Saravanan666/pypi-org huggingface dataset:
Package: shiva-piplaad-001
Shiva-piplaadShiva-Piplaadis an AI module created byTheAIHub.appwhich aims to automate mundane tasks such as data entry, document organization, and general administrative duties to reduce the amount of time human personnel dedicate to them. It utilizes a library of artificial intelligence technologies and algorithms, including natural language processing (NLP) and computer vision (CV) technologies, and machine learning (ML) algorithms. It will also be integrated with web services such as e-commerce and customer relationship management (CRM) platforms to facilitate customer-facing services.Shiva-Piplaad's main goalis to create a fully-automated digital workplace, allowing human personnel to focus on more complex tasks, while also being an open-source AI platform for developers to customize and extend as needed.OverviewShiva-Piplaadis an experimental AI module created byTheAIHub.appthat will utilize its baseShiva moduleto complete various automated tasks. The focus of Shiva-Piplaad is to automate mundane processes such as data entry, document organization, and general administrative tasks to reduce the amount of time human personnel dedicate to them.Shiva-Piplaad works by utilizing the Shiva module's extensive library ofartificial intelligence technologies and algorithmsto identify what processes need to be automated. This process includesdata extraction using natural language processing (NLP) and computer vision (CV) technologies. Shiva-Piplaad will then utilizemachine learning (ML) algorithmsto create a custom plan for each automated task. This plan will be tailored to fit the requirements of the organization or individual, promoting accuracy and efficiency.Shiva-Piplaad will also feature integration with other web services such ase-commerce and customer relationship management (CRM) platforms. This will allow humans to access data stored in those platforms and utilize Shiva-Piplaad to automate tasks such as customer communication, e-mail campaigns, data collection, and other customer-facing services.The main objective of Shiva-Piplaad is to facilitate afully-automated digital workplacethat can be used to streamline mundane and highly-repetitive tasks. This will allow human personnel to focus on more complex tasks, boosting creativity and collaboration in the workplace. Finally,Shiva-Piplaad will be an open-source AI platform, allowing developers to customize and extend its automation capabilities as needed.Functionscreate.text--```

def create.text(self, data, word_count):

```create.text_command--```

def create.text_command(self, command, data, word_count):

```InstallationTo install, run the following command in your terminal:pip install <Package Name>QuickstartDear github viewer,Thank you for taking the time to view the code of theaihub.app. We really appreciate your interest and contributions to our projects. Your feedback and support is invaluable to us as we strive to provide better and more useful services for our users.Sincerely,Thetheaihub.appTeam
Package: shiva-piplaad-002
Shiva-piplaadShiva-Piplaadis an AI module created bytheaihub.appwhich aims to automate mundane tasks such as data entry, document organization, and general administrative duties to reduce the amount of time human personnel dedicate to them. It utilizes a library of artificial intelligence technologies and algorithms, including natural language processing (NLP) and computer vision (CV) technologies, and machine learning (ML) algorithms. It will also be integrated with web services such as e-commerce and customer relationship management (CRM) platforms to facilitate customer-facing services.Shiva-Piplaad's main goalis to create a fully-automated digital workplace, allowing human personnel to focus on more complex tasks, while also being an open-source AI platform for developers to customize and extend as needed.OverviewShiva-Piplaadis an experimental AI module created bytheaihub.appthat will utilize its baseShiva moduleto complete various automated tasks. The focus of Shiva-Piplaad is to automate mundane processes such as data entry, document organization, and general administrative tasks to reduce the amount of time human personnel dedicate to them.Shiva-Piplaad works by utilizing the Shiva module's extensive library ofartificial intelligence technologies and algorithmsto identify what processes need to be automated. This process includesdata extraction using natural language processing (NLP) and computer vision (CV) technologies. Shiva-Piplaad will then utilizemachine learning (ML) algorithmsto create a custom plan for each automated task. This plan will be tailored to fit the requirements of the organization or individual, promoting accuracy and efficiency.Shiva-Piplaad will also feature integration with other web services such ase-commerce and customer relationship management (CRM) platforms. This will allow humans to access data stored in those platforms and utilize Shiva-Piplaad to automate tasks such as customer communication, e-mail campaigns, data collection, and other customer-facing services.The main objective of Shiva-Piplaad is to facilitate afully-automated digital workplacethat can be used to streamline mundane and highly-repetitive tasks. This will allow human personnel to focus on more complex tasks, boosting creativity and collaboration in the workplace. Finally,Shiva-Piplaad will be an open-source AI platform, allowing developers to customize and extend its automation capabilities as needed.Functionscreate.text--```

def create.text(self, data, word_count):

```create.text_command--```

def create.text_command(self, command, data, word_count):

```InstallationTo install, run the following command in your terminal:pip install <Package Name>QuickstartDear github viewer,Thank you for taking the time to view the code of theaihub.app. We really appreciate your interest and contributions to our projects. Your feedback and support is invaluable to us as we strive to provide better and more useful services for our users.Sincerely,Thetheaihub.appTeam
Package: shiver_me_tinders
A lightweight wrapper around the Tinder undocumented API.RequirementsPython 2.6+ (2.7 or 3.4 recommended)PIP (for some installation methods)GIT (for some installation methods)InstallationIf you are on Mac OS X or Linux, chances are that one of the following two commands will work for you:InstallationUsing PIP via PyPIpipinstallshiver_me_tindersUsing PIP via Githubpipinstallgit+git://github.com/NFicano/shiver-me-tinders.git#egg=shiver_me_tindersAdding to yourrequirements.txtfile (runpip install-rrequirements.txtafterwards)git+ssh://github.com/NFicano/shiver-me-tinders.git#egg=shiver_me_tindersManually via GITgitclonegit://github.com/NFicano/shiver-me-tinders.gitshiver-me-tinderscdshiver-me-tinderspythonsetup.pyinstallUsage Examplefromshiver_me_tinderimportTindertinder=Tinder(fb_token='xxx',fb_id=1001,lat=40.0,lon=-70.0)# Or initialize it with a yaml file (see included config.yaml.sample).tinder=Tinder.config_from_file('/path/to/file/config.yaml')matches=Tinder.get_matches()formatchinmatches:# Show users' name + photos.printmatch.name,match.photos# Now tip your hat at them...tinder.like(match)
Package: shiv-userdirs
shivPython SHIV-version, which creates user owned directires if SHIV_ROOT is used.shiv is a command line utility for building fully self-contained Python zipapps as outlined inPEP 441, but with all their dependencies included!shiv's primary goal is making distributing Python applications fast & easy.📗 Full documentation can be foundhere.sys requirementspython3.6+linux/osx/windowsquickstartshiv has a few command line options of its own and accepts almost all options passable topip install.simple cli exampleCreating an executable of flake8 with shiv:$shiv-cflake8-o~/bin/flake8flake8
$~/bin/flake8--version3.7.8(mccabe:0.6.1,pycodestyle:2.5.0,pyflakes:2.1.1)CPython3.7.4onDarwin-c flake8specifies the console script that should be invoked when the executable runs,-o ~/bin/flake8specifies the location of the generated executable file andflake8is the dependency that should be installed from PyPI.Creating an interactive executable with the boto library:$shiv-oboto.pyzboto
Collectingboto
Installingcollectedpackages:boto
Successfullyinstalledboto-2.49.0
$./boto.pyz
Python3.7.4(v3.7.4:e09359112e,Jul82019,14:54:52)[Clang6.0(clang-600.0.57)]ondarwin
Type"help","copyright","credits"or"license"formoreinformation.(InteractiveConsole)>>>importboto
>>>boto.__version__'2.49.0'installingYou can install shiv by simply downloading a release fromhttps://github.com/linkedin/shiv/releasesor viapip/pypi:pipinstallshivYou can even create a pyzofshivusingshiv!python3-mvenv.sourcebin/activate
pipinstallshiv
shiv-cshiv-oshivshivdevelopingWe'd love contributions! Getting bootstrapped to develop is easy:gitclonegit@github.com:linkedin/shiv.gitcdshiv
python3-mvenvvenvsource./venv/bin/activate
python3-mpipinstall--upgradebuild
python3-mbuild
python3-mpipinstall-e.Don't forget to run and write tests:python3-mpipinstalltox
toxTo build documentation when you changed something indocs:python3-mpipinstall-rdocs/requirements.txt
sphinx-builddocsbuild/htmlgotchasZipapps created with shiv are not guaranteed to be cross-compatible with other architectures. For example, apyzfile built on a Mac may only work on other Macs, likewise for RHEL, etc. This usually only applies to zipapps that have C extensions in their dependencies. If all your dependencies are pure python, then chances are thepyzwillwork on other platforms. Just something to be aware of.Zipapps created with shivwillextract themselves into~/.shiv, unless overridden viaSHIV_ROOT. If you create many utilities with shiv, you may want to occasionally clean this
directory.acknowledgementsSimilar projects:PEXpyzzersuperzippyLogo by Juliette Carvalho
Package: sh-junit-xml
sh_junit_xmlThis a simple wrapper around the junit_xml python library to allow for
generating junit xml files from a shell. The main use case for this is
generating test results for jenkins from shell scripts.InstallationInstall via pip:pip install sh_junit_xmlInstall from source:./setup.py installUsageThe script wraps all arguments passed tojunit_xml.TestCase.__init__()so
that you can construct any arbitrary test case that is available through this
class. The each argument is simply prepended with--and remains otherwise
identical. Most arguments are treated as strings but a few special cases are
converted after they are read in from the command line, such as--elapsed_secwhich is converted to a number since it is expected in that format.
Additionally arguments that start with@will be treated as filename, which
are opened and the contents are passed as the argument.In addition to the automatic arguments generated from the TestCase constructor
the following arguments are available:--suite: Specify the suite name--failure: Mark the test as failed and pass the failure message.--error: Mark the test as an error and pass the error message.--skipped: Mark the test as skipped and pass the skipped message
Package: shlack
shlackshlack is a command line tool which sends slack messages. It is a lot likejarjarbut witha few (important?) differences.QuickstartFirst,set up a slack appfor shlack and have the oauth API token handy.Then install shlack to your python 2.7, 3.5, 3.6, or 3.7 environment:pipinstallshlackOrpipinstallgit+https://github.com/nolanbconaway/shlack.gitExport your slack oauth token:$exportSLACK_OAUTH_API_TOKEN='...'Now send yourself a message! shlack will read your token from the variable you exported.$shlackmessage'Hello!'--channel'@nolan'In your slack workspace:Add an attachment:$shlackmessage'Hello!'-c'@nolan'--attach'Date'"$(date)"shlack can also send you a notification after a long-running task has completed.$shlacktask'sleep 10 && echo done'--channel'@nolan'Running that will spin up a process detached from your terminal session, so you can  grab some lunch :-).Need help? Just ask:$shlacktask--helpSetting up a Slack AppStart by heading over to theapps pageand hit the "create new app" button.Name your app whatever you want (I called mine,shlack) and assign it to your workspace of choice if you have multiple workspaces. Then hit "create app".Slack should take you to an app management page. Hit the "Permissions" button (or the "OAuth & Permissions" tab on the sidebar).Scroll down to the "Scopes" section. Add the following two scopes:chat:write:bot. Shlack needs this to post messages.files:write:user. Shlack needs this to upload files when e.g., your task output is very long.Hit the "Save Changes" button once those two scopes are selected. Then scroll to the top of the page and hit the "Install App to Workspace" button. Slack will ask you to allow the app to access the scopes you set up.The page will refresh and at the top you'll find a new OAuth Access Token. Copy that and put it somewhere for later.Now you can style the app as you see fit :). Hit the "Basic Information" tab at the top of the sidebar and scroll down to the "Display Information" section. Style to your liking. I use this photo which I found by searching "dog with cowboy hat" on the internet.Then you're done! Enjoy your shlack app.TodoWrite some tooling to help users through errors when their display names are not the same as their usernames. Maybe a command to search users. Maybe just some documentation.Surface child PID to user inshlack task.get URLs in attachments to unfurl.migrate to blockshttps://api.slack.com/messaging/attachments-to-blockspypi
Package: shlax
Why?In Python we now have async subprocesses which allows to execute several
subprocesses at the same time. The purpose of this library is to:provide an acceptable asyncio subprocess wrapper for my syntaxic taste,can stream stderr and stdout in real time while capturing it,real time output must be prefixed for when you execute several commands at
the time so that you know which line is for which process, like with
docker-compose logs,output coloration in real time with regexps to make even more readable.This code was copy/pasted between projects and finally extracted on its own.DemoYou will find the demo script in demo.py in this repository.UsageBasicsBasic example, this will both stream output and capture it:fromshlaximportSubprocessproc=awaitSubprocess('echo hi').wait()print(proc.rc,proc.out,proc.err,proc.out_raw,proc.err_raw)LongerIf you want to start the command and wait for completion elsewhere then call
any ofstart()andwait(), or both, explicitely:proc=Subprocess('echo hi')awaitproc.start()# start the processawaitproc.wait()# wait for completionProc aliasNote that shlax defines an aliasProctoSubprocessso this also works:fromshlaximportProcproc=awaitProc('echo hi').wait()QuietTo disable real time output streaming use thequietargument:proc=awaitSubprocess('echo hi',quiet=True).wait()PrefixUsing prefixes, you can have real time outputs of parallel commands and at the
same time know which output belongs to which process:proc0=Subprocess('find /',prefix='first')proc1=Subprocess('find /',prefix='second')awaitasyncio.gather(proc0.wait(),proc1.wait())Coloration and output patchingYou can add coloration or patch real time output with regexps, note that it
will be applied line by line:importsysregexps={'^(.*).py$':'{cyan}\\1',}awaitasyncio.gather(*[Subprocess(f'find{path}',regexps=regexps,).wait()forpathinsys.path])Where is the rest?Shlax used to be the name of a much more ambitious poc-project, that you can
still find in theOLDbranch of this repository. It has been extracted in
two projects with clear boundaries, namelysysplanandpodplanwhich are still in alpha state, although
Shlax as it is feature complete and stable.
Package: shldn
shldnSheldon Cooper ALWAYS points out what (he thinks) is wrong with people and their workWhat is it?shldnis a console-application that finds all the division operations in Python source code to facilitate transitioning Python 2 code to Python 3. It uses Python'sASTmodule to process and parse the code. It, then traverses the abstract syntax tree to find all the division operations, and prints them in a normal or more readable format to the console.MotivationPython 2 won't be supported past 2020tic tac tic tac...Division in Python 3 always returns afloat; whereas division in Python 2 can return anintCritical for large Python 2 code bases that perform division operationsRequirementsPython 3InstallMethod 1: Install fromPypiMethodpip3 install shldnOr, if you usepipenv,pipenv install shldnMethod 2: Clone repositoryMove to the desired directory to installshldngit clone https://github.com/NablaZeroLabs/shldn.gitUsageMethod 1 (Installed fromPypi):In environment installed:Check if installedpip listExecuteshldn [-h] [-hr] [-r] pathMethod 2 (Cloned repo):Executepython3 leonard.py [-h] [-hr] [-r] pathArgumentspositional arguments:
  path                  path to python source file(s)

optional arguments:
  -h, --help            show this help message and exit
  -hr, --human_readable
                        set for friendlier output
  -r, --recursive       recursively check python files in pathLicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details.More ResourcesPorting Python 2 Code to Python 3by Brett Cannon
Package: shleem
Automate and version datasets generation from data sources.importshleem# mystery codeContents1Installation2Setting up shleem2.1Credentials file3Basic Use4Contributing4.1Installing for development4.2Running the tests4.3Adding documentation5Credits1Installationpipinstallshleem2Setting up shleemshleem uses a couple of simple conventions to handle credentials and refer to MongoDB servers:2.1Credentials fileYou must set up a credentials file for shleem to use. Create a.shleem/shleem_credentials.ymlfile in your home folder, populating it with your MongoDB credentials, using an identical structure to the inner structure of theenvsconfiguration parameters:environment_name:server_name:reading:username:reading_usernamepassword:password1writing:username:writing_usernamepassword:password2You can extend this to include any number of environments and servers.3Basic UsePass.4ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.4.1Installing for developmentClone:gitclonegit@github.com:shaypal5/shleem.gitInstall in development mode:cdshleempipinstall-e.4.2Running the testsTo run the tests use:pipinstallpytestpytest-covcoveragecdshleempytest4.3Adding documentationThe project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, followthese conventions.5CreditsCreated by Shay Palachy (shay.palachy@gmail.com).
Package: shlep
shlepshleplets you package your directory's content in a format that can be easily passed to ChatGPT. This is especially
useful when using the code interpreter plugin!It automatically excludes files that match what's in your.gitignorefile, as well as any additional exclude patterns you provide.InstallationYou can installshlepfrom PyPI, but the recommended installation for the CLI is throughpipx.pipxinstallshlepFeaturesSupports various output formats: JSON, CSV, and plain textExcludes files and directories matching patterns in your.gitignorefileAllows you to provide additional exclude patternsCan write output to a file or print it to stdoutDemohttps://github.com/knowsuchagency/shlep/assets/11974795/ebfdae55-35f2-4edc-b0cb-089d2f99a7caUsageshlepcan be used as a command-line tool or programmatically in your Python code.Command-line usageusage: shlep [-h] [-o OUTPUT_FILE] [-i INDENT] [-e EXCLUDE] [-f {json,csv,plaintext}] directory

Gather directory contents into a single output.

positional arguments:
  directory             The directory to analyze.

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT_FILE, --output-file OUTPUT_FILE
                        The output file to generate. If not specified, output is printed to stdout.
  -i INDENT, --indent INDENT
                        The number of spaces to use for indentation in the output JSON.
  -e EXCLUDE, --exclude EXCLUDE
                        Additional files or directories to exclude.
  -f {json,csv,plaintext}, --format {json,csv,plaintext}
                        The output format. Can be 'json', 'csv', or 'plaintext'. Default is 'json'.Programmatically in Pythonfromshlepimportshlepdirectory="path/to/your/directory"indent=2additional_excludes=[".env","node_modules/"]output_format="json"output_file="output.json"shlep(directory,indent,additional_excludes,output_format,output_file)LicenseThis project is licensed under the Apache License 2.0. You can find the full license text in theLICENSEfile.
Package: shlink
shlink-pyPython module for API communication withShlinkInstallationTo install simply use pip:pipinstallshlinkUsageThe package is used by creating a new instance of an Shlink object. API calls can then be made using methods on this object.An example of all of the calls available is shown in the below example:importjsonimportsysfromdatetimeimportdatetimefromshlinkimportShlinkfromshlink.dataimportErrorshlink=Shlink(url="https://example.com/",api_key="000000000-0000-0000-0000-000000000000")# LIST_SHORT_URLSresult=shlink.list_short_urls()print(json.dumps(result.to_dict()))ifisinstance(result,Error):sys.exit(1)# ADD_SHORT_URLresult=shlink.add_short_url(long_url="http://example.com/",tags=["example"],valid_since=datetime(2019,3,14),valid_until=datetime(2019,3,20),custom_slug="example",max_visits=100,find_if_exists=False)print(json.dumps(result.to_dict()))ifisinstance(result,Error):sys.exit(2)# GET_SHORT_URLresult=shlink.get_short_url("example")print(json.dumps(result.to_dict()))ifisinstance(result,Error):sys.exit(3)DevelopmentTo run this project in a development environmentpipenvis recommended.E.g.pipenvrunpythonexample.py
Package: shmarray
shmarrayNOTE: If you are using Python 3.8, please turn tomultiprocessing.shared_memoryinstead.shmarray is a shared numpy-array implementation utilizing
multiprocessing.sharedctypes.  It is a quite useful small library created of
David Baddeley. I found it athttps://github.com/lucastheis/parallel/blob/master/shmarray.pyDespite its usefulness, it not only does not come with a pip-installable
package, but also contains disrepaired bugs.This repo is created as a remedy.Installationpip install shmarrayUsagetest.pyfrommultiprocessingimportProcessimporttimeimportshmarraydefworker(data):whileTrue:data+=1time.sleep(1)defmonitor(data):whileTrue:print(data)time.sleep(0.5)data=shmarray.zeros(10)procs=[Process(target=worker,args=(data,)),Process(target=monitor,args=(data,)),]forpinprocs:p.start()forpinprocs:p.join()Result:$python3test.py[1.1.1.1.1.1.1.1.1.1.][1.1.1.1.1.1.1.1.1.1.][2.2.2.2.2.2.2.2.2.2.][2.2.2.2.2.2.2.2.2.2.][3.3.3.3.3.3.3.3.3.3.][3.3.3.3.3.3.3.3.3.3.][4.4.4.4.4.4.4.4.4.4.][4.4.4.4.4.4.4.4.4.4.][5.5.5.5.5.5.5.5.5.5.][5.5.5.5.5.5.5.5.5.5.][6.6.6.6.6.6.6.6.6.6.][6.6.6.6.6.6.6.6.6.6.]...Under the hoodThe code inshmarray.pyis mostly the same as the original, I've only done a few things:fix a bug that did not passdtypeargument to downstream callsfix python version compatibility bugsreformat the code usingblack
Package: shmem4py
OverviewThis package provides Python bindings forOpenSHMEM.
OpenSHMEM is a Partitioned Global Address Space (PGAS) programming model that
provides low-latency, high-bandwidth communication for use in parallel
applications. The OpenSHMEM project aims to standardize several implementations
of the different SHMEM APIs. shmem4py exposes an API which grounds on theOpenSHMEM 1.5 specification;
however, it also supports legacy 1.4 implementations.Supported implementationsCray OpenSHMEMXOpen Source Software Solutions (OSSS) OpenSHMEMOpen MPI OpenSHMEMOSHMPISandia OpenSHMEMDependenciesPython3.7 or newerA workingOpenSHMEMimplementation with its dependenciesPython modules:CFFI,NumPyDocumentationRead the Docs:https://shmem4py.readthedocs.io/AcknowledgmentsThis project was partially supported by the
Extreme Computing Research Center (ECRC),
King Abdullah University of Science and Technology (KAUST).