Saravanan666/pypi-org huggingface dataset:
Package: dummylibrarylib
A template to create python librariesDocumentation:https://dummylibrarylib.readthedocs.org/en/latestDevelopment WorkflowThe workflow supports the following stepslinttestbuilddocumentuploadgraphThese actions are supported out of the box by the corresponding scripts under _CI/scripts directory with sane defaults based on best practices.
Sourcing setup_aliases.ps1 for windows powershell or setup_aliases.sh in bash on Mac or Linux will provide with handy aliases for the shell of all those commands prepended with an underscore.The bootstrap script creates a .venv directory inside the project directory hosting the virtual environment. It uses pipenv for that.
It is called by all other scripts before they do anything. So one could simple start by calling _lint and that would set up everything before it tried to actually lint the projectOnce the code is ready to be delivered the _tag script should be called accepting one of three arguments, patch, minor, major following the semantic versioning scheme.
So for the initial delivery one would call$ _tag –minorwhich would bump the version of the project to 0.1.0 tag it in git and do a push and also ask for the change and automagically update HISTORY.rst with the version and the change provided.So the full workflow after git is initialized is:repeat as necessary (of course it could be test - code - lint :) )codelinttestcommit and pushdevelop more through the code-lint-test cycletag (with the appropriate argument)buildupload (if you want to host your package in pypi)document (of course this could be run at any point)Important InformationThis template is based on pipenv. In order to be compatible with requirements.txt so the actual created package can be used by any part of the existing python ecosystem some hacks were needed.
So when building a package out of thisdo notsimple call$ python setup.py sdist bdist_eggas this will produce an unusable artifact with files missing.Instead use the provided build and upload scripts that create all the necessary files in the artifact.Project FeaturesTODOHistory0.0.1 (27-10-2021)First code creation0.1.0 (27-10-2021)Added first class
Package: dummylog
dummylogis the open source python package to help the python module/plugin developers to create/update the log files in simple syntax and easy format. If you are looking for a single file based log, then dummylog is for you to keep your simple logsIf you are usingFlask APIor any API decorators withdummylog, it will create a new log every time since decorators will trigger the function as every new instance.InstallationInstall using pip.Offical Python Package Here!!pipinstalldummylog(OR)Clone this Repository. Run this from the root directory to installpythonsetup.pyinstallLicenseThe license for dummylog is MIT licenseNeed help?Stuck on your dummylog code or problem? Any other questions? Don't
hestitate to send me an email (selva221724@gmail.com).Usage1. Import Packageimportdummylog2. Initialize the dummylog Objectdl=dummylog.DummyLog()dummylog.DummyLog() parametersS.NoParametersDescriptionTypeDefault Value1log_nameName of the log instance/filestring"temp"2logging_levellevel of logging that user neededstring"debug"3string_formatFormatter for loggingstring"%(asctime)s: %(levelname)s: %(message)s"3datetime_formatDate-time format for loggingstring"%m/%d/%Y %I:%M:%S %p"4log_on_folderTrue if you want to save them in a folderboolTrue5log_folder_nameName of the folder to save the logsstring"logs"3. Enjoy Loggingdl.logger.info('Log File is Created Successfully')dl.logger.info('Unmayanaa Google Competitors')dl.logger.error('Vada poche')This will create a log file in the root directory of the python script.4. To Kill the dummylog instancedl.kill()Note:Do not usedummylogin case if you are using the logging module in the same script for other purposes. it may conflict with the logging thread and append things to gather.5. To Create Mutiple Logsdl_1=dummylog.DummyLog(log_name='one')dl_1.logger.info('Log File is Created Successfully')dl_1.logger.info('Unmayanaa Google Competitors')dl_1.logger.error('Vada poche')dl_2=dummylog.DummyLog(log_name='two')dl_2.logger.info('Log File is Created Successfully')dl_2.logger.info('Unmayanaa Google Competitors')dl_2.logger.error('Vada poche')It will create two.logfiles:logs
â”œâ”€â”€ one.log
â””â”€â”€ two.logSample Log File View
Package: dummyML
dummyML packageThe dummyML Python package is designed for data analysts to do automated data analysis on tabular data using machine learning algorithms. The package implemented all the components, data preprocessing, data splitting, model selection, model fitting and model evaluation, required for defining pipelines to automatically analyze tabular data.InstallationInstall dummyML package by running:pip install dummyMLon the command line of a Linux system or the Anaconda Prompt on a Windows system. If you don't have root privileges, sometimes you need to add--userafter the above commands, then pip will install the package in your home directory.If you want to install a specific version, please visithttps://pypi.org/project/dummyML/to find the exact version number. For example, you can install version 0.9.3 bypip install dummyML==0.9.3.User guideThe user guide is available athttps://dummyml.readthedocs.io/en/latest/.
Package: dummy-module
dummy_module (v0.0.2)DESCRIPTION_SHORTJust a zero/dummy pypi packageDESCRIPTION_LONGdesigned for test applications which handle with pypi packages (install/uninstall/check versions/upgrade).with this module you dont need to handle with some specific pkg!FeaturesIt doesn't do anything!LicenseSee theLICENSEfile for license rights and limitations (MIT).Release historySee theHISTORY.mdfile for release history.Installationpip install dummy-moduleImportfromdummy_moduleimport*USAGE EXAMPLESSee tests and sourcecode for other examples.
Package: dummymp
SummaryDummyMPis a Python module that simplifies parallelized computing for
those not familiar with Python’s multiprocessing API. It includes
easy-to-use functions for deploying and managing computationally
expensive operations.Simply said, it’s a library that you can use to make your programs
faster!DocumentationOnline documentation can be foundhere.You can find a Markdown version of the guide atGitHub.Bugs/IssuesDummyMP is currently in beta, so there’s bound to be a few bugs.
Please report them to ourissues tracker.
Package: dummy-notebookutils
Dummy NotebookUtils/MsSparkUtils for PythonThis is a pure dummy interfaces package which mirrorsMsSparkUtils' APIsofAzure Synapse Analyticsfor python users,customer of Azure Synapse Analytics can download this package from PyPi to generate the build.Getting startedInstall dummy_notebookutils with pip:pipinstalldummy-notebookutilsExamplesfrom notebookutils import mssparkutils
mssparkutils.fs.ls("/")NOTICE: again, the package only mirrors APIs of synapse mssparkutils without actual functionality. The main goal is to help customer generating the local build. You always need upload your built package to synapse workspace for end to end testing.
Package: dummy_package
py_stringmatchingThis project seeks to build a Python software package that consists of a comprehensive and scalable set of string tokenizers (such as alphabetical tokenizers, whitespace tokenizers) and string similarity measures (such as edit distance, Jaccard, TF/IDF). The package is free, open-source, and BSD-licensed.Important linksProject Homepage:https://sites.google.com/site/anhaidgroup/projects/py_stringmatchingCode repository:https://github.com/anhaidgroup/py_stringmatchingUser Manual:http://anhaidgroup.github.io/py_stringmatching/v0.2.x/index.htmlTutorial:http://anhaidgroup.github.io/py_stringmatching/v0.2.x/Tutorial.htmlHow to Contribute:http://anhaidgroup.github.io/py_stringmatching/v0.2.x/Contributing.htmlDeveloper Manual:http://pages.cs.wisc.edu/~anhai/py_stringmatching/v0.2.0/dev-manual-v0.2.0.pdfIssue Tracker:https://github.com/anhaidgroup/py_stringmatching/issuesMailing List:https://groups.google.com/forum/#!forum/py_stringmatchingDependenciespy_stringmatching has been tested on Python 2.7, Python 3.3, Python 3.4 and Python 3.5.The required dependencies to build the package are NumPy 1.7.0 or higher,
Six, and a C or C++ compiler. For the development version, you will also need Cython.Platformspy_stringmatching has been tested on Linux, OS X and Windows.
Package: dummy_package1
py_stringsimjoinThis project seeks to build a Python software package that provides scalable implementation of string similarity joins over two tables, for commonly used similarity measures such as Jaccard, Dice, cosine, overlap, overlap coefficient and edit distance. The package is free, open-source, and BSD-licensed.Important linksProject Homepage:https://sites.google.com/site/anhaidgroup/projects/py_stringsimjoinCode repository:https://github.com/anhaidgroup/py_stringsimjoinIssue Tracker:https://github.com/anhaidgroup/py_stringsimjoin/issuesMailing List:https://groups.google.com/forum/#!forum/py_stringsimjoinPlatformspy_stringsimjoin has been tested on Linux, OS X and Windows.
Package: dummy-package-manager
dummy_package_managerEffortlessly create and manage dummy Python packages.The dummy_package_manager module offers a streamlined approach to generating
and handling dummy Python packages along with their optional dependencies.
This tool is designed to simplify testing, experimentation, and development
tasks that involve temporary package setups.Key Features:Create temporary dummy packages with optional dependencies.Simplify the process of setting up isolated testing environments.Easily manage package installations and uninstallations.Designed for use in testing, experimentation, and development workflows.Installationdummy_package_manager can be installed using pip:pipinstalldummy_package_manager# to make sure you have the latest versionpipinstall-Udummy_package_manager# latest available code basepipinstall-Ugit+https://github.com/eftalgezer/dummy_package_manager.gitUsageYou can use theDummyPackageclass as a context manager to create and manage dummy packages. Here's an example:fromdummy_package_managerimportDummyPackagewithDummyPackage("my_package",requirements=["dependency1","dependency2"])asdummy_pkg:dummy_pkg.install()# Your code using the dummy packagedummy_pkg.uninstall()# not necessary when exiting the context managerUnit Testsdummy_package_manager comes with a comprehensive set of unit tests to ensure its functionality. To run the tests, navigate to the main directory and execute:pytestContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseGNU General Public License v3.0
Package: dummypdf
Paper size and number of pages are configurable. Files can be used to test pdf
manipulation tools.Examples :One page A4 paper:example1.pdf:dummypdf A4Six pages, a third of an A4 paper:example2.pdf:dummypdf -n 6 -p 21cmx99mmor:dummypdf 21cmx99mm:6A pdf with different page formats:different.pdf:dummypdf A4 A5:landscapewestWhat’s new?Seechangelog.Download and installSee the end of list for a (quick and dirty) Debian package.From sources:Download:https://pypi.python.org/pypi/dummypdfInstall (in avirtualenv, if you do not want to mess with your distribution installation system):python3 setup.py installFrom pip:pip install dummypdfQuick and dirty Debian (and Ubuntu?) packageThis requiresstdebto be installed:python3 setup.py --command-packages=stdeb.command bdist_deb
sudo dpkg -i deb_dist/dummypdf-<VERSION>_all.debDocumentationThe compiled documentation is available onreadthedocsTo compile it from source, download and run:cd doc && make html
Package: dummyrdd
DummyRDD[![Coverage Status](https://coveralls.io/repos/github/wdm0006/DummyRDD/badge.svg?branch=master)](https://coveralls.io/github/wdm0006/DummyRDD?branch=master)
[![Build Status](https://travis-ci.org/wdm0006/DummyRDD.svg?branch=master)](https://travis-ci.org/wdm0006/DummyRDD)Contributors[Henrique Souza](https://github.com/htssouza)[Will McGinnis](https://github.com/wdm0006)[Rob Harrigan](https://github.com/codebynumbers)OverviewA test class that walks like and RDD, talks like an RDD but is just a list.Contains 3 primary classes:SparkConfSparkContextRDDAll of which implement the exact same API as the real spark methods, but use a simple
python list as the actual datastore.  Many functions such as the Hadoop API, partitioning, complex
operations, and other things are not implemented.  See below for detailed list of implemented functions and
their caveats.Note that for now this is experimental, and may later be useful for testing or development, but anything
developed using this should always be checked on real spark to make sure that things actually work there. Because
none of the code is actually distributed in this environment, some things will behave differently.It is intended that this library can be used as a drop in replacement for a real spark context, without erroring out
but maybe not actually doing anything (in the case of irrelevant configuration options, for example).Currently there is no support for the dataframe api, or for that matter most features of anything, very much
still a work in progress.ExampleA quick example:from dummy_spark import SparkContext, SparkConfsconf = SparkConf()
sc = SparkContext(master=’’, conf=sconf)
rdd = sc.parallelize([1, 2, 3, 4, 5])print(rdd.count())
print(rdd.map(lambda x: x**2).collect())yields:5
[1, 4, 9, 16, 25]Methods ImplementedSparkConfSparkConf has everything implemented, but nothing is actually ever set.  There are no real configuration settings for
the dummy version, so the object simply contains a dictionary of configuration parameters. Implemented functions are therefore:__init__()contains()get()getAll()set()setAll()setAppName()setExecutorEnv()setIfMissing()setMaster()setSparkHome()toDebugString()SparkContextImplemented functions are:__init__()__enter__()__exit__()defaultMinPartitions()defaultParallelism()emptyRDD()parallelize()NewAPIHadoopRDD() (only for elasticsearch via elasticsearch-py)range()startTime()stop()textFile() (including from s3 via tinys3)version()RDDImplemented functions are:__init__()__add__()__repr__()cache()cartesian()checkpoint()cogroup()collect()context()count()countApprox()countApproxDistinct()distinct()filter()first()flatMap()flatMapValues()foreach()foreachPartition()getNumPartitions()glom()groupBy()groupByKey()id()intersection()isEmpty()lookup()map()mapPartitions()mapValues()max()mean()meanApprox()min()name()persist()reduceByKey()repartitionAndSortWithinPartitions()sample()setName()sortBy()sortByKey()sum()take()takeSample()toLocalIterator()union()zip()zipWithIndex()
Package: dummyserial
Emulates pyserial’s write/read responses from a serial port.Works by suppling a dictionary of responses to various paramters.For example:ds_responses = {
    'taco': 'yum'
}Wouldread()yumfor awrite()oftaco.Derived from Jonas Berg’s ‘dummy_serial.py’.SourceGithub:https://github.com/ampledata/dummyserialAuthors‘dummyserial’ Stand-alone Python Module: Greg Albrecht <gba@orionlabs.io>‘dummy_serial.py’ Mock Fixture: Jonas Berg <pyhys@users.sourceforge.net>CopyrightCopyright 2016 Orion Labs, Inc.LicenseApache License, Version 2.0
Package: dummy_spark
DummyRDD[![Coverage Status](https://coveralls.io/repos/github/wdm0006/DummyRDD/badge.svg?branch=master)](https://coveralls.io/github/wdm0006/DummyRDD?branch=master)
[![Build Status](https://travis-ci.org/wdm0006/DummyRDD.svg?branch=master)](https://travis-ci.org/wdm0006/DummyRDD)Contributors[Henrique Souza](https://github.com/htssouza)[Will McGinnis](https://gitbhub.com/wdm0006)OverviewA test class that walks like and RDD, talks like an RDD but is just a list.Contains 3 primary classes:SparkConfSparkContextRDDAll of which implement the exact same API as the real spark methods, but use a simple
python list as the actual datastore.  Many functions such as the Hadoop API, partitioning, complex
operations, and other things are not implemented.  See below for detailed list of implemented functions and
their caveats.Note that for now this is experimental, and may later be useful for testing or development, but anything
developed using this should always be checked on real spark to make sure that things actually work there. Because
none of the code is actually distributed in this environment, some things will behave differently.It is intended that this library can be used as a drop in replacement for a real spark context, without erroring out
but maybe not actually doing anything (in the case of irrelevant configuration options, for example).Currently there is no support for the dataframe api, or for that matter most features of anything, very much
still a work in progress.ExampleA quick example:from dummy_spark import SparkContext, SparkConfsconf = SparkConf()
sc = SparkContext(master=’’, conf=sconf)
rdd = sc.parallelize([1, 2, 3, 4, 5])print(rdd.count())
print(rdd.map(lambda x: x**2).collect())yields:5
[1, 4, 9, 16, 25]Methods ImplementedSparkConfSparkConf has everything implemented, but nothing is actually ever set.  There are no real configuration settings for
the dummy version, so the object simply contains a dictionary of configuration parameters. Implemented functions are therefore:__init__()contains()get()getAll()set()setAll()setAppName()setExecutorEnv()setIfMissing()setMaster()setSparkHome()toDebugString()SparkContextImplemented functions are:__init__()__enter__()__exit__()defaultMinPartitions()defaultParallelism()emptyRDD()parallelize()NewAPIHadoopRDD() (only for elasticsearch via elasticsearch-py)range()startTime()stop()textFile() (including from s3 via tinys3)version()RDDImplemented functions are:__init__()__add__()__repr__()cache()cartesian()checkpoint()cogroup()collect()context()count()countApprox()countApproxDistinct()distinct()filter()first()flatMap()flatMapValues()foreach()foreachPartition()getNumPartitions()glom()groupBy()groupByKey()id()intersection()isEmpty()lookup()map()mapPartitions()mapValues()max()mean()meanApprox()min()name()persist()reduceByKey()repartitionAndSortWithinPartitions()sample()setName()sortBy()sortByKey()sum()take()takeSample()toLocalIterator()union()zip()zipWithIndex()
Package: dummy-transformations
Dummy TransformationsPara usar este repo como plantilla debemos hacer lo siguiente:Presiona el botón verde que diceUse this templateSelecciona como dueño a la organización IslasGECIAgrega el nombre del nuevo módulo de pythonPresiona el botónCreate repository from templateReemplazadummy_transformationspor el nombre del nuevo módulo en:Makefilepyproject.tomltests\test_transformation.pyRenombra el archivodummy_transformations\transformation.pyal nombre del primer archivo del
nuevo móduloCambia la descripción del archivodummy_transformations\__init__.pyRenombra el directoriodummy_transformationsal nombre del nuevo móduloCambia elcodecov_tokendel archivoMakefileLos archivos del nuevo módulo los agregarás en la carpeta que antes se llamabadummy_transformationsy las pruebas en la carpetatests.
Package: dummy_wsgi_framework
Common descriptionDummy WSGI Frameworkis base for your Web-applications or your own WSGI-framework.
It is “unpretentious” and “dummy” (although in fact it is not so “simple” and “stupid” as it may seem at first glance). It was build asJust4Fun-project.Dummy WSGI Frameworkmethodology contain “controllers” and “views”, as pattern “Model-View-Controller” without “Model”.It allows you to work with views generated within controllers or loaded from files, for example, in the basic version of the framework it is just HTML-files.Dummy WSGI Frameworkis potentially expanding:due to open access to the WSGI-interface at the level of application controllers, developed on its (framework) basis;due to the possibility of using arbitrary template engines due to the function of loading view files implemented in the core of the decorator framework;and, in the end, it’s Python, there’s a lot possible.You are free to useDummy WSGI Frameworkas a framework for your Web applications or as a basis for your Web framework. It is not necessary to refer to the fact that You useDummy WSGI Framework, but I would like to have a public review, even if it is not positive.LinksGitHub -https://github.com/BorisPlus/dummy_wsgi_frameworkAuthorBorisPlus
Package: dump
Python wrapper around psql and pg_dump to make it easier to
backup/restore a PostgreSQL database.BackupYou backup per table:$ dump backup --dbname=... --username=...  --password=... --dir=/some/base/path table1 table2 ...RestoreYou can restore the entire backup directory:$ dump restore --dbname=... --username=...  --password=... --dir=/some/base/pathInstallUse pip:$ pip install dumpto install the latest and greatest:$ pip install --upgrade git+https://github.com/Jaymon/dump#egg=dump
Package: dumpall
Dump all: 多种泄漏形式，一种利用方式dumpall 是一款信息泄漏/源代码泄漏利用工具https://github.com/0xHJK/dumpall⚠️警告：本工具仅用于授权测试，不得用于非法用途，否则后果自负！⚠️WARNING：FOR LEGAL PURPOSES ONLY!🤘 Features支持多种泄漏情况利用Dumpall使用方式简单使用asyncio异步处理速度快适用于以下场景：.git源代码泄漏.svn源代码泄漏.DS_Store信息泄漏目录列出信息泄漏TODO:支持更多利用方式优化大文件下载优化多任务调度增强绕过功能项目地址：https://github.com/0xHJK/dumpall在macOS下的Python 3.7中测试通过，建议使用Python 3.7+🚀 QuickStart# pip安装pipinstalldumpall# 查看版本dumpall--version# 手动下载使用gitclonehttps://github.com/0xHJK/dumpallcddumpall# 查看版本python3dumpall.py--version💫 Usage# 下载文件（源代码）dumpall-u<url>[-o<outdir>]# 示例dumpall-uhttp://example.com/.git/
dumpall-uhttp://example.com/.svn/
dumpall-uhttp://example.com/.DS_Store
dumpall-uhttp://example.com/帮助$dumpall--help
Usage:dumpall[OPTIONS]信息泄漏利用工具，适用于.git/.svn/.DS_Store，以及index页面Example:dumpall-uhttp://example.com/.git

Options:--versionShowtheversionandexit.-u,--urlTEXT指定目标URL，支持.git/.svn/.DS_Store，以及类index页面-o,--outdirTEXT指定下载目录，默认目录名为主机名-f,--force强制下载（可能会有蜜罐风险）--helpShowthismessageandexit..git源代码泄漏利用.svn源代码泄漏利用.DS_Store信息泄漏利用📜 History2022-03-01 v0.3.2修复URL编码问题2021-08-09 v0.3.1修复任意位置存储漏洞、增加蜜罐警告2020-05-22 v0.3.0完成目录列出信息泄漏利用功能2019-10-27 v0.2.0优化下载方法完成.DS_Store信息泄漏利用功能2019-10-24 v0.1.0项目架构优化完成.svn源代码泄漏利用功能2019-10-23完成.git源代码泄漏利用功能2019-10-19 项目启动🤝 Credit本项目参考或使用了以下项目，在此感谢相关开发者https://github.com/lijiejie/GitHackhttps://github.com/admintony/svnExploithttps://github.com/sbp/ginhttps://github.com/gehaxelt/Python-dsstorehttps://github.com/aio-libs/aiohttphttps://github.com/jreese/aiomultiprocesshttps://github.com/pallets/click📄 LicenseMIT License