Saravanan666/pypi-org huggingface dataset:
Package: containmint
containmintCreate multi-arch containers using native cloud builds.Q&AWhy another tool?Most existing tools and services rely on QEMU to perform container builds on other architectures.
These builds are much slower, often running 15x longer than native builds.Additionally, using customizable virtual machines allows for builds which dedicated build services may not support.How does it work?Ephemeral virtual machines are provisioned through the Ansible Core CI service[1].
These virtual machines are used to perform native container builds.
The resulting images are pushed to a container registry.After the container images are pushed, a manifest list referencing the container images is created.
The manifest list is then pushed to a container registry.Footnotes[1]Authentication is required.
An API key must be provided, or the tool must be run from an approved organization at a supported CI provider.Usage ExamplesConfigure container registry credentialsThe credentials[2]for the container registry[3]are set using environment variables:export CONTAINMINT_USERNAME = 'my-username'
export CONTAINMINT_PASSWORD = 'my-password'Footnotes[2]Use the--no-loginoption to allow operation without credentials.
This option is only usable when not pushing to a container registry.[3]Only one container registry can be used with each invocation.
Multiple repositories from the same registry can be used.Build and push a multi-arch containerThe following steps can be performed in parallel:containmint build --push --tag quay.io/my_org/scratch_repo:my_tag-x86_64 --arch x86_64
containmint build --push --tag quay.io/my_org/scratch_repo:my_tag-aarch64 --arch aarch64Once the steps above have been completed:containmint merge --push \
  --tag quay.io/my_org/final_repo:my_tag \
        quay.io/my_org/scratch_repo:my_tag-x86_64 \
        quay.io/my_org/scratch_repo:my_tag-aarch64This results in three tags:quay.io/my_org/final_repo:my_tag– This manifest list contains x86_64 and aarch64 images.quay.io/my_org/scratch_repo:my_tag-x86_64– This image is x86_64 only.quay.io/my_org/scratch_repo:my_tag-aarch64– This image is aarch64 only.
Package: contaminante
<p align=”center”><img width = “400” src=”https://github.com/christinahedges/contaminante/blob/master/docs/figures/logo.png?raw=true”/></p># contaminantekohn - tah - mee - nahn - teh[Read the docs here!](http://christinahedges.github.io/contaminante)A package to help find the contaminant transiting source in NASA’sKepler,K2orTESSdata. When hunting for transiting planets, sometimes signals come from neighboring contaminants. This package helps users identify where the transiting signal comes from in their data.## What doescontaminantedo?contaminanteuses pixel level modeling of theTargetPixelFiledata from NASA’s astrophysics missions that are processed with theKeplerpipeline. The output ofcontaminanteis a Python dictionary containing the source location and transit depth, and a contaminant location and depth. Optionally you can output a figure showingWhere the main target is centered in all available TPFs.What the phase curve looks like for the main targetWhere the transiting source is centered in all available TPFs, if a transiting source is located outside the main targetThe transiting source phase curve, if a transiting source is located outside the main targetAn example output is shown below for a target with a transiting contaminant.<center><img width = “900” src=”https://github.com/christinahedges/contaminante/blob/master/docs/figures/FP.png?raw=true”/></center>Where as a transit that is centered on the target gives the following output:<center><img width = “900” src=”https://github.com/christinahedges/contaminante/blob/master/docs/figures/real.png?raw=true”/></center>## How do I usecontaminante?You can check out our [tutorial](https://christinahedges.github.io/contaminante/_build/html/tutorial.html) for how to runcontaminante. To runcontaminanteyou will need a target name, a transit period, a transit center and a transit duration.## How do I installcontaminante?You can installcontaminanteusing pip:` pip install contaminante--upgrade`You can also installcontaminanteby cloning this repo:` git clonehttps://github.com/christinahedges/contaminantecd contaminante python setup.py install `### Help, I can’t installcontaminante!You might not be able to installcontaminantebecause your computer doesn’t support some of the features, or perhaps you’re new to Python. Don’t worry, you can still usecontaminante! If you’re struggling to install, try runningcontaminanteonline using Google’s Colaboratory. You can click [here](https://colab.research.google.com/github/christinahedges/contaminante/blob/master/tutorials/Colaboratory-Notebook.ipynb) to open a new Colaboratory notebook and runcontaminantein the cloud!## Dependenciescontaminanteuses the most up to date version of [lightkurve](https://github.com/keplerGO/lightkurve), and uses some of the features available in v2.0. Make sure yourlightkurveinstallation is up to date before using contaminante.
Package: contas-orama
contas_orama======================================|build-status-image| |pypi-version|DescriÃ§Ã£o-------------Sistema de controles de finanÃ§as pessoais.UsuÃ¡rio pode criar uma conta e fazer lanÃ§amentos na mesma.UsuÃ¡rio poderÃ¡ ver o extrato e consultar o saldo em perÃ­odos edatas arbitrÃ¡rias.Requerimentos---------------  Python-  Django-  Django REST FrameworkInstalaÃ§Ã£o------------InstalaÃ§Ã£o usa ``pip``\ â€¦.. code:: bash$ pip install contas_oramaUtilizaÃ§Ã£o----------1. Em settings.py adicione "contas_orama" e "rest_framework" em INSTALLED_APPS:.. code:: bashINSTALLED_APPS = [...'rest_framework','contas_orama',]Ainda em settings.py adicione o seguinte bloco de comando:.. code:: bashREST_FRAMEWORK = {'DEFAULT_PAGINATION_CLASS':'rest_framework.pagination.PageNumberPagination','PAGE_SIZE': 20}O cÃ³digo acima se refere a [paginaÃ§Ã£o](http://www.django-rest-framework.org/api-guide/pagination/#pagination).Dessa forma, em views onde ocorrem listagens de objetos, sÃ³ serÃ£o listados20 contas e/ou 20 lanÃ§amentos.Mudar o parÃ¢metro 'PAGE_SIZE' se desejar um nÃºmero diferente.2. Inclua a URL no urls.py do seu projeto... code:: bashurl('^contas_orama/', include('contas_orama.urls')),3. Crie os modelos ..... code:: bash$ python manage.py migrate4. Crie novos usuÃ¡rios (nÃ£o Ã© permitido acessar as contas sem login) ...... code:: bash$ python manage.py createsuperusers5. Visitar http://127.0.0.1:8000/contas/ para gerir as contas.Testando--------Para executar os testes:.. code:: bash$ python manage.py test contas_orama
Package: contaxy
Python Library TemplateWIP: Document python library.RequirementsPython 3.6+InstallationInstall via pip:pipinstall--upgrade-epath/to/libUsageAfter installation, the package can be imported:importtemplate_packageDevelopmentThis library usesblackfor auto-formatting,isortfor import sorting,flake8for linting,mypyfor type checking, andpydocstylefor docstring style checks. All code is written compatible with Python 3.6+, with type hints wherever possible.TestingDeploymentManager:By default, just the DockerDeploymentManager is executed. To execute also the Kubernetes-related tests, a Kubernetes cluster has to be available from where the code is executed. On your local Mac, you can simply start one by executingkind create cluster. Then execute the tests by setting the environment variableKUBE_AVAILABLE=true(in VS Code, you can for example set the env variable in thelaunch.jsonor append it in front of the pytest command, e.g.KUBE_AVAILABLE=true pytest...)
Package: contek-timbersaw
Timbersaw Logging Configuratortimbersawis a an automatic logging configurator.1. Quick Startimportloggingimportcontek_timbersawastimbersawtimbersaw.setup()logger=logging.getLogger(__name__)logger.info('foo bar')2. ResultsINFOlevel logging will be written to:{WORKING_DIRECTORY}/logs/info/{yyyy}-{MM}-{dd}.logERRORlevel logging will be written to:{WORKING_DIRECTORY}/logs/error/{yyyy}-{MM}-{dd}.log3. FeaturesCompressionWhen anINFOlog file passes its active period (defaults to when a new day starts), it gets GZipped. For example:2009-01-03.log -> 2009-01-03.log.gzRetentionOld logs will be deleted after their retention period. By default,INFOlogs are kept for 14 days andERRORlogs
are kept for 28 days.4. Default Settingslog_format%(asctime)s %(levelname)s %(filename)s:%(lineno)d - %(message)slog_date_format%Y-%m-%dT%H:%M:%Slog_root{WORKDING_DIRECTORY}/logslog_rollingMIDNIGHTlog_utcFalselog_info_retention_days14log_error_retention_days285. CustomizationSettings are configurable via environment properties. They can be set before your program starts usingexport. For
example:export log_error_retention_days=7Otherwise, they can be set at runtime usingos.environ. For example:importosos.environ['log_error_retention_days']='7'
Package: contemarlo
ConteMarlo
A “Monte-Carlo-Like” tester.https://pypi.python.org/pypi/contemarlo/-Ryan BirminghamThe concept is simple (and probably already done better): detailed Monte-Carlo
but without the randomness or memory dependence.
Despite the memory independence, generators default to safe mode not to flood memory.I to construct tests, so I know I don’t break things.Classes:Resolver - A generator for the next distribution value pair
Resolver_md - A multidimensional abstraction of Resolver
Distribution - A distribution, domain [0,1]
Package: contenda-api-client
contenda-api-clientA client library for accessing Contenda APIUsageFirst, create a client:fromcontenda_api_clientimportAuthenticatedClientclient=AuthenticatedClient(base_url="https://prod.contenda.io",token="SuperSecretToken")Note: You'll need to exchange a token for your API key first through the identity endpoint.Now call your endpoint and use your models:fromcontenda_api_client.modelsimportMyDataModelfromcontenda_api_client.api.my_tagimportget_my_data_modelfromcontenda_api_client.typesimportResponsemy_data:MyDataModel=get_my_data_model.sync(client=client)# or if you need more info (e.g. status_code)response:Response[MyDataModel]=get_my_data_model.sync_detailed(client=client)Or do the same thing with an async version:fromcontenda_api_client.modelsimportMyDataModelfromcontenda_api_client.api.my_tagimportget_my_data_modelfromcontenda_api_client.typesimportResponsemy_data:MyDataModel=awaitget_my_data_model.asyncio(client=client)response:Response[MyDataModel]=awaitget_my_data_model.asyncio_detailed(client=client)There are more settings on the generatedClientclass which let you control more runtime behavior, check out the docstring on that class for more info.Things to know:Every path/method combo becomes a Python module with four functions:sync: Blocking request that returns parsed data (if successful) orNonesync_detailed: Blocking request that always returns aRequest, optionally withparsedset if the request was successful.asyncio: Likesyncbut async instead of blockingasyncio_detailed: Likesync_detailedbut async instead of blockingAll path/query params, and bodies become method arguments.If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)Any endpoint which did not have a tag will be incontenda_api_client.api.default
Package: conteneurs
conteneursEnglish versionProject's descriptionconteneurs is a package to provide some containers's types. Those containers can be more suitable to your needs than python's built-in containers such as list, tuple or dict. For example, this package provide a ordered dictionary and a two-way table/two-dimensional array.
Currantly the package's documentation is writen in french, but the README file is also in english.French versionDescription du projetLe package "conteneurs" définit plusieurs classes d’objets conteneurs comme un dictionnaire ordonné ou un tableau à deux entrées. Ces classes permettent aux développeurs d’utiliser des conteneurs plus adaptés à leurs besoins que les conteneurs build-in de Python comme les types list, dict ou tuple.LicenseCreative Commons Attribution-NonCommercial (CC-BY-NC)©Cyprien BONTRON (c.b.e.python@gmail.com)
Package: contentalchemy
IntroductionWhy do we start this repo?Image processing: Snap HTML contents embedded math notations.Image snapper:Config:ENV VARS:CA_HEADLESS_URL: default is empty, you should set it a headless browser(I.e: Browserless, Browserstack, Chrominum, Chrome Stable)Code snippets:fromcontentalchemy.images.sync_api.snapperimportHTMLSnapperhtml_str="<p>abc</p>"snapper=HTMLSnapper()original_encoded=snapper.html_to_encoded_image(html_str)
Package: contentassistant
ContentAssistantRealize teste em massa e obtenha informações como:Intenção reconhecida;__
Entidades reconhecidas;__
Score__
Se encontrou alguma resposta no assistente de conteúdo;__
Resposta encontrada.__importContentAssistantCA=ContentAssistant.Test(AUTH,input)Para instanciar a classe, é necessário dois parâmetros:AUTH: String - Authorization/Key do bot.input: Lista ou pd.Series (coluna de DataFrame) - Frases para efetuar o teste.Uso com listaimportContentAssistantAUTH='KEY_DO_BOT'input=['texto1','texto2']CA=ContentAssistant.Test(AUTH,input)response=CA.run_test()Uso com pd.SeriesimportContentAssistantimportpandasaspdAUTH='KEY_DO_BOT'df=pd.read_csv('C:\Users\base.csv',sep=';',encoding='utf8')#Abrindo o arquivoinput=df['Frases']#Selecionando a coluna com as frasesCA=ContentAssistant.Test(AUTH,input)response=CA.run_test()Salvando o resultadoEscolha o caminho, nome do arquivo, tipo (csv,txt) e separador, e rode o código abaixo.response.to_csv(r'/minha_pasta/resultado_1.csv',sep=";",encoding='utf8')
Package: contentconvertor
Content-ConvertorContent-Convertor est un package python permettant de convertir des tweets en fichiers png prêts à être postés sur Instagram.InstallationPour l'installer, il suffit de lancer la commande :pip install contentconvertor.UtilisationImportez le package avec le code suivant :fromcontentconvertorimporttweet2gramast2gCe package comprends différentes fonctions.1. Se connecter à l'API de Twitterconnexion_to_api: permet de se connecter à l'API de Twitter. Il faut pour cela rentrer les clés d'API et les tokens d'accès. Pour les obtenir, il faut créer une application sur le site de Twitter.Liste des paramètres dans l'ordrebearer_token: clé d'API (Bearer Token)Retourne la connexion à l'API de TwitterCode d'exemple :api=t2g.connexion_to_api(bearer_token="")2. Récupérer l'ID de l'utilisateurget_user_id: permet de récupérer l'id d'un utilisateur. Il faut pour cela rentrer le nom de l'utilisateur.Liste des paramètres dans l'ordreapi: connexion à l'API de Twitter (obtenue avec la fonctionconnexion_to_api)username: nom de l'utilisateur (sans le @)Retourne l'id de l'utilisateurCode d'exemple :user_id=t2g.get_user_id(api,"iziatask")3. Récupérer les tweetsget_tweets: permet de récupérer les tweets d'un utilisateur. Il faut pour cela rentrer le nom de l'utilisateur et le nombre de tweets à récupérer.Liste des paramètres dans l'ordreObligatoire :api: connexion à l'API de Twitter (obtenue avec la fonctionconnexion_to_api)user_id: ID de l'utilisateur (obtenu avec la fonctionget_user_id)Optionnel :nb_tweets: nombre de tweets à récupérer (par défaut : 5)exclude: exclure les tweets de type "replies" ou "retweets" (par défaut : None)Retourne une liste de tweetsCode d'exemple :Minimum requislist_tweets=t2g.get_tweets(api,user_id)Avec tous les paramètreslist_tweets=t2g.get_tweets(api,user_id,nb_tweets=5,exclude=["replies","retweets"])4. Télécharger les tweetsdownload_tweets:permet de télécharger au format png les tweets d'un utilisateur.permet de télécharger au format png un tweet en particulier.Liste des paramètres dans l'ordreObligatoire :user: utilisateurtweet: (itérer sur la liste obtenue avec la fonctionget_tweets)
oulink: lien du tweetOptionnel :path: chemin où télécharger les tweets (par défaut : "")name: nom du fichier (par défaut : "tweet")mode: mode de capture d'écran (par défaut  : 0)nigh_mode: mode nuit (par défaut : 2)Retourne rien, vous aurez vos tweets téléchargés au format png, à l'endroit où vous avez lancé votre script.Code d'exemple :Avec la liste de tweets obtenue avec la fonctionget_tweetsMinimum requisfortweetinlist_tweets.data:t2g.download_tweets(user,tweet)Avec tous les paramètresfortweetinlist_tweets.data:t2g.download_tweets(user,tweets,path="images/",name="screenshot",mode=0,nigh_mode=2)Avec le lien du tweetMinimum requist2g.download_tweets(user,link="https://twitter.com/iziatask/status/1606364038195093504")Avec tous les paramètrest2g.download_tweets(user,link="https://twitter.com/iziatask/status/1606364038195093504",path="images/",name="screenshot",mode=0,nigh_mode=2)Exemple d'utilisationPour voir un exemple d'utilisation complet, rendez-vous ici :GitHub.
Package: contentcopy
ContentcopyContentcopy merges directory contents, deduplicating files based on their content.If a file with differing contents but the same name is encountered, the copied filename will automatically have a number appended, making it easy to see duplicate filenames.Usagepython3 contentcopy.py <source> <dest>e.g.python3 contentcopy.py old_pictures new_picturesWill copy all pictures in old_pictures that are not yet in new_pictures to new_pictures. After this you can safely delete old_pictures without worrying about data loss.Installationpip install contentcopy
Package: content-disposition
Content DispositionA library that allows easy management of 'Content-Disposition' details of HTTP responses.So far it provides:RFC 5987 (file response,https://datatracker.ietf.org/doc/html/rfc5987)InstallationInstall using pip:pip install content-dispositionUsageUse the rfc5987_content_disposition function to set an HTTP response's 'Content-Type' field in any endpoint, e.g.:# within views.py

from django.http import FileResponse
from rest_framework import viewsets
from rest_framework.decorators import action

from content_disposition import rfc5987_content_disposition

class MyViewSet(viewsets.ModelViewSet):
    ...

    @action(
        detail=True,
        methods="get",
        url_path=r"download",
    )
    def download_route(self, request, pk=None):
        """
        Assuming that self.get_object() returns a model defining 
        'name = models.CharField(...)', 'file = models.FileField(...)' and 'mime = models.CharField(...)'
        whereas 'mime' represents the correct mime_type related to 'file'
        """
        instance = self.get_object()

        response = FileResponse(
            instance.file,
            content_type=instance.mime,
        )
        response["Content-Disposition"] = rfc5987_content_disposition(instance.name)

        return response
    ...Run tests in PyCharmThe package comes with a Django test app to verify functionalities in a realistic environment. To locally run the
provided test cases, e.g. in your IntelliJ IDEA, you can:Configure the interpreterGo toFile->Settings->Project: content-disposition->Python interpreterAddaVirtualenv Environmentand configure the correctLocationand checkInherit global site-packages:Configure the Django frameworkGo toFile->Settings->Languages & Frameworks->DjangoCheckEnable Django Supportand configure the correct project root, settings and manage script location:Install the requirements by running:pip install -r requirements.txtin a terminal within your venvRight click on thetestsfolder and selectRun 'Test:'The "Run" window should list all (successfully) executed test cases:
Package: content-extractor-pi
content-extractor-piis a Python module which aims
to extract a certain piece of content defined by the user in a set of documents.
This piece of content can be a paragraph that deals with a certain topic,
headers, page numbers et cetera.content-extractor-pidoes need some examples of the desired
content, supplied by a domain expert, but our focus on few shot learning means ~10
examples is usually enough out a corpus that may contain 1000s of documents.InstallationThe easiest way to install content-extractor-pi is using pip:pip install content-extractor-piDocumentationThe main object of content-extractor-pi is ContentExtractor and its only attribute that it expects
is a pre-trained word embedding model. In the example I'm using the pre-trained
google news word-2-vec model availablehere.ContentExtractor.train_model methodThetrain_modelmethod extracts and scales features for the provided text examples contained
in train_df, creates synthetic samples of the target class, and trains
the model at the core of content_extractor.Parameterstrain_df: pandas DataFrame containing the text examples in one column and the corresponding
labels in the other onetrain_additional_features, default=None: pandas DataFrame containing additional features
describing the text examples contained in train_dfy_name, default="label": column name of train_df where the labels are storedtext_name, default="text": column name of train_df where the text examples are storeduse_pca, default=False: apply Principal component analysis to the scaled extracted features,
more info can be findhere.gamma, default=1: Kernel coefficient forsklearn.svm.SVCC, default=0.1: Regularization parameter forsklearn.svm.SVCContentExtractor.extract_content methodTheextract_contentmethod extracts and scales features for the provided text examples
contained in target_df and returns the ones labeled as 1 by the model.Parameterstarget_df: pandas DataFrame containing all the text examples that we have at disposaltarget_additional_features, default=None: pandas DataFrame containing additional features
describing the text examples contained in target_dftext_name, default="text": column name of target_df where the text examples are storedExampleIn the following example we have a full implementation that leads to extracting the desired
content from a set of more than 62000 paragraphs originated from the World Bank Loan Agreements
Corpus, using just 11 examples manually labeled as 1. We aim to extract theobjective paragraphthat is a short segment describing how the the loan it's going to be spent, below you can find an
example.The desired paragraphs are stored in thetarget_exampleslist. You can find train_df.csv and
target_df.csvherefromcontent_extractorimportcontextractorasctefromgensim.modelsimportKeyedVectorsimportpandasaspdW2V_MODEL=KeyedVectors.load_word2vec_format('/your/path/to/GoogleNews-vectors-negative300.bin.gz',binary=True)train_df=pd.read_csv("data/train_df.csv")target_df=pd.read_csv("data/target_df.csv")cont_ext=cte.ContentExtractor(W2V_MODEL)cont_ext.train_model(train_df)target_examples=cont_ext.extract_content(target_df)
Package: contentfetch
content-fetchThis package helps you parse the text from the webpage. The input to the function can be a path to an HTML file on your disk or a URL to a web page.You can install the package by running the following commandpip install contentfetchPlease refer to the code snippet for parsing the text from the HTML fileimport contentfetch

results_json = contentfetch.extract_content(html=<html_file>)

results_json

### OR

from contentfetch import extract_content

results_json = extract_content(html=<html_file>)

results_jsonPlease refer to the code snippet for parsing the text from the webpage through URLimport contentfetch

results_json = contentfetch.extract_content(url=<webpage_url>)

results_json

### OR

from contentfetch import extract_content

results_json = extract_content(url=<webpage_url>)

results_json
Package: content-filter
Python Content FilterContent Filter is a basic but robust content filter for python. Content Filter allows you to easily detect language in a message and offers great customizability.FeaturesAbility to have different levels of filtration.Can ignore repeated characters and infer certain characters aliases.Converts non-english characters to their english equivalents such as ç to c.Ignores non-printing characters.Easily and very customizable.Easy to get up and running.No third-party dependencies.InstallationPython 3.6 or higher is requiredTo install Content Filter, enter the following command in any sort of terminal window or command prompt.# Linux/OSXpython3-mpipinstallcontent-filter# Windowspy-3-mpipinstallcontent-filterLinksDocumentationContributingIf you are interested in contributing to Content Filter, please check out ourcontribution guidebefore you get started. Thanks for your interest!LicenseThe Content Filter module for Python is licensed under anMIT license.