Saravanan666/pypi-org huggingface dataset:
Package: validate-parameters
validate_parameters-------------------校验函数参数的工具- 校验类型.. code :: python@validParam(a=int)def func(a, b=1, *args, **kwargs):return Result().data = a, b, args, kwargs| 正确时，返回值：func定义的Result()对象（由用户自己定义）| 错误时，返回值：Result()对象，.. code :: pythonResult().flag = False,Result().msg = 'func() parameter validation fails, param: a, value: a(str), msg(None)'Result().code/status = -3000* 自定义函数.. code :: pythondef max_11(x):if x> 11:return True, 'msg', 'code'else:return False, 'msg', 'code'@validParam(a=int, b=max_11)def func(a, b=1, *args, **kwargs)return Result().data = a, b, args, kwargs| 其中，msg和code为用户自定义，且为可选参数| 正确时，返回值：func定义的Result()对象（由用户自己定义）| 错误时，返回值：Result()对象，.. code :: pythonResult().flag = False,Result().msg = 'func() parameter validation fails, param: a, value: a(str), msg(*{{ msg }}*)'Result().code/status = *{{ code }}** 校验类型和函数.. code :: python@validParam(a=int, b=(int, max_11))def func(a, b=1, *args, **kwargs)return Result().data = a, b, args, kwargs| 其中，msg和code为用户自定义，且为可选参数| 正确时，返回值：func定义的Result()对象（由用户自己定义）| 错误时，返回值：Result()对象，.. code :: pythonResult().flag = False,Result().msg = 'func() parameter validation fails, param: a, value: a(str), msg(*{{ msg }}*)'Result().code/status = *{{ code }}*Result------- 返回值对象Result()- 支持Result().code和Result()["code]取值- 支持Result().code=1和Result()["code]=1赋值- 支持转json格式，json.dumps(Result(), defaults=Result().to_json)- 支持转dict对象，Result().dict()- 默认参数包括：.. code :: pythonflag=True, data=None, code=None, msg=None, status=None, return_values=None上传至pypi------::python setup.py bdist_wheel --universaltwine upload dist/*| *username: chenyang21 passwd: devops58ganji*| *需要更新LICENSE里的version，否则上传会失败*
Package: validate-pip-version
validate-pip-versionCLI tool to validate the version of a local PIP package against its currently published version. Intended to be used as part of a CI build.InstallationFirst Install:pip install validate-pip-versionUpgradingpip install validate-pip-version --upgradeGitHub Actions Examplehttps://github.com/werzl/validate-pip-version/blob/master/.github/workflows/CI.ymlname: CI

env:
  PACKAGE_NAME: <package_name>
  VERSION_FILE_PATH: <path_to__init__.py>

on:
  pull_request:
    branches: [master]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: "3.8"
          architecture: "x64"
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install setuptools wheel twine
      - name: Run Version Checker
        run: |
          pip install ${{ env.PACKAGE_NAME }}
          pip install validate-pip-version
          validate_pip_version check-init-file -n ${{ env.PACKAGE_NAME }} --init_file_path ${{ env.VERSION_FILE_PATH }}
      - name: Build
        run: python setup.py sdist bdist_wheelUsagevalidate_pip_version [OPTIONS] COMMAND [ARGS]check-init-fileRetrieves the local package version from aninit.py, by matching the string '__version__'.validate_pip_version check-init-file -n <package_name> --init_file_path <path_to__init__.py>OptionsOptionRequiredDescription-n, --package_nameRequiredName of the package on PyPi--init_file_pathRequiredRelative file path for the __init__.py file(uses current working dir)--helpOptionalShow help message and exitcheck-setup-fileRetrieves the local package version from an setup.py file, by matching the string 'version=' and stripping away spaces/newlines.validate_pip_version check-setup-file -n <package_name> --setup_file_path <path_to_setup.py>OptionsOptionRequiredDescription-n, --package_nameRequiredName of the package on PyPi--setup_file_pathRequiredRelative file path for the setup.py file(uses current working dir)--helpOptionalShow help message and exit
Package: validate-plist-xml
validate_plist_xmlThis python module will validate Apple XML Plist files.This is pure python and runs on any operating system,tested onWindows, Mac, and Linux.Currently targets Python 3 only but would like to add Python 2 support.By default configured to check files with the following extensions:('.recipe', '.plist', '.profile')By default, runs in the current working directory against all files in that directory and all subdirectories.This module depends upon thelxmlmodule. (which is handled bypipautomatically)The code is found withinsrc/validate_plist_xml/validate_plist_xml.pywithin thegit repo.This module checks that the plist is valid XML and meets the Apple Plist DTD here:https://www.apple.com/DTDs/PropertyList-1.0.dtdInstall with pip:pip install validate-plist-xmlThis will installlxmlif not already installed.Usage Examples:Run as Python Script:The python script can be called directly:$ python3 src/validate_plist_xml/validate_plist_xml.py 
XML Syntax Error in: ./tests/bad/example-bad-xml-tags.recipe
Opening and ending tag mismatch: BAD_TAG line 4 and dict, line 15, column 8 (example-bad-xml-tags.recipe, line 15)
Failed DTD Validation: ./tests/bad/example-bad-dtd.recipe
Element dict content does not follow the DTD, expecting (key , (array | data | date | dict | real | integer | string | true | false))*, got (key astring key string key dict key string key array ), line 4
2 errors found in 3 plist xml filesRun as module:If installed through pip as a module, then it can be run like this:$ python3 -m validate_plist_xml
XML Syntax Error in: ./tests/bad/example-bad-xml-tags.recipe
Opening and ending tag mismatch: BAD_TAG line 4 and dict, line 15, column 8 (example-bad-xml-tags.recipe, line 15)
Failed DTD Validation: ./tests/bad/example-bad-dtd.recipe
Element dict content does not follow the DTD, expecting (key , (array | data | date | dict | real | integer | string | true | false))*, got (key astring key string key dict key string key array ), line 4
2 errors found in 3 plist xml filesUse as GitHub Action:https://github.com/jgstew/jgstew-recipes/blob/main/.github/workflows/plistlint.yaml---
name: plistlint

on:
  push:
    paths:
      - "**.plist"
      - "**.recipe"
      - ".github/workflows/plistlint.yaml"
  pull_request:
    paths:
      - "**.plist"
      - "**.recipe"
      - ".github/workflows/plistlint.yaml"

jobs:
  plistlint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.8

      - name: Install validate-plist-xml
        run: pip install validate-plist-xml

      - name: Lint Plist files
        run: python3 -m validate_plist_xmlUse in another Python script:import validate_plist_xml

# The default folder is `.` but could be any folder
validate_plist_xml.validate_plist_xml.main('.')Use as Python command:python3 -c "import validate_plist_xml; validate_plist_xml.validate_plist_xml.main('.')"
Package: validate-pypi-name
Validate PyPi namesSuper simple CLI to quickly validate your PyPi package names. Available here:https://pypi.org/project/validate-pypi-name/UsageAssume you want to check if the package "package_name" is available.pip3 install validate-pypi-name
valid -n package_nameIf you want to run the "intensive workload" version, where we iterate through all possible collisions, use the -i tagpip3 install validate-pypi-name
valid -n package_name -iThat's about it.
Package: validate-pyproject-schema-store
validate-pyproject-schema-storeThis provides a versioned copy of SchemaStore forvalidate-pyproject. You
can pin this to get a stable set of schema files.UsageThe following should be supported:Installing alongside validate-pyprojectJust usepip install validate-pyproject-schema-storewherever you havevalidate-pyproject[all]installed. You can "inject" it if using pipx, or use--pip-argsif usingpipx run.In pre-commit, this would be:repos:-repo:https://github.com/abravalheri/validate-pyprojectrev:<insert here>hooks:-id:validate-pyprojectadditional_dependencies:[validate-pyproject[all],validate-pyproject-schema-store]Direct usageFor pre-commit or pipx, you can simplify this a bit by using this package
directly. That looks like this:pipxrunvalidate-pyproject-schema-store[all]Or for pre-commit:repos:-repo:https://github.com/henryiii/validate-pyproject-schema-storerev:<insert here>hooks:-id:validate-pyprojectThis also has the benefit that the version will be pinned and updated by
pre-commit automatically.DevelopingThis project useshatch. You can run the sync script on Python 3.11+ by
running:hatchrunsync
Package: validate-rg
validate-rgValidates Brazilian RG (SSP SP)FeaturesRG Validation with maskRG Validation without maskModes of use#!/usr/bin/pythonimportvalidate_rg# Without maskvalidate_rg.is_valid('505675092')# True# With maskvalidate_rg.is_valid('50.567.509-2')# Trueor#!/usr/bin/pythonfromvalidate_rgimportis_valid# Without maskis_valid('111111111')# False# With maskis_valid('11.111.111-1')# FalseAuthorJoão FilhoGithubCreditsThis package was created with Cookiecutter and thecs01/cookiecutter-pypackageproject template.Cookiecuttercs01/cookiecutter-pypackageDV calculation
Package: validatesns
Validate integrity of Amazon SNS messages.Verifies cryptographic signature.Checks signing certificate is hosted on an Amazon-controlled URL.Requires message be no older than one hour, the maximum lifetime of an SNS message.Licence:MIT.Quick start$pipinstallvalidatesnsimportvalidatesns# Raise validatesns.ValidationError if message is invalid.validatesns.validate(decoded_json_message_from_sns)GotchasThevalidatefunction downloads the signing certificate on every call. For performance reasons, it’s worth caching certificates - you can do this by passing in aget_certificatefunction.This takes aurl, and returns the certificate content. Your function could cache to the filesystem, a database, or wherever makes sense.ContributeGithub:https://github.com/nathforge/validatesns
Package: validateurgenerique
Package Name: validateurgenerique
Overview
This package provides utilities for data validation and transformation. It includes modules for schema validation, form validation, and various data transformations.Installation
You can install the package using pip from PyPI or directly from GitLab.From PyPI:
pip install validateurgenerique
From GitLab:
pip install validateurgenerique --index-urlhttps://gitlab.com/api/v4/projects/54359493/packages/pypi/simpleModules
schemavalidator.py
This module includes a SchemaValidator class for validating data against JSON schemas.Example usage:
from validateurgenerique import SchemaValidatorCreate a schema validator instancevalidator = SchemaValidator(schema=my_schema)Validate data against the provided schemais_valid, errors = validator.validate_schema(data=my_data)
transformation.py
This module contains a Transformations class with various data transformation methods.Example usage:
from validateurgenerique import TransformationsCreate a transformations instancetransformer = Transformations(date_formats=["%Y-%m-%d"])Transform date to a different formattransformed_date = transformer.transform_date(date_str="2024-02-05")
formvalidator.py
This module provides a FormValidator class for validating data against a specified form schema.Example usage:
from validateurgenerique import FormValidatorCreate a form validator instancevalidator = FormValidator(schema=my_form_schema)Validate data against the provided form schemavalidation_errors = validator.validate_schema(data=my_form_data)
Example Use Cases
Schema Validation:
from validateurgenerique import SchemaValidatorCreate a schema validator instancevalidator = SchemaValidator(schema=my_schema)Validate data against the provided schemais_valid, errors = validator.validate_schema(data=my_data)
Data Transformation:
from validateurgenerique import TransformationsCreate a transformations instancetransformer = Transformations(date_formats=["%Y-%m-%d"])Transform date to a different formattransformed_date = transformer.transform_date(date_str="2024-02-05")
Form Validation:
from validateurgenerique import FormValidatorCreate a form validator instancevalidator = FormValidator(schema=my_form_schema)Validate data against the provided form schemavalidation_errors = validator.validate_schema(data=my_form_data)
Contributors
Gharbi Youssef
License
This package is distributed under the MIT license.
Package: validate-utf8
validate-utf8Python library and CLI for validating UTF-8 text.Install it like this:pip3 install validate-utf8The command-line tool works like this:$ validate-utf8 20081104__wv__general__mason__precinct.csv
invalid continuation byte
Mason,28 COURTHOUSE-GROUND FLOORá,,,REP,Straight Ticket,15
                                ^
invalid continuation byte
Mason,28 COURTHOUSE-GROUND FLOORá,,,DEM,Straight Ticket,21
                                ^Use it as a library like this:from validate_utf8 import find_utf8_errors

errrors = find_utf8_errors(bytestring):
if not errors:
    print("It is valid")
else:
    print(errors)
Package: validate-version-code
Python package to validate version codes.How do I install this package?As usual, just download it using pip:pipinstallvalidate_version_codeTests CoverageSince some software handling coverages sometimes
get slightly different results, here’s three of them:Usage exampleHe’s a basic how-to:fromvalidate_version_codeimportvalidate_version_codevalid_version_code="1.2.3"invalid_version_code="beta.3"assertvalidate_version_code(valid_version_code)assertnotvalidate_version_code(invalid_version_code)
Package: validating-models
Validating ModelsThis project supports explainable AI on Semantic Constraint Validation.
Check out theGitHub pagesfor the documentation of the source code.The underlying research is presented in:Julian Alexander Gercke.Supporting Explainable AI on Semantic Constraint Validation. Master's Thesis, Leibniz Universität Hannover, June 2022. DOI:10.15488/12527
Package: validation-app-engine
ContentsOverviewWhy Required ?What is Validation App Engine ?Target AudianceKey HighlightsArchitectureInstallationNetwork Resource Realization and ValidationSupported Protocols/ModesTopology and workflowHow to validate trafficOverviewWhy Required ?Distributed Workload Resource Validation and Metrics Collection is a key aspect of scale testing today.
resources can be Network Resource, Memory Resource etc.Currently, available resource validators don’t scale economically and are limited when it comes to customization.
Metrics and Analytics tools are also extremely expensive and do not always work with some products.
That may not be generating metrics that are compatible with the platform. Reliability and Serviceability
testing is not fully automated today as teams struggle to simulate user workloads and analyze the data from
the same. Products need metrics to measure performance and reliability.What is Validation App Engine ?validation-app-enginean agent based distributed workload resource validation and monitoring engine that lets various
quality and reliability engineering teams to validate their products at large scale.
The remote agents can be programmed to generate resource patterns OR monitor resources and send back information
to a central analytics layer for further analysis teams can make several inferences by analyzing the data generated.Target AudianceQuality TeamsPerformance Engineering TeamsChaos/Reliability Engineering TeamsDevelopment TeamsKey HighlightsFramework agnostic.Platform agnostic (Windows / Linux).It also support validation against Linux network namespaces.Cloud agnostic (vSphere, AWS, Azure).Self-contained Repo / Binaries.Support for client / servers running on any port.Integration with any DB (natively works on SQLite DB and RiakDB).Utility for validating Traffic, Resource and Realization patterns.Build utilities on top of it to manage or monitor remote endpoints.ArchitectureAs per current architecture, it can support number of resources.
Currently it supports only network resources, where it generates, validates and monitor network traffic.
In future, other resources support can be added based on interfaces provided in the code.
Under network resource it supports -Distributed traffic generationValidating traffic patternsTraffic rule realizationLocal and central store of traffic stats.InstallationPlease referInstallation Guidefor detailed installation.Network Resource Realization and ValidationSupported Protocols/ModesProtocols -TCPUDPModes -ClientServerTopology and workflowPlease referWorkflow Guidefor detailed description on basic topologies and workflow.How to validate trafficPlease referExample Traffic Validation Guidefor detailed example on how to generate, realize traffic rules and validate traffic.
Package: validation-component-bootstrap-utils
Utilities for Bootstrapping Validation ComponentsUtilities for Bootstrapping Validation ComponentsValidation Component Bootstrap Utils is a comprehensive set of Python scripts and utilities designed to streamline the process of parsing and validating tab-delimited or comma-separated files. This toolkit automates the generation of essential modules, making it easier to integrate file validation components into your projects.Modules Generatedvalidator.py: This module provides a robust set of functions and classes for validating data within the specified file format. It ensures that the content adheres to defined standards, offering a solid foundation for data integrity.parser.py: The parser module facilitates efficient extraction of data from tab-delimited or comma-separated files. It is engineered to handle various data structures and file layouts, ensuring flexibility and adaptability to diverse use cases.record.py: The record module defines structures for organizing and representing individual records within the files. It lays the groundwork for maintaining data consistency and ease of manipulation during the parsing and validation processes.file_validation.py: This module orchestrates the validation and parsing processes, providing a unified interface for users. It acts as the entry point for utilizing the toolkit, promoting simplicity and coherence in handling file operations.FeaturesThe following exported console script is available for use:bootstrap-validation-component.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-12-19)First release on PyPI.
Package: validations-common
A collection of Ansible roles and playbooks to detect and report potential
issues during deployments.The validations will help detect issues early in the deployment process and
prevent field engineers from wasting time on misconfiguration or hardware
issues in their environments.Free software:Apache_licenseDocumentation:https://docs.openstack.org/validations-common/latest/Release notes:https://docs.openstack.org/releasenotes/validations-commons/Source:https://opendev.org/openstack/validations-commonBugs - Upstream:https://bugs.launchpad.net/tripleo/+bugs?field.tag=validationsBugs - Downstream:https://bugzilla.redhat.com/buglist.cgi?component=validations-common&product=Red%20Hat%20OpenStackInstallationRecommended processThere are several different ways to install validations-common.
However it isrecommendedto both install and use
the package inside python virtual environment.At the command line usingpip.$python3-mpipinstallvalidations-commonOr, if you havevirtualenvwrapperinstalled.$mkvirtualenvvalidations-common$python3-mpipinstallvalidations-commonInstallation with package managerAlternatively it is possible to install validations-common using package manager.Such asyumordnf…$yum|dnfinstallvalidations-commonor the more moderndnf.$dnfinstallvalidations-commonUsageOnce the validations-common project has been installed,
navigate to the chosen share path, usually/usr/share/ansibleto access the installed roles, playbooks, and libraries.While the validations-common can be run by itself,
it nonetheless depends on Ansible and validations-libs.
Therefore it isn’t recommended to use only validations-common.Validations Callbackshttp_json callbackThe callbackhttp_jsonsends Validations logs and information to an HTTP
server as a JSON format in order to get caught and analysed with external
tools for log parsing (as Fluentd or others).This callback inherits fromvalidation_jsonthe format of the logging
remains the same as the other logger that the Validation Framework is using
by default.To enable this callback, you need to add it to the callback whitelist.
Then you need to export your http server url and port:export HTTP_JSON_SERVER=http://localhost
export HTTP_JSON_PORT=8989The callback will post JSON log to the URL provided.
This repository has a simple HTTP server for testing purpose under:tools/http_server.pyThe default host and port are localhost and 8989, feel free to adjust those
values to your needs.Running the molecule testsIn order to run the molecule tests locally on your local machine, please follow
the instructions documented in theTripleo ValidationsDevelopers Guide.
Package: validations-engine
Validations EngineEngine for creating and running validations for general purposes.SourceDownloadsPageInstallation CommandPyPiLinkpip install validations-engineBuild statusDevelopStableDocumentationSonarPurposeThis automation engine was conceived to simulate key communication/integration lines between services that compose the
core jobs of the data engineering pipeline at QuintoAndar, to guarantee there is no failures in the pipeline (andearly catcheventual failures).
But it can execute validations for general purposes.Structure and the engine core conceptsThis engine is composed by the Executors and the ValidationSuites.ExecutorsExecutors are responsible for executing all the respective validation suites that inherit them.
A suite can inherit a custom executor or theBaseValidationSuitesExecutor.
The executor may contain default and generic code and tests for a group of suites. For example, we may have one executor
with features for validating a group ofDatabasesand another for validating someAPIs.Validation SuitesA validation suite works like a Python unit test class. Once you define the validation methods (following the name pattern),
these methods will be run by the engine.Inside the validation suite class, you may implement into the validation methods the validations you want to perform.
You can implement a custom validation inside your suite or if it is a common validation you may want to generalize it
and use a custom executor likeDatabaseValidationSuitesExecutorfor sharing the same validation among other suites that inherits it.
Every suite must inherit from an executor, in order to be parsed and run. So it should inherit from theBaseValidationSuitesExecutoror from a custom one (that inherits from the base executor).Thevalidation_*methods inside the suite and inside the executor classes will run automatically by the validation engine.To check library main features and full documentation you can check theValidations Engine's Documentation, which is hosted by Read the Docs.LicenseApache License 2.0ContributingAll contributions are welcome! Feel free to open Pull Requests. Check the development and contributingguidelinesdescribed inCONTRIBUTING.mdMade with :heart: by theData Engineeringteam fromQuintoAndar
Package: validatish
Validatish is a simple validation library designed to be a good building block for validation structures. It implements some basic validators in functional form (i.e. simple function calls) or in class form (i.e. instantiate a class and then just call the validate method). Have a look at the project site athttp://validat.ish.ioChanglog athttp://github.com/ish/validatish/raw/master/CHANGELOG
Package: validator-and-token-generator
DescriptionValidator_and_token_generator is a package created for a class project to validate certain strings such as credit/debit card numbers, and generate alphanumeric tokens.Install validator_and_token_generator from PyPi.pipinstallvalidator_and_token_generatorExample# Import libraryimportvalidator_and_token_generator# Generate token_token=validator_and_token_generator.token_generator(length=10)