Saravanan666/pypi-org huggingface dataset:
Package: test-cmd
This is a tool for black-box testing command-line programs simply based on STDIN, STDOUT, and STDERR,with multiple
CPUs. A sempahore keeps track of which CPUs are in use; and thus, tests are run in parallel on all CPUs, and new test
runs are dispatched as tests complete (or fail).test_cmdwaits until all tests have completed (either successfully
or failed), and prints failed tests’STDERRoutput, as they fail, in real time.TutorialTest cases usually consist of pairs of input and output files, as well as an optionaltests.jsonfile specifying applicable command-line arguments.  The input file is piped in viaSTDIN. If the command being tested emits the expected output file viaSTDOUT, the test case passes. A file representing an expectedSTDERRoutput can also optionally be specified.The input/output file pairs must follow this naming pattern:test-A.in.txt  ->  test-A.out.txt
test-B.in.txt  ->  test-B.out.txt, test-B.err.txt
test-C.in.txt  ->  test-C.out.txtThe file extension (.txthere) can be anything. The file naming pattern is*.in*for input files,*.out*for expected output files, and*.err*for expected error files. The content of the*.in.*file is piped to the command being tested, and itsSTDOUTis compared against the*.out*file. If a*.err.*file has been provided, then the commandSTDERRis matched against it as well.For an example of test_cmd in action, see thepypage project, particularly itstests folder.Usageusage: test_cmd.py [-h] [-b] [-d] [-u] [-t] tests_dir cmd ...

Functional Testing Utility for Command-Line Applications

positional arguments:
  tests_dir      Path to the directory containing test cases
  cmd            Path to the command to be tested
  args           The command-line arguments with an ampersand character '@' markingwhere arguments from test.json should be injected

options:
  -h, --help     show this help message and exit
  -b, --bw       black & white output
  -d, --diff     diff output
  -u, --to-unix  convert CR+LF to LF in cmd output and test files
  -t, --rtrim    ignore trailing whitespaces at the end of each line as well as trailing newlinesCommand-line arguments for test cases can be specified by creating a special file namedtests.json, and placing it in the directory containing your test cases. Thistests.jsonfile maps test cases to objects representing command-line arguments for that test case. If a command-line argument is a non-string value (e.g. a complex JavaScript object), the argument is stringified (with Python’sjson.dumps), and passed in as JSON.
Package: test-cmd-x
test_cmd_xThis is a tool for black-box testing command-line programs simply based on STDIN, STDOUT, and STDERR. test_cmd_x is a extended version of originaltest_cmd.TutorialTest cases usually consist of pairs of input and output files, as well as an optionaltests.jsonfile specifying applicable command-line arguments.  The input file is piped in viaSTDIN. If the command being tested emits the expected output file viaSTDOUT, the test case passes. A file representing an expectedSTDERRoutput can also optionally be specified.The input/output file pairs must follow this naming pattern::test-A.in.txt  ->  test-A.out.txt
test-B.in.txt  ->  test-B.out.txt, test-B.err.txt
test-C.in.txt  ->  test-C.out.txtThe file extension (.txthere) can be anything. The file naming pattern is*.in*for input files,*.out*for expected output files, and*.err*for expected error files. The content of the*.in.*file is piped to the command being tested, and itsSTDOUTis compared against the*.out*file. If a*.err.*file has been provided, then the commandSTDERRis matched against it as well.For an example of test_cmd in action, see thepypage project, particularly itstests folder.Usageusage: test_cmd_x.py [-h] [-b] [-d] [-u] [-t] tests_dir cmd ...

Functional Testing Utility for Command-Line Applications

positional arguments:
  tests_dir      Path to the directory containing test cases
  cmd            Path to the command to be tested
  args           The command-line arguments with an ampersand character '@'
                 markingwhere arguments from test.json should be injected

optional arguments:
  -h, --help     show this help message and exit
  -b, --bw       black & white output
  -d, --diff     diff output
  -u, --to-unix  convert CR+LF to LF in cmd output and test files
  -t, --rtrim    ignore trailing whitespaces at the end of each line as well
                 as trailing newlinesCommand-line arguments for test cases can be specified by creating a special file namedtests.json, and placing it in the directory containing your test cases. Thistests.jsonfile maps test cases to objects representing command-line arguments for that test case. If a command-line argument is a non-string value (e.g. a complex JavaScript object), the argument is stringified (with Python'sjson.dumps), and passed in as JSON.
Package: test-common
test_commonCommon functions useful for tests.In this package you can find some functions I use frequently at my tests.Modules listbenchmarkPackage with modules to measure apps performance.timingFunctions to take time related features.fsPackage with filesystem utilities. They are useful to prepare folders and files for your tests.cryptoCryptographic functions for your tests. Here you can find hashing functions to check file contents.opsFunctions for file operations (copy, delete, etc).randomUtilities to generate random content for your tests. Includes next modules:cryptoFunctions to create random strings.systemUtilities to deal with your hot operating system. Includes next modules:envFunctions to manipulate environment variablesoutputTools to capture outputs.
Package: testcontainer-python-rabbitmq
Python Testcontainer RabbitMQUsageimportpikafromtestcontainer_python_rabbitmqimportRabbitMQContainerdeftest_rabbitmq():config=RabbitMQContainer()withconfigascontainer:connection=pika.BlockingConnection(pika.ConnectionParameters(host=container.get_container_host_ip(),port=container.get_amqp_port(),credentials=pika.PlainCredentials(username="guest",password="guest"),))# do something...
Package: testcontainers
Python port for testcontainers-java that allows using docker containers for functional and integration testing. Testcontainers-python provides capabilities to spin up docker containers (such as a database, Selenium web browser, or any other container) for testing.Currently available features:Selenium Grid containersSelenium Standalone containersMySql Db containerMariaDb containerNeo4j containerOracleDb containerPostgreSQL Db containerClickHouse containerMicrosoft SQL Server containerGeneric docker containersArangoDB containerLocalStackRabbitMQKeycloakAzurite containerInstallationThe testcontainers package is available fromPyPI, and it can be installed usingpip. Depending on which containers are needed, you can specify additional dependencies asextras:# Install without extraspipinstalltestcontainers# Install with one or more extraspipinstalltestcontainers[mysql]pipinstalltestcontainers[mysql,oracle]Basic usage>>> from testcontainers.postgres import PostgresContainer
>>> import sqlalchemy

>>> postgres_container = PostgresContainer("postgres:9.5")
>>> with postgres_container as postgres:
...     e = sqlalchemy.create_engine(postgres.get_connection_url())
...     result = e.execute("select version()")
...     version, = result.fetchone()
>>> version
'PostgreSQL 9.5...'The snippet above will spin up a Postgres database in a container. Theget_connection_url()convenience method returns asqlalchemycompatible url we use to connect to the database and retrieve the database version.More extensive documentation can be found atRead The Docs.Usage within Docker (e.g., in a CI)When trying to launch a testcontainer from within a Docker container two things have to be provided:The container has to provide a docker client installation. Either use an image that has docker pre-installed (e.g. theofficial docker images) or install the client from within theDockerfilespecification.The container has to have access to the docker daemon which can be achieved by mounting/var/run/docker.sockor setting theDOCKER_HOSTenvironment variable as part of yourdocker runcommand.Setting up a development environmentWe recommend you use avirtual environmentfor development. Note that a python version>=3.7is required. After setting up your virtual environment, you can install all dependencies and test the installation by running the following snippet.pipinstall-rrequirements/$(python-c'import sys; print("%d.%d" % sys.version_info[:2])').txtpytest-sAdding requirementsWe usepip-toolsto resolve and manage dependencies. If you need to add a dependency to testcontainers or one of the extras, modify thesetup.pyas well as therequirements.inaccordingly and then runpip install pip-toolsfollowed bymake requirementsto update the requirements files.Contributing a new containerYou can contribute a new container in three steps:Create a new module attestcontainers/[my fancy container].pythat implements the new functionality.Create a new test module attests/test_[my fancy container].pythat tests the new functionality.Add[my fancy container]to the list of test components in the GitHub Action configuration at.github/workflows/main.yml.
Package: testcontainers-iris
Testcontainers-python for InterSystems IRIStestcontainers-pythonfacilitates the use of Docker containers for functional and integration testing.Other implementationstestcontainers-iris-javaBasic usage>>> from testcontainers.iris import IRISContainer
>>> import sqlalchemy

>>> iris_container = IRISContainer("intersystemsdc/iris-community:latest")
>>> with iris_container as iris:
...     engine = sqlalchemy.create_engine(iris.get_connection_url())
...     with engine.begin() as connection:
...         result = connection.execute(sqlalchemy.text("select $zversion"))
...         version, = result.fetchone()
>>> version
'IRIS for UNIX (Ubuntu Server LTS for ARM64 Containers) 2023.2 (Build 227U) Mon Jul 31 2023 17:43:25 EDT'The snippet above will spin up a InterSystems IRIS database in a container. The get_connection_url() convenience method returns a sqlalchemy compatible url we use to connect to the database and retrieve the database version.It is possible to run Enterprise edition as well, with passing a valid iris.key.iris_container=IRISContainer("containers.intersystems.com/intersystems/iris:2023.3",license_key="/full/path/to/iris.key",username="testuser",password="testpass",namespace="TEST",)username,password,namespacewill be created during start of the container with required values, user will get %All roleMore extensive documentation can be found atRead The Docs.
Package: test-cookiecutter
test_cookiecutterPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: GNU General Public License v3Documentation:https://test-cookiecutter.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2018-03-05)First working version.0.1.0 (2018-03-05)First release on PyPI.
Package: test-cookiecutter-pypackage-project
test-cookiecutter-pypackage-projectA test project for my cookiecutter-pypackage repoFree software: MITDocumentation:https://test-cookiecutter-pypackage-project.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand themgancita/cookiecutter-pypackageproject template.
Package: test-crawlab-sdk
Crawlab SDK for Python中文| EnglishThe SDK for Python contains two parts:CLI ToolUtility ToolsCLI ToolThe CLI Tool is mainly designed for those who are more comfortable using command line tools to interact with Crawlab.The installation of the CLI Tool is simple:pipinstallcrawlab-sdkThen, you can use thecrawlabcommand in the command prompt to action with Crawlab.Check the help document below, or you can refer tothe official documentation (Chinese).crawlab --helpUtility ToolsUtility tools mainly provide somehelpermethods to make it easier for you to integrate your spiders into Crawlab, e.g. saving results.Below are integration methods of Scrapy and general Python spiders with Crawlab.⚠️Note: make sure you have already installedcrawlab-sdkusing pip.Scrapy IntegrationInsettings.pyin your Scrapy project, find the variable namedITEM_PIPELINES(adictvariable). Add content below.ITEM_PIPELINES={'crawlab.pipelines.CrawlabMongoPipeline':888,}Then, start the Scrapy spider. After it's done, you should be able to see scraped results inTask Detail -> ResultGeneral Python Spider IntegrationPlease add below content to your spider files to save results.# import result saving methodfromcrawlabimportsave_item# this is a result record, must be dict typeresult={'name':'crawlab'}# call result saving methodsave_item(result)Then, start the spider. After it's done, you should be able to see scraped results inTask Detail -> Result
Package: testcsg
Computer Systems Genome APIAboutThe Mission of the Computer Systems Genome Project (CSGenome) is to conduct the first scientific effort to catalog the lineage of computer system performance over time to enable knowledge discovery and further understanding of the impact of computing innovations on transformative technologies, the knowledge-based economy, and societal change.This is an API to access our database and request computer system information, ranging from RAM, to processors, and even GPUs.Learn more at our website:https://csgenome.org/Table of ContentsComputer Systems Genome APIAboutGetting Started and TutorialExamplesContributingCreditsGetting Started and TutorialInstall throughpip install csgapiRetrieve the client through the importfrom csgapi import clientConnect the client withcli = client.CSGenome()If you have an auth token that allows you more access:cli = client.CSGenome(auth_token='test123')Accessing a specific table in our databasecli.processorsGetting an entry in a table given UIDcli.processors.get(1000)Retrieving only specific columns of an entrycli.processors.get(uid=1, params={'columns': 'model,uid'})Getting a list of entries and only specific columnscli.processors.list(columns=['model', 'uid'])Request entries that satisfy a filterGet all processors with clock_speed of 2100cli.processors.list(clock_speed=2100)Get all processors within 2100 to 2500 value for clock_speedcli.processors.list(clock_speed=[ge(2100), lt(2500)])Get all processors wih clock_speed greater than 2100cli.processors.list(clock_speed=gt(2100))ge()is used to signify greater than or equal togt()is used to signify greater thanle()is used to signify less than or equal tolt()is used to signify less thanThese functions can be imported fromcsgapi.baseHigher authentication capabilities (Post, Put, Delete)Create entryprocessor_data = {'vdd_high': test}res1 = cli.processor.create(data=processor_data)Update a entry by UIDupdated_data = {'accepted_memory': 'spec2017_test_put'}res = cli.spec2017.update(50, updated_data)Modify a specific entryres = cli.spec2017.get(50)res.accepted_memory = "test_smart_update"res.save()Delete an entry by UIDcli.processors.delete(50)ExamplesUsing the data in our database, you can create meaningful visualizations such as these.ContributingIf you would like to submit a request for a change, email us atcsgenomevtteam@gmail.comCreditsThe CSGenome Project is led by Professors Kirk W. Cameron (PI), Godmar Back (co-PI and Technical Advisor), and Margaret Ellis (co-PI and Team Leader) and an extremely talented and highly diverse group of more than a dozen Virginia Tech undergraduate and graduate students.
Package: testdatagen
This testdatagen.nameandaddr module generates pipe (’|’) delimited test
data with the following fields - firstname, lastname, email,
streetAddress, ZipCode, City, State, StateAbrv, County, Latitude,
LongitudeThe intention of this module is to provide a simple way to quickly
generate person test data.Usage :from testdatagen import nameandaddr
nameandaddr.nameaddr(filename, recordcount)

example : nameandaddr.nameaddr('persons.csv', 300)

This example will create  a file named 'persons.csv' in the  directory where this program is located.
The file 'persons.csv' will have 300 lines.
It is recommended that you provide a fuly qualified file name (i.e. /path/filename)It needs the included csv files to generate the test data. These files
are listed below -testdata_dn.csv
testdata_fn.csv
testdata_ln.csv
testdata_st.csv
us_postal_codes.csvOf the above csv files, the first four are created using faker module.
The last file is publicly available information.
Package: test-data-lake
## DATALEKE AZURE# technologies
azure, synapses, delta lake, data lake azure, notebookThis library has methods to connect to the data lake in azure
It also allows you to pass data from a storage account to
another through delta lake#Landing
the raw data arrives from the source of information origin#BronzeThe landing data is passed to bronze in delta lake format#Silver
The columns defined for the change are passed to silver and there is also
in delta lake format#installation1. Go to the cluster and select the package option
![image info](./assets/cluster.PNG)2. then go and upload the requirements that need to be installed
![image info](./assets/requerements.PNG)3. verify that the libraries have been installed
![image info](./assets/req_synapses.PNG)
Package: testdatatable
TestDataTableAboutTestDataTable is a web-based application that offers an alternative to static data sheets (text files, excel, etc.). When you use static data sheets, each test script is assigned parameter values from a dedicated set of values, values are either  not shared between test scripts or are duplicated in each script. In contrast, TestDataTable enables you to assign test data variable values from a single set of data to multiple scripts while allowing you to ensure each script has a unique data value. This may enable you to more accurately emulate a real-user environment.TestingTheRegression_Testsfolder contains the current Regression Test Suite, I would love to hear the results of running this suite against Test Data Table on your machine, especially a Window Machine (as I don't have one)Getting HelpTestDataTable DocumentationDiscordReporting Issues / Known IssuesDonationsIf you would like to thank me for this project please use thisPayPal.melink, the $5 is a suggestion, feel free to change to any amount you would like.
Package: test-detect-spots
test-detect-spotsA dummy plugin to learn how to create pluginsThisnapariplugin was generated withCookiecutterusing@napari'scookiecutter-napari-plugintemplate.InstallationYou can installtest-detect-spotsviapip:pip install test-detect-spotsContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"test-detect-spots" is free and open source softwareIssuesIf you encounter any problems, please [file an issue] along with a detailed description.
Package: test-dichen-proj
dbtenables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.This package installs thedbt Cloud CLIto invoke dbt commands from the command line that execute in adbt Cloudenvironment.Understanding dbtAnalysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.These select statements, or "models", form a dbt project. Models frequently build on top of one another – dbt makes it easy tomanage relationshipsbetween models, andvisualize these relationships, as well as assure the quality of your transformations throughtesting.Join the dbt CommunityBe part of the conversation in thedbt Community SlackRead more on thedbt Community Discourse
Package: test-django-cookie-app
Learning DjangoCookieCuttterAppDocumentationThe full documentation is athttps://test_django_cookie_app.readthedocs.io.QuickstartInstall test_django_cookie_app:pip install test_django_cookie_appAdd it to yourINSTALLED_APPS:INSTALLED_APPS=(...'test_django_cookie_app.apps.TestDjangoCookieAppConfig',...)Add test_django_cookie_app’s URL patterns:fromtest_django_cookie_appimporturlsastest_django_cookie_app_urlsurlpatterns=[...url(r'^',include(test_django_cookie_app_urls)),...]FeaturesTODORunning TestsDoes the code actually work?source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install tox
(myenv) $ toxCreditsTools used in rendering this package:Cookiecuttercookiecutter-djangopackageHistory0.1.0 (2019-04-30)First release on PyPI.
Package: testdocker
testdocker~~~~~~~~~~Maintainer: Joe Black <joeblack949@gmail.com>Repository: https://www.github.com/joeblackwaslike/testdockerDescription-----------Unit Testing for docker containers.Usage:------* Execute `testdocker init` in the root of your project folder to create the`tests` directory.* Modify class attributes in `tests/tests.py` according to the directions inthe docstrings.* Add any additional tests as methods of the classes in the same style as:class:`unittest.TestCase`.* Run tests with `tests/run` or `tests/run local`.
Package: testdoubles
===========testdoubles===========.. image:: https://pypip.in/v/testdoubles/badge.png:target: https://crate.io/packages/testdoubles?version=latest:alt: Latest PyPI version.. image:: https://pypip.in/d/testdoubles/badge.png:target: https://crate.io/packages/testdoubles?version=latest:alt: Number of PyPI downloads.. image:: https://travis-ci.org/testsuite/testdoubles.png?branch=master:target: https://travis-ci.org/testsuite/testdoubles:alt: Build Status.. image:: https://coveralls.io/repos/testsuite/testdoubles/badge.png?branch=master:target: https://coveralls.io/r/testsuite/testdoubles?branch=master:alt: Coverage Status.. image:: https://www.versioneye.com/python/testdoubles/badge.png:target: http://www.versioneye.com/python/testdoubles/:alt: Dependencies StatusTestdoubles is a python library that provides mocks, stubs, fakes and dummy objects.* Free software: BSD3 license* Documentation: http://testdoubles.rtfd.org.Features--------* TODOHistory-------0.1.0 (2013-10-03)++++++++++++++++++* First release on PyPI.
Package: teste3-pacotes-maurohf12
Autora do Projeto: Karina KatoAula: Descomplicando a criaÃ§Ã£o de pacotes de processamento de imagens em PythonTecnologia: PythonData: 02/03/2022Image_ProcessingThe package "image_processing-test" is used to:Processing:Histogram matching;Structural similarity;Resize image;Utils:Read image;Save image;Plot image;Plot result;Plot Histogram;InstallationInstalaÃ§Ã£o das Ãºltimas versÃµes de "setuptools" e "wheel"py -m pip install --upgrade pip
py -m pip install --user setuptools wheel twineTenha certeza que o diretÃ³rio no terminal seja o mesmo do arquivo "setup.py"py setup.py sdist bdist_wheelApÃ³s completar a instalaÃ§Ã£o, verifique se as pastas abaixo foram adicionadas ao projeto:build;dist;image_processing_test.egg-info.Basta subir os arquivos, usando o Twine, para o Test Pypi:py -m twine upload --repository-url https://test.pypi.org/legacy/ dist/*ApÃ³s rodar o comando acima no terminal, serÃ¡ pedido para inserir o usuÃ¡rio e senha. Feito isso, o projeto estarÃ¡ hospedado no Test Pypi.hospedÃ¡-lo no Pypi diretamente.
Use the package managerpipto install package_namepipinstallwallet_python_pacotes_imagensLocal InstallationInstalaÃ§Ã£o de dependÃªnciaspip install -r requirements.txtInstalaÃ§Ã£o do PacoteUse o gerenciador de pacotespip install -i https://test.pypi.org/simple/ image-processing-testpara instalar image_processing-testpipinstallimage-processing-testAuthorMauro HenriqueLicenseMIT