate (Optional). Default:False.Commandlist-spacesList your available spaces in Oneprovider.ArgumentEnvironment variableDescription-H HOST,--oneprovider-host HOSTONEPROVIDER_HOSTOneprovider hostname or IP.-t TOKEN,--token TOKENONEDATA_ACCESS_TOKENOnedata access token.-i,--insecureONEPROVIDER_INSECUREConnect to a provider without a trusted certificate (Optional). Default:False.ExamplesSubscribing to file eventsonetriggerrun-Hexample.com-txxxxx-smy-onedata-space-whttp://example.com/webhook-fmy-folderDeploy on KubernetesOneTrigger can be deployed on Kubernetes using our public Docker Hub imagegrycap/onetriggerby applying a YAML file like this:apiVersion:apps/v1beta1kind:Deploymentmetadata:name:onetriggerspec:replicas:1spec:containers:-name:onetriggerimage:grycap/onetrigger:latestimagePullPolicy:Alwaysenv:-name:ONEPROVIDER_HOSTvalue:"example.com"-name:ONEDATA_ACCESS_TOKENvalue:"xxxxx"-name:ONEDATA_SPACEvalue:"my-onedata-space"-name:ONETRIGGER_WEBHOOKvalue:"http://example.com/webhook"-name:ONEDATA_SPACE_FOLDERvalue:"my-folder"Event formatWhen a new file is created inside the space (or the specified folder) a JSON formatted event is sent to the webhook following the structure of the example shown below:{"Key":"/my-onedata-space/files/file.txt","Records":[{"objectKey":"file.txt","objectId":"0000034500046EE9C67756964233836666330363031303664303964623739666562393165336632306232613736236664323861626330656664643566313938313333336633356232333838623137","eventTime":"2019-02-07T09:51:04.347823","eventSource":"OneTrigger"}]}
Package: onetsdb
onetsdbA Uniform Interface for Timeseries Database Python Library. Will Support MongoDB, InfluxDB, SQLITE3 etc.Installpip install onetsdbUsagefromonetsdbimportconnect,TSDBPoint# tsdb = connect('mongodb://localhost/tsdb')# tsdb = connect('influxdb://localhost/tsdb')# tsdb = connect('sqlite3://localhost/tmp/tsdb.sqlite3')  # file: with/tmp/tsdb.sqlite3,# tsdb = connect('tslite://localhost/tmp/tslite/test')  # tslitetsdb=connect('sqlite3://localhost/file::memory:')# with memory sqlite3tsdb.register_table('device',{'tags':{# Tags can be a filter for querying'devid':'string',# Device ID},'fields':{'temp':'float',# Temperature value'humi':'float',# humidity value}})tsdb.write_point('device',TSDBPoint(data={'devid':'A1','temp':23.5,'humi':45.5}))tsdb.write_point('device',TSDBPoint(data={'devid':'A2','temp':20.2,'humi':35}))tsdb.write_point('device',TSDBPoint(data={'devid':'A1','temp':24.5,'humi':50}))printtsdb.query('device').filter(devid='A1').all()Click to view more information!CHANGES1.0.0first version1.2.0support mongodb, influxdb, sqlite31.2.4fix sqlite3 bug1.3.0support tslite1.3.1mongodb support time_group, values and aggregate oprations
Package: oneup
OneUpA CLI tool to check for dependency updates for Python, in Python.What's this?oneupis a simple command-line interface to aid developers in determining the most recent version of their project's dependencies, as specified in files such asrequirements.txtandpyproject.toml.Right now, the tool can parse your dependency lists and report the latest version of all your dependencies to the standard output. In the future, the tool might add some other features such as: automatically updating your lists with a latest version, if desired, and only showing the latest version of dependencies if they differ from your currently specified version (or range).InstallationYou can use your Python package manager (e.g.pip) to installoneup.pipinstalloneupUsageoneupcomes with a command-line interface and will automatically detect any supported dependency files in the current directory:oneupYou can also specify which file to check:oneup--filepath/to/requirements.txtA complete list of arguments and flags can be found by running:oneup--helpContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate; a minimum coverage of 80% is expected (and enforced by Github Actions!).LicenseThis project is licensed under theGNU Affero General Public License v3.0.
Package: oneupsdk
OneUp Learning SDKThis is a Python SDK to allow for the scripting and integration of theOneUp Learning platformdeveloped by West-Salem State University.InstallationThe package is available through Python's package management system:pipinstalloneupsdkConfigurationYou can request an account by contactingDarina Dicheva. Once you have an account, create aconfig.yamlfile containing your authentication information. This file is parsed by the SDK to authenticate your API calls.oneup:username:"username"password:"OneUP-P4ssW04d!"UsageBelow are the macros that are available from the subpackageoneupsdk.integration. Note that a call toset_active_course()must be made before most of the other calls will work.Coursesget_instructor_courses()set_active_course(course_id)get_active_course()Studentsget_enrolled_students()get_student_by_id(user_id)get_student_by_username(username)add_student(email, password, first=None, last=None, user_id=None)modify_student(username, email=None, password=None, first=None, last=None, new_user_id=None)delete_student(user_id)Activitiesget_activity_by_id(activity_id)create_activity(name, **kwargs)modify_activity(activity_id, **kwargs)post_activity_points(activity_id, data, as_dict=False)delete_activity(activity_id)Activity categoriesget_activity_categories()get_default_activity_category()create_activity_category(name)delete_activity_category(category_id)ReferencesDicheva, Darina, Keith Irwin, and Christo Dichev. "OneUp learning: a course gamification platform." InInternational Conference on Games and Learning Alliance, pp. 148-158. Springer, Cham, 2017. (link)Dicheva, Darina, Keith Irwin, and Christo Dichev. "OneUp: Supporting Practical and Experimental Gamification of Learning."International Journal of Serious Games5, no. 3 (2018): 5-21. (link)Dicheva, Darina, Keith Irwin, and Christo Dichev. "OneUp: Engaging Students in a Gamified Data Structures Course." InProceedings of the 50th ACM Technical Symposium on Computer Science Education, pp. 386-392. 2019. (link)
Package: oneutil
One Squared Utilitiesoneutil is a Python package that provides useful utilities for work we do at One Squared.InstallationTo install oneutil, you can get it from PYPI:pipinstalloneutilFor testing, you can install it locally viapythonsetup.pysdistbdist_wheel
pipinstall-e.Usagefromoneutil.etl.awsimportget_s3_bucket_files,get_s3_bucketsfromoneutil.etl.databentoimportget_df_from_s3# List files in the default S3 bucket in 'us-east-1' regionfiles=get_s3_bucket_files()# List all available S3 buckets in 'us-east-1' regionbuckets=get_s3_buckets()# Read a databento filedf=get_df_from_s3(get_s3_bucket_files()[-1])oneutil relies on the AWS SDK (boto3) for interacting with AWS S3. To use the package, make sure you have valid AWS access credentials (access key ID and secret access key) set as environment variables or provide them explicitly when calling the functions.s3_public_key: The AWS access key ID.s3_private_key: The AWS secret access key.If the environment variables are not set, you can pass the credentials directly as arguments to the functions.
Package: one-utils
OneUtilsA collection of useful tools for oneflowInstallationpipinstallone-utils==0.0.2UsageWeight Transferfromone_utilsimportconvert_torch_to_flowmodel=model()torch_weight="path/to/pytorch_pretrained_weight.pth"save_path="path/to/save"convert_torch_to_flow(model,torch_weight_path,save_path)Eval Pytorch Model Acc on ImageNetfromone_utilsimporteval_torch_accmodel=model()data_path="path/to/imagenet/val"eval_torch_acc(model,data_path,n_gpu_use=1)Eval OneFlow Model Acc on ImageNetfromone_utilsimporteval_flow_accimportmultiprocessingasmpmp.set_start_method("spawn")model=model()data_path="path/to/imagenet/val"eval_flow_acc(model,data_path)
Package: onevox
Small library for adding well described noise to images, with a command-line
utility specifically for application on Nifti formatted images.InstallationSimple! Just open your favourite terminal and type:$ pip install onevoxAlongside installing the oneVoxel package, this will also ensure the
dependencies are installed:numpy,scipy,nibabel, andnilearn.For building this in Docker, you can run the following command:$ docker build -t gkiar/onevox:local --network host .UsageFrom within Python this library can used to apply noise to arbitrary images:[1]: import numpy as np
[2]: np.random.seed(1234)
[3]: data = np.random.random((10,10,10))  # Create data matrix
[4]: mask = data[:,:,0] > 0.4  # Define mask as values higher than 0.4 in the first 2D slice

[5]: from onevoxel import noise  # Load noise utils

[6]: # Generate noise locations from image and mask
[7]: loc = noise.generate_noise_params(data, mask, erode=0, mode='independent')
[8]: loc
[(3, 6, 0),
 (4, 4, 1),
 (9, 0, 2),
 (7, 9, 3),
 (1, 2, 4),
 (3, 1, 5),
 (0, 7, 6),
 (9, 0, 7),
 (3, 4, 8),
 (1, 9, 9)]

[9]: # Apply noise to the image and verify it's in the right spot
[10]: noisy_data, noisy_hash = noise.apply_noise_params(data, loc, scale=True, intensity=0.01)
[11]: sorted(list(zip(*np.where(noisy_data != data))), key=lambda elem: elem[2]) == loc
TrueContributingExcited by the project and want to get involved?!Pleasecheck out ourcontributing guide, and look through theissuesto start seeing where
you can lend a hand. We look forward to approving your amazing contributions!
Package: oneworld
OneWorldWelcome to OneWorld, a python mapping library intended to make plotting
maps with python easy and accessible to everyone. It combines the
mapping power ofcartopywith
the versatility ofleafletand
the aesthetics and input structure ofseabornto create both static and interactive maps.OneWorld accepts data inputs as sequences (lists or tuples) orpandasdataframes, much in the same
way asseaborndoes. If you have used seaborn
before, you already know how inputs work in OneWorld.InstallationYou can install OneWorld throughpip, provided you satisfy the
requirements listed below:$ pip install oneworldYou can find the source code in Bitbuckethttps://bitbucket.org/taguilar/oneworldYou'll need the following packages to use OneWorld :matplotlib3.6.0 (or later)pandas1.5.2 (or later)seaborn0.12.2 (or later)cartopy0.21.1 (or later)jinja23.1.2 (or later)Note: Depending on the package manager
that you use to installcartopy, you may have to
manually installfiona(1.8.13 or later) and python3-dev as well.DocumentationThe full documentation packaged with OneWorld can be foundhere. It can also be found
in Read the Docs athttps://oneworld.readthedocs.io.
It includes an introduction, tutorials and API reference.LicenseThis software is distributed under a MIT license.
Package: onewot
OnewotAn opinionated, static typed WotBlitz API wrapper for Python3.Python >=3.8 are currently supported.Documentation:View HereInstallationInstall Onewot from PyPi with the following command:python-mpipinstall-Uonewot# Windows users may need to use this instead...py-3-mpipinstall-UonewotUpdatingpipinstall--upgradeonewotStart up clientimportonewotclient=onewot.WOTBClient(application='...')Exampleimportonewotclient=onewot.WOTBClient(application='...',language=onewot.Language.ENGLISH)print(client.fetch_user(152870490))Python optimization flagsCPython provides two optimisation flags that remove internal safety checks that are useful for development, and change other internal settings in the interpreter.python main.py - no optimisation - this is the default.python -O main.py - first level optimisation - features such as internal
assertions will be disabled.python -OO main.py - second level optimisation - more features (including
all docstrings) will be removed from the loaded code at runtime.A minimum of first level of optimizationsis recommended when running applications in a production environment.
Package: onex
Installation$ pip install onex && onexFeaturesonex, ** Onex manage more than 400+ hacking tools that can be installed on single click. **How to install ?$ pkg install python -y
$ pip install onex
$ onex
$ onex startDisclaimerWe are not responsible for your loss.
this tool is made for Education/Learning Purpose.
Dont harm anyone using this tool.Usage$ onex

Author : rajkumardusad
PIP module : Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.comGithub
Package: onf-parser
Introductiononf-parseris a lightweight pure Python library for parsing the OntoNotes Normal Form format
(.onf– cf.section 6.3).InstallationNote that Python >=3.7 is required due to our dependency ondataclasses.pipinstallonf-parserUsageThere are three top-level functions:fromonf_parserimportparse_files,parse_file,parse_file_string# read a single filesections=parse_file('ontonotes/some/file.onf')# or parse a raw stringsections=parse_file_string(s)# read all .onf files in a single directoryfiles=parse_file('ontonotes/')For each file, a list ofSectionobjects (which correspond to documents for the purposes of annotation) will
be available:forfilepath,sectionsinfiles:forsectioninsections:coref_chains=section.chainsforchainincoref_chains:print(chain.type)print(chain.id)print(chain.mentions)formentioninchain.mentions:print(mention.sentence_id)print(mention.tokens)forsentenceinsection.sentences:print(sentence.plain_sentence)print(sentence.plain_sentence.string)print(sentence.treebanked_sentence)print(sentence.treebanked_sentence.string)print(sentence.treebanked_sentence.tokens)print(sentence.speaker_information)print(sentence.speaker_information.name)print(sentence.speaker_information.start_time)print(sentence.speaker_information.stop_time)print(sentence.tree)print(sentence.tree.tree_string)print(sentence.leaves)forleafinsentence.leaves:print(leaf.token)print(leaf.token_id)# NERprint(leaf.name)print(leaf.name.type)print(leaf.name.token_id_range)print(leaf.name.tokens)# Coreferenceprint(leaf.coref)print(leaf.coref.type)print(leaf.coref.token_id_range)print(leaf.coref.tokens)# WordNet senseprint(leaf.sense)print(leaf.sense.label)# PropBankprint(leaf.prop.label)print(leaf.prop)forarg_label,arg_spansinleaf.prop.args.items():print(arg_label)forarg_spaninarg_spans:print(arg_span)Seemodel classesfor more information.
Package: ongair-whatsapp
# ongair-whatsapp## Introductionongair-whatsapp is python package that was built on top of Yoswsup to support Ongair’s platform. ongair-whatsapp is
on top of yowsup layers.## Requirementsongair-whatsapp has been tested and known to work on Python27 ; PyPi 4.0## InstallationI bet you are using [virtualenv](http://www.virtualenv.org/en/latest/index.html) ,right?At the command line , install ongair-whatsapp using eitherpip(recommended)` pip installongair-whatsapp`oreasy_install` easy_installongair-whatsapp`## Project Structure` bash ├── CONTRIBUTING.md ├── LICENCE ├── MANIFEST.in ├── README.md ├── dist │   └──ongair-whatsapp-1.0.0.tar.gz├── logs ├── ongair │   ├── __init__.py │   ├── client.py │   ├── models.py │   ├── notification.py │   ├──ongair-example.conf│   ├── ongair.py │   ├── stack.py │   ├── starter.py │   ├── util.py ├──ongair-cli├──ongair-health-cli├──ongair_whatsapp.egg-info│   ├──PKG-INFO│   ├── SOURCES.txt │   ├── dependency_links.txt │   ├── requires.txt │   └── top_level.txt ├── requirements.txt ├── setup.cfg ├── setup.py └── tmp `## Scriptsongair-whatsapp comes with a launch script`ongair-cli`## Setup`pip install-rrequirements.txt`## CreditsShoutouts to Yowsup for inspriting ongair-whatsapp## Licenceongair-whatsapp is distributed under the GPL V3 Licence
Package: onhold-ding
🔊 Play sounds after shell jobs completedingis command-line utility that will play a sound after a long job completes.The utility will take data that is piped into its standard inputs and pipe it to standard output. That is to say that data piped intodingwill be piped right back out.$echo"Hello!"|ding
Hello!As a result, you can build pipelines withding.For example, you can download an ISO withhttp, visualize the progress withpv, and when it's finished, play a sound withding.$exportURL="https://releases.ubuntu.com/20.04.1/ubuntu-20.04.1-desktop-amd64.iso"$http"$URL"|pv|ding>/dev/nullThis project usesplay_sounds, a wrapper overplaysoundandboombox.dingYou can either set the$DINGenvironment variable to the sound you'd like to play, or supply the sound with the-sflag.# You can run ding after a command or as part of a pipeline$exportDING="~/Music/ding.ogg"$sleep5;ding
$echo"Hello!"|ding
Hello!This allows you to set$DINGin your~/.bashrc.You can also specify it with a flag.$echo"Hello!"|ding-sding.ogg
Hello!dingcomes with a default sound that will play if neither$DINGor-sare set. You can use the-wflag to show warnings if$DINGor-sare not set.$echo"Hello!"|ding
Hello!InstallationDependenciesA Unix shell like BashPython 3.6+requirements.txtLinuxGStreamerOn Ubuntu, you will need to installPyGObject,gstreamer1.0-python3-plugin-loaderandpython3-gst-1.0.sudoaptinstallpython3-gigstreamer1.0-python3-plugin-loaderpython3-gst-1.0PyPI$python3-mpipinstallonhold_dingGitHub$python3-mpipinstall-rrequirements.txt
$python3setup.pyinstallHelpding$ding--help
Usage:ding[OPTIONS]Playspecifiedsoundafterjobiscomplete.

Options:-s,--sound_pathPATHPathtosoundtoplay.-i,--ignoreSuppresswarnings.--helpShowthismessageandexit.LicenseSeeLICENSE. If you'd like to use this project with a different license, please get in touch.CreditMusicSeeCREDIT.md.
Package: oni-api
OniDev's Helper Para Consumo das API'sRetorna objeto json da requisição das API's trabalhadas até o momento.A documentação completa do pacote está disponível em:Pypi:oni_api.Como testar sua contribuíção antes de enviar uma PRPrimeiramente faça a desinstalação do pacote presente em suavenv.pipuninstalloni_apiApós a desinstalação, instale o pacote atráves do código fonte modificado.pipinstall-e<path>/oni_api/Teste a implementação feita em um projeto terceiro ou até mesmo usando os códigos de teste disponíveis. Após testar o código atual faça a desinstalação do pacote novamente.pipuninstalloni_apiApós o teste crie sua PR e envie para avaliação.
Se deseja que sua PR vire uma nova release, não se esqueça de mudar nosetup.pyna opção version adicione o nome da
próxima versão proposta para o pacote.Como criar uma nova release do projetoUma vez que sua contribuição for aprovada via PR você está preparado para criar uma nova release do projeto.O primeiro passo é criar uma nova tag com base no commit que foi aprovado. Para isso na branch master execute o comando:gittag-a<tag_name>-m"<comentário descritivo da tag>"Exemplos detag_name:v1.2,v1.2.3ouv1.2-betaCom a tag criada faça o seu envio para o repositório noGitHub.gitpushorigin<tag_name>Com a tag enviada podemos seguir para a criação de uma release.
Essa pode ser criada diretamente pelo github com os seguintes passos:Vá no repositório do projeto no GitHub na aba Code.Acesse o menu Releases presente, normalmente, no lado direito.Clique na opção Tags para ver a listagem de tags.Clique nos três pontinhos a direita na tag que você acabou de criar.Selecione a opção create release.Adicione um nome na release (pode ser o mesmo nome da tag) e uma descrição para as novas features da tag.
Package: onica-iotloader
A tool to load bulk simulation data into AWS IoT Analytics for experimenting with the capabilities of AWS IoT Analytics. This tool is intended to load simulation data that can then be utilized to explore the capabilities of Datasets, integration with Amazon QuickSight, etc.Installation:Make sure you have a working Python environmentpip install onica-iotloaderto get the latest version installedTemplateA user-provided template is invoked to create each sample message. Arbitrary Python code can be used to generate each message via this template, enabling a rich set of simulated values. The template file must set a local variable namedmessageto the value of a single simulated message, each time it is invoked. Seetemplate.pyin this repository as an example of a static, but very large (~ 127kb) message template.Usageonica-iotloader [--concurrency=<n>] <channel> <template> <count>A concurrency value of 1000 seems to be ideal to maximize the ingest rate of AWS IoT Analytics from a single source (achieving approximately 3200 msgs/sec, or 400 mb/sec on an m5.24xlarge). The<channel>must be an existing AWS IoT Analytics channel,<template>is the template Pyton script described above, and<count>is the number of messages (within a margin of error) to transmit. For reference, a count of 900000 results in just over 100GB of ingest using the defaulttemplate.pyincluded herein.To load about 100GB in 4 minutes, run the following command using the template.py included in this repository (tested on m5.24xlarge):onica-iotloader --concurrency=1000 <channel> template.py 900000Replace<channel>with the name of an AWS IoT Analytics Channel configured in your account.AWS Credentials & RegionThe default boto3 AWS credential & region resolution (env vars, EC2 metadata, config file, etc) is utilized.FeedbackContact us at opensource at onica.com, or via Github issues, with feedback!
Package: onicalc
OniCalcOniCalc is a Python library of various calculators for the survival simulation video gameOxygen Not Included. It supports Python 3.8+.Not official Klei product. Not approved by or associated with Klei.FeaturesColony food calculator for figuring out raw resources required for any number of dupesSpaced Out! foods currently in beta (ratios not 100% confirmed)InstallationInstall my-project with npmpipinstallonicalcUsage/Examples>>>fromonicalcimportfoodcalc>>>foodcalc(4,"Gristle Berry"){'Gristle Berry':2.0,'Bristle Blossom':12.0,'Water':240.0}