Saravanan666/pypi-org huggingface dataset:
Package: gcommons.recipe.apachevhosts
.. contents::.. Note to recipe author!---------------------Update the following URLs to point to your:- code repository- bug tracker- questions/comments feedback mail(do not set a real mail, to avoid spams)Or remove it if not used.- Code repository: http://svn.somewhere.com/...- Questions and comments to somemailing_list- Report bugs at http://bug.somewhere.com/..Detailed Documentation**********************Supported options=================The recipe supports the following options:.. Note to recipe author!----------------------For each option the recipe uses you shoud include a descriptionabout the purpose of the option, the format and semantics of thevalues it accepts, whether it is mandatory or optional and what thedefault value is if it is omitted.option1Description for ``option1``...option2Description for ``option2``...Example usage=============.. Note to recipe author!----------------------zc.buildout provides a nice testing environment which makes itrelatively easy to write doctests that both demonstrate the use ofthe recipe and test it.You can find examples of recipe doctests from the PyPI, e.g.http://pypi.python.org/pypi/zc.recipe.eggThe PyPI page for zc.buildout contains documentation about the testenvironment.http://pypi.python.org/pypi/zc.buildout#testing-supportBelow is a skeleton doctest that you can start with when buildingyour own tests.We'll start by creating a buildout that uses the recipe::>>> write('buildout.cfg',... """... [buildout]... parts = test1...... [test1]... recipe = gcommons.recipe.apachevhosts... option1 = %(foo)s... option2 = %(bar)s... """ % { 'foo' : 'value1', 'bar' : 'value2'})Running the buildout gives us::>>> print 'start', system(buildout)start...Installing test1.Unused options for test1: 'option2' 'option1'.<BLANKLINE>Contributors************Juan Grigea, AuthorChange history**************0.1 (xxxx-xx-xx)----------------- Created recipe with ZopeSkel[Juan Grigea]Download********
Package: gconsul
While the python-consul doc advises to use the monkey patch approach when using gevent with python-consul you might run into trouble using other software alongside python-consul, say kafka-python for instance.So this is a more careful approach using grequests versions of put, get, post etc.Example usagefromconsulgeventimportConsul# and then go on as usualconsul=Consul()# ... and so on
Package: gcore-interface-sdk
#GCore SDK - (Gravity Core Interface Software Development Kit) - представляет из себя модуль с классом MainController, через методы которого
#реализовано исполнение всех методов API GCore. Все методы, а так-же их описания хранятся в файле methods.py, в форме словаря.#—Getting started—
#Для исполнения метода создайте экземпляр класса, передав ему ip, port GCore API.*
#Для просмотра всех доступных методов используйте метод класса get_api_methods.
#Для отправки метода используйте метод класса execute_method, передавая в виде аргумента method_name (method_name=”open_entry_gate”, например).#* - Если на GCore API включена защита по логину и паролю, передайте их в соответсвующих атрибутах login и password;
#так-же вы можете передать название главного метода general_method.(используется на GCore API
#для более глубокой дифференциации методов, например метод user_commands - для методов клиентского приложения (для которого написан этот SDK),
#или метод wserver_command - для комманд, которые приходят от WServer.)
Package: gcoreutils
GCoreUtilsA versatile python utility library.Authors: Hiroaki KusunoseInstallation:  GCoreUtils can be installed from PyPI using pip on Python >= 3.9:pip install gcoreutilsYou can also visitPyPIorGitHubto download the source.Features:manage binary filetext conversioneigen systemLaTeX utilitystring utilityplot utilitycombined numpy and sympy arraypdf creationcolor palette
Package: gcovr
generate GCC code coverage reportswebsite and documentation•bugtracker•GitHubGcovr provides a utility for managing the use of the GNUgcovutility
and generating summarized code coverage results. This command is
inspired by the Pythoncoverage.pypackage, which provides a similar
utility for Python.Thegcovrcommand can produce different kinds of coverage reports:CLI OptionUser GuideDescriptiondefault,--txtText Outputcompact human-readable summaries--htmlHTML Outputoverview of all files--html-detailsHTML Outputannotated source files--html-template-dirHTML Outputuse custom set of Jinja2 templates--csvCSV OutputCSV report summarizing the coverage of each file--jsonJSON OutputJSON report with source file structure and coverage--json-summaryJSON OutputJSON summary coverage report--cloverClover XML Outputmachine readable XML reports inCloverformat--coberturaCobertura XML Outputmachine readable XML reports inCoberturaformat--coverallsCoveralls JSON Outputmachine readable JSON report inCoverallsformat--jacocoJaCoCo XML Outputmachine readable XML reports inJaCoCoformat--lcovLCOV info Outputmachine readable report inLCOVinfo format--sonarqubeSonarQube XML Outputmachine readable XML reports inSonarQubeformatThus, gcovr can be viewed
as a command-line alternative to thelcovutility, which runs gcov
and generates an HTML-formatted report.
The development of gcovr was motivated by the need for
text summaries and XML reports.Example HTML summary:Example HTML details:InstallationGcovr is available as a Python package that can be installed viapip.Install newest stablegcovrrelease from PyPI:pipinstallgcovrInstall development version from GitHub:pipinstallgit+https://github.com/gcovr/gcovr.gitQuickstartGCC can instrument the executables to emit coverage data.
You need to recompile your code with the following flags:--coverage -g -O0Next, run your test suite.
This will generate raw coverage files.Finally, invoke gcovr.
This will print a tabular report on the console.gcovrYou can also generate detailed or nested HTML reports:gcovr --html-details coverage.html
gcovr --html-nested coverage.htmlGcovr will create one HTML report per source file and for--html-nestedalso per directory next to the coverage.html
summary.You should run gcovr from the build directory.
The-roption should point to the root of your project.
This only matters if you have a separate build directory.
For example:cd build; gcovr -r ..For complete documentation, read themanual.ContributingIf you want to report a bug or contribute to gcovr development,
please read our contributing guidelines first:https://github.com/gcovr/gcovr/blob/main/CONTRIBUTING.rstLicenseCopyright (c) 2013-2024 the gcovr authors
Copyright (c) 2013 Sandia Corporation.
Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
the U.S. Government retains certain rights in this software.This software is distributed under the 3-clause BSD License.
See LICENSE.txt for full details.
See AUTHORS.txt for the full list of contributors.Gcovr development moved to this repository in September, 2013 from
Sandia National Laboratories.
Package: gco-wrapper
pyGCO: a python wrapper for the graph cutsThe original wrapper ispygcoThis is a python wrapper forgco-v3.0 package, which implements a graph cuts based move-making algorithm for optimization in Markov Random Fields.It contains a copy of thegco-v3.0 package.  Some of the design were borrowed from thegco_pythonpackage. However, compared to gco_python:This package does not depend on Cython. Instead it is implemented using the ctypes library and a C wrapper of the C++ code.This package is an almost complete wrapper for gco-v3.0, which supports more direct low level control over GCoptimization objects.This package supports graphs with edges weighted differently.This wrapper is composed of two parts, a C wrapper and a python wrapper.Implemented functionscut_general_graph(...)cut_grid_graph(...)cut_grid_graph_simple(...)Building wrapperdownload the last version ofgco-v3.0to thegco_sourcecompile gco-v3.0 and the C wrapper usingmakecompile test_wrapper usingmake test_wrapperrun the C test code./test_wrapper(now you have the C wrapper ready)makedownload
makeall
maketest_wrapper
./test_wrapperThe successful run should return:labels=[0221],energy=19dataenergy=15,smoothenergy=4Next test the python wrapper usingpython test_examples.py, if it works fine you are ready to use pygco.To include pygco in your code, simply import pygco module. See the documentation inside code for more details.Install wrapperClone repository and enter folder, thenpipinstall-rrequirements.txt
pythonsetup.pyinstallNow it can be also installed from PyPipipinstallgco-wrapperShow test resultsVisualisation of the unary terns forbinary segmentation4-connectedcomponents with the initial labeling (left) and estimated labeling with regularisation1(middle) and0(right)8-connectedcomponents with the initial labeling (left) and estimated labeling with regularisation1(middle) and0(right)Visualisation of the unary terns for3 labels segmentationwith theinitiallabeling (left) andestimatedlabeling (right)
Package: gcp-accessor
This software is released under the MIT License, see LICENSE.txt.gcp-accessoris a wrapper ofGoogle Cloud Storage APIandGoogle BigQuery APIfor more simply accesseing both functions.While there aregoogle-cloud-storageandgoogle-cloud-bigquerylibrary provided by Google which have several sophisticated features, frequently used features can be limited in python application development.gcp-accessorfocuses on the simplicity of usage pattern and includes the selected features as described below.bq_accessorget_datasetget_table_namecheck_if_dataset_existscheck_if_table_existscreate_table_from_jsonload_data_from_gcsexecute_querygcs_accessorget_blobget_blob_listget_uris_listupload_csv_gzipdownload_csv_gzipSetupInstallationpipinstallgcp-accessorSet GOOGLE_APPLICATION_CREDENTIALSexportGOOGLE_APPLICATION_CREDENTIALS='full path to credential key json file'Usage of Google Cloud Storage AccessorImport gcp_accessor and create google cloud storage clientimportgcp_accessorgcs=gcp_accessor.GoogleCloudStorageAccessor()Get Binary Large Object (blob) from google cloud storage (gcs)gcs.get_blob('bucket_name','full path to file on gcs')Get Binary Large Object (blob) lists as HTTPIterator object from gcsgcs.get_blob_list('bucket_name',prefix=None,delimiter=None)Get uris listsgcs.get_uris_list('bucket_name',prefix=None,delimiter=None)Upload gzip object on gcsgcs.upload_csv_gzip('bucket_name','full path to file on gcs','texts')Download gzip file from gcs and load it through in-memorygcs.download_csv_gzip('bucket_name','full path to file on gcs')Usage of Big Query AccessorImport gcp_accessor and create big query clientimportgcp_accessorbq=gcp_accessor.BigQueryAccessor()Get datasets if datasets do not exist, then return empty listbq.get_dataset()Get table names if table names do not exist, then return exception error messagebq.get_table_name('dataset_name')Check if dataset exsists in bigquery and then return True or Falsebq.check_if_dataset_exists('dataset_name')Check if table exsists in bigquery and then return True or Falsebq.check_if_table_exists('dataset_name','table_name')Create table on bigquery based on schema json filebq.create_table_from_json('path_schema_file','dataset','table_name')Load data from gcs (You have to upload file on gcs.).bq.load_data_from_gcs('dataset_name','uris','table_name',location="US",skip_leading_rows=0,source_format="CSV",create_disposition="CREATE_NEVER",write_disposition="WRITE_EMPTY",)Execute a simple query or query with the below optiponsbq.execute_query('query',location="US",timeout=30,page_size=0,project=None,allow_large_results=False,destination=None,destination_encryption_configuration=None,dry_run=False,labels=None,priority=None,query_parameters=None,schema_update_options=None,table_definitions=None,time_partitioning=None,udf_resources=None,use_legacy_sql=False,use_query_cache=False,write_disposition=None)NoteSome argument names and descriptions about each argument are cited and referred from the documents of'Google Cloud Client Libraries for Python'The explanations about each argument are written in the code.
Package: gcp-airflow-foundations-dev-jiny
gcp-airflow-foundationsAirflow is an awesome open source orchestration framework that is the go-to for building data ingestion pipelines on GCP (using Composer - a hosted AIrflow service). However, most companies using it face the same set of problemsLearning curve: Airflow requires python knowledge and has some gotchas that take time to learn. Further, writing Python DAGs for every single table that needs to get ingested becomes cumbersome. Most companies end up building utilities for creating DAGs out of configuration files to simplify DAG creation and to allow non-developers to configure ingestionDatalake and data pipelines design best practices: Airflow only provides the building blocks, users are still required to understand and implement the nuances of building a proper ingestion pipelines for the data lake/data warehouse platform they are usingCore reusability and best practice enforcement across the enterprise: Usually each team maintains its own Airflow source code and deploymentWe have written an opinionated yet flexible ingestion framework for building an ingestion pipeline into data warehouse in BigQuery that supports the following features:Zero-code, config file based ingestion - anybody can start ingesting from the growing number of sources by just providing a simple configuration file. Zero python or Airflow knowledge is required.Modular and extendable - The core of the framework is a lightweight library. Ingestion sources are added as plugins. Adding a new source can be done by extending the provided base classes.Opinionated automatic creation of  ODS (Operational Data Store ) and HDS (Historical Data Store) in BigQuery while enforcing best practices such as schema migration, data quality validation, idempotency, partitioning, etc.Dataflow job support for ingesting large datasets from SQL sources and deploying jobs into a specific network or shared VPC.Support of advanced Airflow features for job prioritization such as slots and priorities.Integration with GCP data services such as DLP and Data Catalog [work in progress].Well tested - We maintain a rich suite of both unit and integration tests.Installing from PyPIpipinstall'gcp-airflow-foundations'UsageSee thegcp-airflow-foundations documentationfor more details.
Package: gcp-alpa
gcpVarious wrappers over Google Cloud Platform APIInstallationpip install gcp-alpageocodingfrom gcp.geocoding import GCPGeocoding

geocoding = GCPGeocoding(api_key='<API_KEY>')

result = geocoding.reverse(78.2232, 15.6267)

print(result.country)
print(result.locality)
print(result.neighborhood)OutputSvalbard and Jan Mayen
Longyearbyen
None
Package: gcp-cloud-logger
GCP Cloud loggerPackage forGoogle Cloud Logging clientInstallpip install gcp-cloud-loggerUsagefromgcp_cloud_loggerimportcloud_loggeraslogger# Default use GOOGLE_APPLICATION_CREDENTIALS to get credential.json pathlogger.setup(name="logger_name",credential_path="/your/service/account/json",)# ORlogger.setup(name="logger_name",credential_json="{}",)# Infologger.info("Test info log message")# Warninglogger.warn("Test warning log message")# Errorlogger.error("Test error log message")# Criticallogger.critical("Test critical log message")Result
Package: gcpcsvhunt
[]GCPCsvHuntwhat is GCPCsvHunt  ?GCP Csv Hunt is a open Source Python Modules that allows you to download all the Files from the GCP bucket with 2 lines of
python code check ouyt the example on How to use itDependenciespipinstallgoogle-cloud-storage
pipinstallpandasInstallationpipinstallGCPCsvHuntUsagefromgcpcsvhunt.gcpcsvhuntimportGCPCsvHuntpathToJsonFile='PATH TO YOUR JSON FILE 'BucketName='BUCKET NAME GOES HERE 'prefix=''# Searches in Outer Directoryobj=GCPCsvHunt(path=pathToJsonFile,bucketName=BucketName)data=obj.getAllCSV(prefix=prefix)# Return list of Objectsdf1=data.pop()# Pop the First Pandas Objectprint(df1.data)# Print Pandas Data FrameYoutube Links to TutorialsTutorial :https://www.youtube.com/watch?v=Cd7v8UtyFpwAuthorsSoumil Nitin ShahBachelor in Electronic Engineering |
Masters in Electrical Engineering |
Master in Computer Engineering |Website :https://soumilshah.herokuapp.comGithub:https://github.com/soumilshah1995Linkedin:https://www.linkedin.com/in/shah-soumil/Blog:https://soumilshah1995.blogspot.com/Youtube :https://www.youtube.com/channel/UC_eOodxvwS_H7x2uLQa-svw?view_as=subscriberFacebook Page :https://www.facebook.com/soumilshah1995/Email :shahsoumil519@gmail.comLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: gcp_dbt_remote
dbt-remote projectThis package providesdbt-remote, a drop-in replacement for the dbt CLI for analytics engineers.dbt-server, a Cloud Run API that will need to be deployed to perform the remote dbt runs (for data platform engineers:How to deploy?).dbt-remoteThis CLI runs dbt commands remotely on GCP-hosted server.RequirementsAn initialized dbt core project.(dbt core quickstart)The gcloud CLI.(gcloud install guide)A deployed dbt-server.(dbt-server deployment guide)Installationpython3-mpipinstallgcp-dbt-remote--no-cache-dirRefresh your shell/venv to enable the cli:sourcevenv/bin/activateORcondaactivateSetup your GCP project, gcloud CLI, and default credentialsexportPROJECT_ID=<your-gcp-project-id>gcloudauthlogin
gcloudauthapplication-defaultlogin
gcloudconfigsetproject$PROJECT_IDMake sure your dbt project is properly setup locally.dbtdebug>Allcheckspassed!Test the CLI installation (requires you to have deployed thedbt-server)dbt-remotedebug>INFO[dbt]Allcheckspassed!
>INFO[job]CommandsuccessfullyexecutedUsedbt-remotejust like you would do with the regular dbt CLIdbt-remoterundbt-remoterun--selectmy_first_dbt_modelView alldbt-remoteoptionsdbt-remote--helpSchedule dbt runsUse the --schedule option and a cron expression to schedule a run.Help with cron expressions.dbt-remoterun--schedule'0 8 * * *'[...]Sendingrequesttoserver...
Jobrunscheduledat08***(At08:00AM)withuuid:e11f1085-8ad9-4dcd-b09f-d8a8369075b9This will create acloud schedulerthat will call the dbt-server at the configured time.You can also declare a run schedule using a config file. Be aware that this is authoritative and will replace any previously scheduled runs not in the file. It will also update the manifest for the scheduled runs.# schedules.yamlschedule-1:command:build --select my_first_dbt_model --project-dir=tests/dbt_projectschedule:"23456"schedule-3:command:build --select my_first_dbt_model --project-dir=tests/dbt_projectschedule:"23456"dbt-remoteschedulessetschedules.yaml[...]Thefollowingactionswillbeperformed:
+Add:schedule-1
-Delete:schedule-2
~Redeploy:schedule-3
Doyouwanttocontinue?[y/N]:yTo check your scheduled runs, either go to thecloud scheduler UI of your project, or list them uting the cli:dbt-remotescheduleslist[...]dbt-server-e11f1085-8ad9-4dcd-b09f-d8a8369075b9command:runschedule:08***(At08:00AM)UTCtarget:https://dbt-server-vo6sb27zvq-ew.a.run.app/schedule/e11f1085-8ad9-4dcd-b09f-d8a8369075b9/startYou can also delete them in the UI, or using the CLI:dbt-remoteschedulesdeletee11f1085-8ad9-4dcd-b09f-d8a8369075b9[...]Scheduledbt-server-e11f1085-8ad9-4dcd-b09f-d8a8369075b9deleted(optional) Set persistent configurations fordbt-remoteusingconfigcommanddbt-remoteconfigsetserver_url=http://myserver.comlocation=europe-west9View all configuration optionsdbt-remoteconfighelp
Package: gcpdns
A Python module and CLI for managing zones and resource record sets on Google Cloud DNSFeaturesDump all project zones names in CSV and/or JSON formatDump all zone resource record sets in CSV and/or JSON formatCreate and delete zones via CLI or CSVCreate and delete resource record sets via CLIUpdate DNS resource records for multiple zones in one project using one CSV
fileAutomatically splitTXTrecords longer than 255 characters when publishingAutomatically add an ending.to records when neededCLIUsage: gcpdns [OPTIONS] CREDENTIAL_FILE COMMAND [ARGS]...

  gcpdns: A CLI for managing zones and resource record sets on Google Cloud
  DNS.

Options:
  --version  Show the version and exit.
  --verbose  Enable verbose logging.
  --help     Show this message and exit.

Commands:
  record  Manage DNS resource record sets.
  zone    Manage DNS zones.gcpdns recordUsage: gcpdns record [OPTIONS] COMMAND [ARGS]...

  Manage DNS resource record sets.

Options:
  --help  Show this message and exit.

Commands:
  create  Create a resource record set (Data fields separated by |).
  delete  Delete a resource record set
  dump    Dump a list of DNS resource records.
  update  Create, replace, and delete resource record sets using a CSV file.gcpdns zoneManage DNS zones.

Options:
  --help  Show this message and exit.

Commands:
  create  Create a DNS zone.
  delete  Delete a DNS zone and all its resource records.
  dump    Dump a list of DNS zones.
  update  Create and delete zones using a CSV file.InstallationUsepip(orpip3for Python 3).sudo -H pip install gcpdnsSetupTo usegcpdns, you’ll need a separateservice accountcredentials JSON
file for each GCP project that you want to work with.Ensure that the Service Account has the proper permissions to edit DNS
(e.g. the DNS Administrator role) in the project.Zone CSV fieldsactioncreate- Creates a zonedelete- Deletes a zonedns_name- The zone’s DNS namegcp_name- The zone’s name in GCP (optional)description- The zone’s description (optional)Record CSV fieldsactioncreate- Creates a resource record setreplace- The same ascreate, but will replace an existing resource
record set with the samenameandrecord_type(if it exists)delete- Deletes a resource record setname- The record set name (i.e. the Fully-Qualified Domain Name)record_type- The DNS record typettl- DNS time to live (in seconds)data- DNS record data separated by|
Package: gcpfwup
OverviewA command-line utility to update Google Cloud Platform firewall rules.
Particularly, source IP range for allow SSH rule.Use caseHarden a virtual machine in Google Cloud by allowing only a single IP address to log in through SSH.Installationpip install gcpfwupRequires Python 3.8 or newer.
Depends ongoogle-cloud-compute.Usagegcpfwup -v service_account_file.json

Target public IP: 101.35.101.248.
IP(s) allowed for SSH: 127.0.0.1.
Updating rule to allow only target IP address...
Done.The only required argument is a path to Google service account JSON key file.
You can download it from Google Cloud Console.
It should be under IAM & Admin / Service Accounts and needs permissions for Google compute engine.Helpgcpfwup --help

usage: gcpfwup [-h] [-v] [--ip IP] service_account_file

positional arguments:
  service_account_file  path to Google API service account JSON file

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         increase output verbosity
  --ip IP               use this IP instead of auto-resolved public IP