Saravanan666/pypi-org huggingface dataset:
Package: g-checker-for-itf
g-checker-for-itfITF の卒業要件を確認したい。(今は coins19/coins20/coins21, mast20 だけ)提供されているプログラム、またそのプログラムによる実行結果に関する保証はできかねます。InstallClone Repositorygitclone--depth1https://github.com/oshamashama/g-checker-for-itfcdg-checker-for-itf
pipinstall.pip installpipinstallg-checker-for-itfのいずれかでgchkコマンドをインストールできます。
今時点で要件確認のためのファイルをこのリポジトリからダウンロードしてくる必要があるため、 Clone によるインストールを推奨します。Get Requirements Filewgethttps://raw.githubusercontent.com/oshamashama/g-checker-for-itf/main/coins20.jsonなどで卒業要件を定義したファイルをダウンロードしてきてください。Usagegchk-itarget_csv_file-rrequirements_json_fileで実行できます。target csv fileは twins の成績ページからダウンロードしたファイル (UTF, CSV) を想定しています。$ gchk
usage: gchk [-h] [-i INPUT] [-r REQUIREMENTS] [-g] [-d] [-n] [-s] [-e] [-V]

A checker if your credits meet the graduation requirements or not.

optional arguments:
  -h, --help                  show this help message and exit
  -i INPUT, --input INPUT     target file from twins (UTF-8, CSV) (default: sample.csv)
  -r REQUIREMENTS, --requirements REQUIREMENTS
                              requirements file (default: coins20.json)
  -g, --gpa                   print GPA (default: False)
  -d, --drop                  print drop credit (default: True)
  -n, --name                  print name and id (default: False)
  -s, --save                  save as JSON (default: False)
  -e, --expect                count 履修中 (default: False)
  -V, --version               show program's version number and exitTestgchk-isample.csv-rcoins20.jsonで予め用意されたサンプルで動作を確認することが出来ます。wgethttps://raw.githubusercontent.com/oshamashama/g-checker-for-itf/main/coins20.json
wgethttps://raw.githubusercontent.com/oshamashama/g-checker-for-itf/main/sample.csvViewer現状、このリポジトリを clone することが必要です。gchk-s-itarget_csv_file
cpsaved_file~/g-checker-for-itf/tani/src/grade.jsoncdtani
npminstall
npmstarthttp://localhost:3001/にアクセスすると確認できます。Screenshots
Package: gcigps
gciGpswork in progressMIT LicenseCopyright (c) 2023 The Guild of Calamitous IntentPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: gc-ims-tools
gc-ims-toolsPython package to handle and analyze GC-IMS data.
The aim is to implement common data specific routines to fully utilize the existing data science ecosystem for chemometrics.Installationgc-ims-toolsrequieres Python 3.8+ and can be installed with pip:pip install gc-ims-toolsDocumentationDetailed documentation can be found at:https://charisma-mannheim.github.io/gc-ims-tools/build/html/index.htmlExample datasetAn example GC-IMS dataset on the classification of olive oils by geographical origin can be found at:https://data.mendeley.com/datasets/fr9t5fkkvz/2CitationIf this project is helpful for your work citing the following publication would be appreciated:Christmann, Joscha; Rohn, Sascha; Weller, Philipp (2022): gc-ims-tools – A new Python package for chemometric analysis of GC–IMS data. In: Food chemistry 224 (4), S. 133476. DOI: 10.1016/j.foodchem.2022.133476.
Package: gci-validator
# gci-csv-validatorCSV validator for the [Google Code-in Web API](https://developers.google.com/open-source/gci/api)### Installation```pip install gci-validator```### Usage```gci-validator.Usage:gci-validator (FILE) [options]Options:-h --help    Show help text.-v --version Show program version.-b=<bvals>   Define boolean values. Separated by commas [default: true, false].```### Todo- [ ] Add more flexible command-line options- [ ] Verbose validation output- [x] Custom boolean flags- [ ] Custom selection of tags- [ ] Custom meta-data validator- [x] Add validation logic to 'status' field so that it can only be set to "Published" (2) if the below fields are entered validly:- 'name'- 'description'- 'max_instances'- 'categories'- 'time_to_complete_in_days'- 'mentors'
Package: gc-kafka-python
>>> pip install kafka-pythonkafka-python is a client for the Apache Kafka distributed stream processing
system. It is designed to function much like the official java client, with a
sprinkling of pythonic interfaces (e.g., iterators).KafkaConsumer>>> from kafka import KafkaConsumer
>>> consumer = KafkaConsumer('my_favorite_topic')
>>> for msg in consumer:
...     print (msg)KafkaConsumer is a full-featured,
high-level message consumer class that is similar in design and function to the
new 0.9 java consumer. Most configuration parameters defined by the official
java client are supported as optional kwargs, with generally similar behavior.
Gzip and Snappy compressed messages are supported transparently.In addition to the standard KafkaConsumer.poll() interface (which returns
micro-batches of messages, grouped by topic-partition), kafka-python supports
single-message iteration, yielding ConsumerRecord namedtuples, which include
the topic, partition, offset, key, and value of each message.By default, KafkaConsumer will attempt to auto-commit
message offsets every 5 seconds. When used with 0.9 kafka brokers,
KafkaConsumer will dynamically assign partitions using
the kafka GroupCoordinator APIs and a RoundRobinPartitionAssignor
partitioning strategy, enabling relatively straightforward parallel consumption
patterns. SeeReadTheDocsfor examples.KafkaProducer<in progress - see SimpleProducer for legacy producer implementation>ProtocolA secondary goal of kafka-python is to provide an easy-to-use protocol layer
for interacting with kafka brokers via the python repl. This is useful for
testing, probing, and general experimentation. The protocol support is
leveraged to enable a KafkaClient.check_version() method that
probes a kafka broker and attempts to identify which version it is running
(0.8.0 to 0.9).Low-levelLegacy support is maintained for low-level consumer and producer classes,
SimpleConsumer and SimpleProducer. SeeReadTheDocsfor API details.
Package: gcl
gcl -- Generic Configuration Language=====================================GCL is an declarative modeling language that can be dropped into any Python project. It supportsdictionaries with name-value pairs, all the basic types you'd expect, lists, includes, and methodsfor abstraction.The goal for GCL is to be a modeling language with lots of expressive power, intended to makecomplex configurations extremely DRY. Behavior is not part of the goal of the language; behavior andsemantics are added by scripts that interpret the GCL model.Documentation-------------Detailed documentation is available at [http://gcl.readthedocs.org](http://gcl.readthedocs.org)Quick GCL showcase------------------GCL is built around named tuples, written with curly braces:# This is a comment# Various data types:number = 1;string =  'value';  # Strings can be doubly-quoted as wellbool =  true;       # Note: lowercaseexpression = number * 2;list = [ 1, 2, 3 ];Expressions:a = 1 + 1;b = 'foo' + 'bar';c = 80 * '-';d = inc(1);  # Function applicatione = inc 1;   # Can omit parens with 1 argument# Conditionalsallow_test_commands = if stage == 'alpha' then true else false;# List comprehensionevens = [ x * 2 for x in [1, 2, 3, 4, 5] if x % 2 == 0 ];Tuples and accessing tuple members:tuple = {foo = 3;}that_foo = tuple.foo;Includes and tuple composition:http = include 'library/http.gcl';server = http.Server {port = 8080;}Using the library-----------------You now know enough GCL to get started. Using the library looks like this:import gclfrom gcl import util# Load and evaluate the given filemodel = gcl.load('myfile.gcl')# This gives you a dict-like object back, that you can just indexprint(model['element'])# Translate the whole thing to a Python dict (for example to convert to JSON)dict_model = util.to_python(model)import jsonprint(json.dumps(dict_model))Requirements------------* Uses `pyparsing`.Extra-----* Vim syntax definitions available: https://github.com/rix0rrr/vim-gcl
Package: gclang
GuardedGuarded is the interpreter of Dijkstra's guarded command language,
written in python with ANTLR4 generator.Example of program in this language:a := 45;
b := 15;

do a != b ->
    if a > b -> a := a - b
    |  a < b -> b := b - a
    fi
odQuick start:To start using interpreter, follow these steps:Install gclang:$pipinstallgclangLaunch program of your choice with:$gclang./examples/euqclid.guarun$gclang./examples/euqclid.guaderive
Package: gclassifier
GENDER-CLASSIFIERGender classifier is a python libary written over a classification model which can classify gender of the human inside the imageClassify gender from a imageModel train on tensorflow (CNN)Building for sourceFirst install gclassifier from pip:>>>pipinstallgclassifierRun the drawing code:Step 1 :  Import module for classifier>>> from gclassifier import image_classifier
>>> import cv2Step 2 :  Runn the classifier>>> img = cv2.imread('7.jpg')
>>> a = image_classifier(frame=img)
['woman']LicenseMITPYPI MODULEhttps://pypi.org/project/gclassifier/NOTE :This modlue have been inspired from - https://www.youtube.com/watch?v=WOuAI5DhHyU

Author: Dip Ghosh
email: dipghoshraj@gmail.com
IOT and robotics developer with a handson experience in Software development and DevOps.
Package: gclfs
Git Cloud Large File StorageGit LFS for large files that need cloud storage.SetupAWS S3Install AWS CLI.Create anS3 bucketandAWS access keys.Put the access keys in~/.aws/credentialsas describedhere.
Note, the profile used by gclfs isgclfs. This can be overridden ingclfs.config. See example below.Add bucket name togclfs.config.Example credentials file:# ~/.aws/credentials[default]aws_access_key_id=AKIAASSFODNN7EXAMPLEaws_secret_access_key=wJalrP0dnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY[gclfs]aws_access_key_id=BTIAIOSFODNN7EXAMPLEaws_secret_access_key=xyalrXUghdEMI/K7MDENG/bPxRfiCYEXAMPLEKEYExample config file with overridden profile name:# gclfs.config[s3]bucket=my-awesome-bucketprofile=defaultUsageTrackingTo track files with a specific extension that will be uploaded to cloud storage, rungcl track *.<file_extension>For example, to track wav files:gcl track "*.wav"This adds the extension to.gitattributes. When pushing to remote repo, this will sync all wav files to S3.Important: To keep the large files from getting committed to the git repo, you still have to add them to.gitignore.CommandsYour normal git commands work here and a remote git repo should be setup as usual
(withgit initandgit remote add, etc.).Examplegcl init(initialize repo)gcl track "*.wav"(track wav files)gcl add .(add files to commit)gcl status(get repo status)gcl commit -m "init commit"(commit changes)gcl remote add origin git@github.com:me/my-awesome-project(add remote repo)gcl push(push git repo and sync files with cloud)gcl fetch(fetch changes)gcl pull(pull remote changes and sync files with cloud)LimitationsCurrently, only AWS S3 is supported.LicenseSee theLICENSEfile for license rights and limitations (MIT).
Package: gclick-sdk
GClick SDKSDK Python para integração com a plataforma de controle de processos GClick.Este SDK foi construído com base nadocumentação oficial das APIs.Features disponíveis (por classe)GClickCredentials- Classe de controle da autenticação com o portal do GClick.AtividadesClient- Classe para manipulação das atividades na plataforma.gclick_sdk.credentials.GClickCredentialsControle de autenticação com o portal do GClick (obtenção de access_token, pelo protoco OAuth). Disponibiliza os seguintes métodos públicos:Construtor padrãoRetorna uma instância da classe, necessária para construção das classes "client" (para manipulação de dados na plataforma).Parâmetrossistema: Código do sistema informado pelo G-Click (para integração).usuario: Apelido do usuário no sistema parceiro correspondente ao apelido do usuário no G-Click.conta: Conta de acesso do usuário junto ao GClicksenha: Senha de acesso do usuário junto ao GClickempresa: Código da empresa parceira provido pelo GClick, para integração.client_id: ID da aplicação cliente cadastrada no servidor de autenticação.client_secret: Chave da aplicação cliente cadastrada no servidor de autenticação.gclick_sdk.atividades_client.AtividadesClientClient para invocação dos endpoints destinados à manipulação de Atividades no GClick.baixar_upload()Realiza a baixa de atividade, realizando também upload de arquivo associado.RetornoMétodo sem retorno esperado (ver sessão de exceções para o caso de erro no processamento).Parâmetrosid_atividade: ID da atividade.inscricao: Inscrição do cliente do escritório contábil (CPF ou CNPJ).competencia: Data da competência no formato AAAAMM.file_content: Conteúdo, em bytes, do arquivo a sofrer upload.file_name: Nome do arquivo sofrendo upload.file_mimetype: Mimetype do arquivo sofrendo upload.usuario [opcional]: Apelido do usuário no sistema parceiro.Obs.: Se o parâmetro "usuario" não for passado, será assumido o usuário recebido com o objeto de credenciais (no construtor da classe).ExceçõesBaixadaAnteriormenteException: Indica que a atividade já foi baixada anteriormente (não podendo ser baixada diretamente, apenas atualizada, o que ainda não está implementado).UnauthorizedException: Erro de autenticação, contendo mensagem de erro descritiva, porém seguem algumas possíveis causas:contaousenhaincorretos (no objeto GClickCredentials).empresaousistemaincorretos (no objeto GClickCredentials).client_idouclient_secretincorretos (no objeto GClickCredentials).AtividadeNotFoundException: Atividade pendente não encontrada, causas prováveis:inscriçãoda empresa cliente incorreto.id_atividadeincorreto.competenciaincorreta.BaixaUnknowException: Erro desconhecido inesperado na requisição. Possíveis causas:Chamada à API da GClick com retorno HTTP 400.Faltando um ou mais parâmetros do arquivo para upload.HTTPError: Erro desconhecido inesperado no processamento (retorno HTTP 500 ou outros, consultar a mensagem da exceção).Exception: Erro desconhecido.
Package: gclient-service-account-auth
GClient API Service Account AuthorizationAuthorizing server-to-server communications with Google’s APIs should
be easy. However, it can be difficult to understand how to do
authorization based on Google’sService Accounts, instead of the
better-documented three-party oauth flow. This library
attempts to streamline this process.It makes authentication and authorization as simple as creating an
instance of theAuthorizedServiceclass, with the name of your
project and the name of the API to authorize for. To get started, see
thequickstartguide.InstallationTo install the latest release, type:pip install gclient-service-account-authTo install the latest code directly from source, type:pip install git+git://github.com/ambitioninc/gclient-service-account-auth.gitDocumentationFull documentation is available athttp://gclient-service-account-auth.readthedocs.orgLicenseMIT License (see LICENSE)
Package: gcl-language-server
This repository contains an implemention of the [Language Server
Protocol](https://github.com/Microsoft/language-server-protocol) spec for the [GCL
language](https://github.com/rix0rrr/gcl). It is used by the
[Visual Studio Code extension](https://github.com/rix0rrr/gcl-vscode).InstallationInstall this package viapip:pip install gcl-language-serverAnd the extension from within Visual Studio Code.
Package: gclone
Python 3 program for cloning github repositories by name or keyword.Installpip install--usergcloneEnsure script location is in yourPATH. Usually~/.local/bin.UsageClone by repo name:$ gclone mtbrock/gcloneClone by partial name or keyword. gclone provides a list of github
repositories to choose from, sorted by ‘stars’:$ gclone tmuxPass options like you would togit clone:$ gclone --branch my-branch my-repo /path/to/my-dirSpecify sort, order, or limit:$ gclone --sort='forks' --order='desc' --limit=20 keyword
Package: gcloud-aio-auth
This is a shared codebase forgcloud-aio-authandgcloud-rest-authThis library implements anIamClientclass, which can be used to interact
with GCP public keys and URL sign blobs.It also implements anIapTokenclass which is used for authorizing against
anIdentity-Aware Proxy(IAP) secured GCP service. IAP uses identity tokens
which are specific to the target service and allows administrators to configure
a list of identities (ex. service accounts, users, or groups) that may access
the service. Therefore eachIapTokeninstance corresponds to an ID token
which may be used to authorize against a single IAP service.It additionally implements aTokenclass, which is used for authorizing
against Google Cloud. The othergcloud-aio-*package components accept aTokeninstance as an argument; you can define a single token for all of
these components or define one for each. Each component corresponds to a given
Google Cloud service and each service requires various “scopes”.Installation$pipinstall--upgradegcloud-{aio,rest}-authUsageSeeour docs.ContributingPlease see ourcontributing guide.
Package: gcloud-aio-run
asyncio Python Client for Google Cloud RunInstallationpip install gcloud-aio-runUsageList Cloud Run Servicesimportaiohttpfromgcloud.aio.runimportRunClientasyncdefprint_services(project,region):asyncwithaiohttp.ClientSession()assession:asyncwithRunClient(session=session)asclient:services=awaitclient.list_services(project,region=region)forserviceinservices:print(service.name)LicenseCopyright (c) 2021 byCornelius Buschka.Apache License, Version 2.0
Package: gcloud-config-helper
gcloud config helperThis library allows you to use the current gcloud configuration credentials to authenticate with against
the google APIs.The library provides the class GCloudCredentials which wraps thegcloud config config-helpercommand.To use:import gcloud_config_helper
credentials, project = gcloud_config_helper.default()Next you can pass these credentials in when constructing an API client:from google.cloud import compute_v1
c = compute_v1.InstancesClient(credentials=credentials)
for zone, instances in c.aggregated_list(request={"project": project}):
    for instance in instances.instances:
        print(f'found {instance.name} in zone {zone}')Note that Google documentation states thatgcloud config config-helpershould be regarded as an
unstable interface.if you want, you can use thegcloud_config_helper.on_path()to determine to use gcloud or the
default credentials:if google_config_helper.on_path():
   credentials, project = gcloud_config_helper.default()
else:
   logging.info("using application default credentials")
   credentials, project = google.auth.default()obtain Google gcloud configuration credentialsFree software: Apache Software License 2.0History0.1.0 (2021-04-24)First release on PyPI.
Package: gcloud-connectors
GCLOUD CONNECTORSPython utilities to simplify connection with Google APIsWhere to get itThe source code is currently hosted on GitHub at:https://github.com/pualien/py-gcloud-connectorsBinary installers for the latest released version are available at thePython
package index.pipinstallgcloud-connectorsUsageBe sure to check out thedocumentation.Google WrappersBigQueryConnector: read and cast pandas DataFrame from BigQueryGAnalyticsConnector: unsample data and return pandas DataFrame from Google AnalyticsGDriveConnector: download, upload, search and rename files from Google DriveGSCConnector: get data from Google Search ConsoleGSheetsConnector: read and upload pandas DataFrame from / to Google SpreadsheetGStorageConnector: write pandas DataFrame in parquet format to Google Cloud Storage, recursive delete, copy files and folders between bucketsGAnalytics4Connector: return pandas DataFrame from Google Analytics 4 reportsBonusForeignExchangeRatesConverter: get currency conversion ratesLTVCalculator: compute Customer Lifetime Valuepd_utils: derive quarter, month column from date in pandas DataFrame
Package: gclouddatastore
# Google Cloud Datastore## Incredibly quick demoYou need a few packages, so take a look at requirements.txt andpip installthem.$ pip install -r requirements.txtThen you should be all set to run the demo…$ python demo/demo.py## Doing this on your own### Create a projectVisithttps://cloud.google.com/consoleClick the big red button that says “Create Project”.In the name box, pick something friendly.In the ID box, pick something unique to you (ie, jgatsby-storage).### Enable the Cloud DatastoreClick on APIs & Auth, and scroll down to Google Cloud Datastore API.Click the “Off” button on the right to turn it into an “On” :)### Enable a Service AccountClick on Credentials (under APIs & Auth).Under the OAuth section (the first one) click the big red button that says “Create New Client ID”.Choose Service Account, and click the blue button “Create Client ID”.This will automatically download a private key, don’t lose this.Rename this key something shorter… like “jgatsby-storage.key”.Copy the long weird e-mail address (it’s labeled “E-mail address” in the information for the service account you just created).### Add some demo data (manually)Click on Cloud Datastore towards the bottom.Click the big red button that says “Create Entity”.Leave Namespace alone (should be in the default one).For Kind, type in “Thing”.Leave Key set to ID (this will automatically pick a numeric ID for you).In the first textbox, type in “name” (this is like one of the columns in a regular database).Leave it as a string, and indexed.For the value, type in “Computer”.Click “Create Entity”.What you just did there is add an entity that equates to a Python object you’d construct like… Thing(name=’Computer’).Feel free to add another Thing. Maybe a “Beer”? And a “Desk”?### Write some codeYou can usedemo/demo.pyas a reference.import gclouddatastoreCLIENT_EMAIL = ‘<the e-mail address you copied>’
KEY_PATH = ‘./jgatsby-storage.key’  # Make this the path to the key that auto-downloaded.connection = gclouddatastore.get_connection(‘jgatsby-storage’,  # Make this the project ID you picked before.
CLIENT_EMAIL, KEY_PATH)# This will print out all the Thing’s you created.
print connection.query(‘Thing’).fetch()### Yay, you’re doneGo build cool stuff :)## Hmm, that’s not on the Google Cloud website…You might notice that the other tutorials are a bit different.
This introduction is focused on you, on your home computer, with no interest
at all in using Google Compute Engine.
Package: gcloud_datastore_odm
gcloud-datastore-odmGoogle cloud datastore based ODMFree software: BSD licenseDocumentation:https://gcloud-datastore-odm.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2017-04-24)First release on PyPI.