Saravanan666/pypi-org huggingface dataset:
Package: invoke-plugin-for-sphinx
Invoke Plugin for SphinxThis is a plugin which allows the documentation of invoke tasks with sphinxautodoc.
An invoke task looks like a normal function but the@taskdecorator creates aTaskobject behind the scenes.
Documenting these withautodoccan lead to errors or unexpected results.Installationpip install invoke-plugin-for-sphinx, that's it.UsageAdd the plugin to the extensions list:extensions=["invoke_plugin_for_sphinx"]Then you can use.. automodule::as usual.
Behind the scenes, the function documenter ofautodocis extended to also handle tasks equal to functions.
Therefore the same configurations, limitations and features apply.Build and PublishThis project usessetuptoolsas the dependency management and build tool.
To publish a new release, follow these steps:Update the version in thepyproject.tomlAdd an entry in the changelogPush a new tag likevX.X.Xto trigger the releaseSupport, Feedback, ContributingThis project is open to feature requests/suggestions, bug reports etc. viaGitHub issues. Contribution and feedback are encouraged and always welcome. For more information about how to contribute, the project structure, as well as additional contribution information, see ourContribution Guidelines.Code of ConductWe as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone. By participating in this project, you agree to abide by itsCode of Conductat all times.LicensingCopyright 2023 SAP SE or an SAP affiliate company and invoke-plugin-for-sphinx contributors. Please see ourLICENSEfor copyright and license information. Detailed information including third-party components and their licensing/copyright information is availablevia the REUSE tool.
Package: invoke-release
This provides some stock tasks built atop Invoke (http://www.pyinvoke.org/) to make releasing
your libraries and services, using Git tags, easier and more streamlined. Simply configure your project with atask.pyfile per the instructions athttps://github.com/eventbrite/invoke-release, and you can then perform robust
releases like so:$invokeversion$invokerelease$invokewheel$invokebranch$invokerollback-release
Package: invoker-terminal
Python Project TemplateA low dependency and really simple to start project template for Python Projects.See alsoFlask-Project-Templatefor a full feature Flask project including database, API, admin interface, etc.FastAPI-Project-TemplateThe base to start an openapi project featuring: SQLModel, Typer, FastAPI, JWT Token Auth, Interactive Shell, Management Commands.HOW TO USE THIS TEMPLATEDO NOT FORKthis is meant to be used fromUse this templatefeature.Click onUse this templateGive a name to your project(e.g.my_awesome_projectrecommendation is to use all lowercase and underscores separation for repo names.)Wait until the first run of CI finishes(Github Actions will process the template and commit to your new repo)If you wantcodecovReports and Automatic Release toPyPIOn the new repositorysettings->secretsadd yourPYPI_API_TOKENandCODECOV_TOKEN(get the tokens on respective websites)Read the fileCONTRIBUTING.mdThen clone your new project and happy coding!NOTE:WAITuntil first CI run on github actions before cloning your new project.What is included on this template?🖼️ Templates for starting multiple application types:Basic low dependencyPython program (default)use this templateFlaskwith database, admin interface, restapi and authenticationuse this template.or Runmake initafter cloning to generate a new project based on a template.📦 A basicsetup.pyfile to provide installation, packaging and distribution for your project.Template uses setuptools because it's the de-facto standard for Python packages, you can runmake switch-to-poetrylater if you want.🤖 AMakefilewith the most useful commands to install, test, lint, format and release your project.📃 Documentation structure usingmkdocs💬 Auto generation of change log usinggitchangelogto keep a HISTORY.md file automatically based on your commit history on every release.🐋 A simpleContainerfileto build a container image for your project.Containerfileis a more open standard for building container images than Dockerfile, you can use buildah or docker with this file.🧪 Testing structure usingpytest✅ Code linting usingflake8📊 Code coverage reports usingcodecov🛳️ Automatic release toPyPIusingtwineand github actions.🎯 Entry points to execute your program usingpython -m <invoker_terminal>or$ invoker_terminalwith basic CLI argument parsing.🔄 Continuous integration usingGithub Actionswith jobs to lint, test and release your project on Linux, Mac and Windows environments.Curious about architectural decisions on this template? readABOUT_THIS_TEMPLATE.mdIf you want to contribute to this template please open anissueor fork and send a PULL REQUEST.❤️ Sponsor this projectinvoker_terminalAwesome invoker_terminal created by m00dyInstall it from PyPIpipinstallinvoker_terminalUsagefrominvoker_terminalimportBaseClassfrominvoker_terminalimportbase_functionBaseClass().base_method()base_function()$python-minvoker_terminal#or$invoker_terminalDevelopmentRead theCONTRIBUTING.mdfile.
Package: invokify
Welcome to InvokifyInvokify is a lightweight library that allows you to run functions from a string or list of values.InstallingInvokify is published on PyPI so you can easily install it with eitherpiporpoetry.pipinstallinvokifypoetryaddinvokifyThis package is for python 3.10+Getting startedLet's create a simple command-line command.We can start by importing theInvokeEnginefrom invokify:frominvokifyimportInvokeEngineNow we need to create an engine to hold our commands:engine=InvokeEngine()Once we have our engine set up, all we need to do is decorate our target function using the engine'scommandmethod:@engine.commanddefgreet(user:str):print(f"Hello{user}!")Congrats, that's all you need to do to create a command! Our next goal will be to parse our command by feeding our engine'sparsemethod a list of values.cmd,args,callstack=engine.parse(["greet","Jeff"])Theparsemethod will return a tuple containing three values:The command if one was found. If no command was found, it will returnNoneAny remaining arguments that were passed. Theparsemethod will recursively look for subcommands until it cannot find any, and the remaining arguments will be passed back.The callstack. A list of the commands and subcommands that were passed in.Once you have your command, you can execute it easily just by calling it. If your command accept arguments, you can passargsto it.cmd(*args)Output:Hello Jeff!To actually run commands on the command line, we can just set up a simple input loop.
Invokify has a simple built-in parser calledstring_to_args()that can turn a string into a list of arguments, keeping things like strings and lists intact, and converting numbers automatically.whileTrue:user=input()cleaned=string_to_args(user)cmd,args,cs=engine.parse(cleaned)ifcmd:cmd(*args)else:print("No command was found.")
Package: involution-pytorch
involution_pytorchUnofficial PyTorch implementation of "Involution: Inverting the Inherence of Convolution for Visual Recognition" by Li et al. presented at CVPR 2021.[abs,pdf,Yannic's Video]InstallationYou can installinvolution_pytorchviapip:pipinstallinvolution_pytorchUsageYou can use theInv2dlayer as you would with any PyTorch layer:importtorchfrominvolution_pytorchimportInv2dinv=Inv2d(channels=16,kernel_size=3,stride=1)x=torch.rand(1,16,32,32)y=inv(x)# [1, 16, 32, 32]The paper talks about using Self-Attention for the dynamic kernel generation function. I'll try implementing it later if time permits.ContributingIf I've made any errors anywhere in the implementation, please do let me know by raising an issue. If there's any cool addition you want to introduce, all PRs appreciated!LicenseMIT
Package: invopt
InvOpt: Inverse Optimization with PythonInvOpt is an open-source Python package for solving Inverse Optimization (IO) problems. In IO problems, our goal is to model the behavior of an expert agent, which given an exogenous signal, returns a response action. The underlying assumption of IO is that to compute its response, the expert agent solves an optimization problem parametric in an exogenous signal. We assume to know the constraints imposed on the expert, but not its cost function. Therefore, using examples of exogenous signals and corresponding expert response actions, our goal is to model the cost function being optimized by the expert. More concretely, given a dataset $\mathcal{D} = \{(\hat{s}_ i, \hat{x}_ i)\}_ {i=1}^N$ of exogenous signals $\hat{s}_ i$ and the respective expert's response $\hat{x}_ i$, feature mapping $\phi$, our goal is to find a cost vector $\theta \in \mathbb{R}^p$ such that a minimizer $x_ i$ of theForward Optimization Problem (FOP)$$
x_i \in \arg\min_ {x \in \mathbb{X}(\hat{s}_ i)} \ \langle \theta,\phi(\hat{s}_ i,x) \rangle
$$reproduces (or in some sense approximates) the expert's action $\hat{x}_ i$. For a more detailed description of IO problems and their modeling, please refer toZattoni Scroccaro et al. (2023)and the references therein.InstallationpipinstallinvoptInvOpt depends onnumpy. Moreover, some of its functions also depend ongurobipyorcvxpy. You can get a free academic license for Gurobihere.Usage and examplesThe following functions are available in the InvOpt package:discrete_consistent: for FOPs with discrete decision spaces (e.g., binary), and when the dataset is consistent with some cost vector. Can be used to check if the data is consistent.discrete: for FOPs with dicrete decision spaces (e.g., binary).continuous_linear: for continuous, linear FOPs.continuous_quadratic: for continuous, quadratic FOPs.mixed_integer_linear: for FOPs with mixed-integer decision spaces and cost functions linear w.r.t. the continuous part of the decision variable.mixed_integer_quadratic: for FOPs with mixed-integer decision spaces and cost functions quadratic w.r.t. the continuous part of the decision variable.FOM: for general FOPs. Solves IO problem approximately using first-order methods.ContributingContributions, pull requests and suggestions are very much welcome. TheTODOfile contains some ideas to possibly improve the InvOpt package.CitingIf you use InvOpt for research, please cite our accompanying paper:@article{zattoniscroccaro2023learning,title={Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms},author={Zattoni Scroccaro, Pedro and Atasoy, Bilge and Mohajerin Esfahani, Peyman},journal={https://arxiv.org/abs/2305.07730},year={2023}}
Package: invQ
invQinvQ is a Python package to calculate the angles of quaternions with respect to a reference quaternion considering the point group symmetry of the rigid body.QuaternionYou may look atWikipediafor details.ContributorSumitava Kundu,IACS, Kolkata.InstallationPrerequisitespython3 or higherrowanInstallation viapippipinstallpltRDFUsing source codegitclonehttps://github.com/sumitavakundu007/invQ.git
tar-xvfinvQ-X.X.XcdinvQ-X.X.X
python3setup.pyinstall--userUsagefrominvQimportinvQuatinvQuat("orientations.json","equivQ.json")You need to prepare 'orientations.json' and 'equivQ.json'Sample format for 'orientations.json'{"refQ":[1,0,0,0],# The refernce quaternion which you want to use to calculate the angles"Orientations":[[1,0,0,0]]# The quaternions of the rigid body}Sample format for 'equivQ.json'{"Equivalent_orientations":[[1,0,0,0]]# Equivalent quaternions from the point group symmetry of the rigid body.}ContributingPull 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.License[MIT](https://choosealicense.com/licenses/mit/
Package: invrcptexporter
a tool to export invoice and receipt from libreoffice calc into pdf filevirtualenv has to be created with option--system-site-packagesas
it needs to use system site-packages which has uno module installed by$ sudoapt-getinstallpython3-uno, on Ubuntu. (Unfortunately, uno
module is not available from pip.)The code is based on Christopher5106’s tutorial (many thanks to him):('http://christopher5106.github.io/office/2015/12/06/openoffice-','libreoffice-automate-your-office-tasks-with-python-macros.html')How it workswe open LibreOffice with socket openwe use Python to send commands to LibreOffice through the socket (you
should use iptables to block incoming connection on the specified TCP
port for security reason)To open LibreOffice with socket open, use the command line below:libreoffice \
  --accept="socket,host=localhost,port=2002;urp;StarOffice.ServiceManager"INSTALL for TESTINGOn Ubuntu 14.04install python3-uno as a system-wide package as this package is not
available via pip:$ sudoapt-getinstallpython3-unocreate virtualenv (probably, with virtualenvwrapper) with--system-site-pacakges:$ mkvirtualenv--system-site-packagesinvrcptexporterinstall required packages from pip:$ pip install-rrequirements-test.txtinstall poppler-utils from apt as we have to use pdftotext in our
tests:$ sudoapt-getinstallpoppler-utilsI have tried a few other alternatives such as extractText() from
PyPDF2, it doesn’t work for many casesconfigure~/.invrcptexporterrc:$ cd ~   $ cp /your/project/path/source/invrcptexporterrc .invrcptexporterrcadjust settings in this rc file, the most important one is
SCRIPT_PATH which is the absolute path to ‘source’ directoryrun the test:$ /your/project/path/source/test.shnotes: test.sh is a hack (you can see details from comments inside
test.sh ), each time you run it, you have to manually close (exit or
ctrl+q) the opened libreoffice ui so that the test will continue to
runINSTALL for USERinstall python3-uno as a system-wide package as this package is not
available via pip:$ sudoapt-getinstallpython3-unoinstall invrcptexporter from pip:$ pip3 install invrcptexporteruse /sample_invoice_list.ods as the templatethe program is currently shipped as a library only, please see sample
codes for how to use it from:/source/sample-code/invrcptexporter/
Package: invrs-opt
invrs-opt - Optimization algorithms for inverse designv0.4.0OverviewTheinvrs-optpackage defines an optimizer API intended for topology optimization, inverse design, or AI-guided design. It (currently) implements the L-BFGS-B optimization algorithm along with some variants. The API is intended to be general so that new algorithms can be accommodated, and is inspired by the functional optimizer approach used in jax. Example usage is as follows:initial_params=...optimizer=invrs_opt.lbfgsb()state=optimizer.init(initial_params)for_inrange(steps):params=optimizer.params(state)value,grad=jax.value_and_grad(loss_fn)(params)state=optimizer.update(grad=grad,value=value,params=params,state=state)Optimizers ininvrs-optare compatible with custom types defined in thetotypespackage. The basiclbfgsboptimizer enforces bounds for custom types, while thedensity_lbfgsboptimizer implements a filter-and-threshold operation forDensityArray2Dtypes to ensure that solutions have the correct length scale.Installpip install invrs_opt
Package: invrs-utils
invrs-utils - Miscellaneous utilitiesv0.4.0This package is a collection of utilities that may be useful, but do not have fundamental roles in the invrs-io ecosystem. These currently include,experiment.checkpoint: Defines a simple checkpoint manager with anorbax-like API.experiment.sweep: Functions to construct experiments involving hyperparameter sweeps.experiment.data: Functions to load and summarize data from experiments.Installpip install invrs-utils
Package: invsearch
Welcome toinvsearchDocumentationA fast document search engine allow search by field and value pair.Example:>>>frominvsearchimportInvIndex>>>ii=InvIndex([...{"id":1,"name":"Alice","friends":[2,3]},...{"id":2,"name":"Bob","age":15,"friends":[1,3]},...{"id":3,"name":"Cathy","age":None,"friends":[1,2]},...{"id":4,"name":"Bob","age":None},...])>>>ii.find_one(id=1){"id":1,"name":"Alice","friends":[2,3]}>>>ii.find(name="Bob")[{"id":2,"name":"Bob","age":15,"friends":[1,3]},{"id":4,"name":"Bob","age":None}]>>>ii.by_id(id=1){"id":1,"name":"Alice","friends":[2,3]}Installinvsearchis released on PyPI, so all you need is:$pipinstallinvsearchTo upgrade to latest version:$pipinstall--upgradeinvsearch
Package: inworld-python
inworld_pythonUnder construction! Not ready for production use. Currently experimenting and planning!Developed by Edgelord and ChatGPT (c) 2023Examples of How To Use (Buggy Alpha Version)Installationpipinstallinworld-pythonNote: If you don't have node js installed also do the followingpipinstall'nodejs-bin[cmd]'Usage for dummies even.frominworld_pythonimportinworld_chatchat_app=inworld_chat.InWorldChat('inworld_key','inworld_secret','inworld_scene')# installs the inworld command in the current directory as iw.jschat_app.setup()# enter query, user_name, channel_id, and user_idout=chat_app.chat("What did I just ask?","blowhard","3232323","23232323")print(out)
Package: inwx-domrobot
INWX Domrobot Python 3 ClientYou can access all functions of our frontend via our API, which is available via the XML-RPC or JSON-RPC protocol and thus can be easily consumed with all programming languages.There is also an OT&E test system, which you can access viaote.inwx.com. Here you will find the known web interface which is using a test database. On the OT&E system no actions will be charged. So you can test as much as you like there.DocumentationYou can view a detailed description of the API functions in our documentation. You can find the online documentationby clicking here.If you still experience any kind of problems don't hesitate to contact oursupport via email.InstallationThe recommended way is via pip:pipinstallinwx-domrobotYou can find more information about the package onpypi.org.ExamplefromINWX.DomrobotimportApiClientusername=''password=''domain='my-test-domain-which-is-definitely-not-registered6737.com'api_client=ApiClient(api_url=ApiClient.API_OTE_URL,debug_mode=True)login_result=api_client.login(username,password)iflogin_result['code']==1000:domain_check_result=api_client.call_api(api_method='domain.check',method_params={'domain':domain})ifdomain_check_result['code']==1000:checked_domain=domain_check_result['resData']['domain'][0]ifchecked_domain['avail']:print(domain+' is still available!')else:print('Unfortunately, '+domain+' is already registered.')else:raiseException('Api error while checking domain status. Code: '+str(domain_check_result['code'])+'  Message: '+domain_check_result['msg'])api_client.logout()else:raiseException('Api login error. Code: '+str(login_result['code'])+'  Message: '+login_result['msg'])You can also have a look at theexample folderin the project for even more info.LicenseMIT
Package: inxs
inxs – A Python framework for XML transformations without boilerplate.inxs is inexcessive.inxs is not XSLT.inxs is ISC-licensed.inxs is fully documented here:https://inxs.readthedocs.io/en/latest/At a glimpseSolving theWikipedia XSLT example #1:defextract_person(node:TagNode):returnnode.attributes['username'],first(node.css_select("name")).full_textdefappend_person(previous_result,result:TagNode):result.append_child(result.new_tag_node("name",attributes={"username":previous_result[0]},children=[previous_result[1]]))transformation=Transformation(Rule('person',(extract_person,append_person)),result_object='context.result',context={'result':new_tag_node('root')})# that's four lines less LOC than the XSLT implementationSolving theWikipedia XSLT example #2:defgenerate_skeleton(context):context.html=new_tag_node("html",namespace='http://www.w3.org/1999/xhtml',children=(tag("head",tag("title","Testing XML Example")),tag("body",(tag("h1","Persons"),tag("ul"))),))defextract_person(node:TagNode,persons):persons.append((first(node.css_select("name")).full_text,first(node.css_select("family-name")).full_text))deflist_persons(previous_result,html:TagNode):first(html.css_select("html|body html|ul")).append_child(*(html.new_tag_node("li",children=[f'{x[1]},{x[0]}'])forxinprevious_result))transformation=Transformation(generate_skeleton,Rule('person',extract_person),lib.sort('persons',itemgetter(1)),list_persons,result_object='context.html',context={'persons':[]})# that's four lines more LOC than the XSLT implementationHereyou can find the source repository and issue tracker of inxs.History0.2b1 (2019-06-23)refactored to base ondelbinstead of lxmlremovedfrom the available symbols for handler functions:treexpath_evaluator(useroot.xpathinstead)renamedavailable symbols for handler functions:element->noderenamedin core:SkipToNextElement->SkipToNextNoderemovedfrom the lib:drop_siblingsextract_texthas_tailinit_elementmakermergereplace_textsubrenamedin the lib:make_element->make_noderemove_element->remove_noderemove_elements->remove_nodessorter->sortstrip_attributes->remove_attributesstrip_namespace->remove_namespaceVarious arguments to functions and methods have been renamed accordingly.0.1b1 (2017-06-25)new: Allows the definition that any rule must match per transformation ascommon_rule_conditions.Minor improvements and fixes.0.1b0 (2017-06-19)First beta release.0.1a0 (2017-05-02)First release on PyPI.
Package: inyoka-theme-default
On development systems:Rungit clonegit@github.com:inyokaproject/theme-default.gitnext to
the cloned Inyoka repository. (Basically, it doesn’t matter were you clone
the theme repository, but for support reasons it might be better to use the
same base folder like for Inyoka). After cloning the file-structure should
look like:$ tree -L 1
.
├── inyoka
├── theme-default
└── maybe another-themeSwitch into the repository:cdtheme-defaultActivate sourcesource~/.venvs/inyoka/bin/activateInstall as a development package:python setup.py developRunnpm installto installGruntRun./node_modules/grunt-cli/bin/gruntwatchto build all static files
and watch for file changes on the CSS / JS filesOn ProductionRunpip install-U"git+ssh://git@github.com:inyokaproject/theme-default.git@staging#egg=inyoka-theme-default"DeploymentRunnpm installto installGruntRun./node_modules/grunt-cli/bin/gruntto build all static filesRunmanage.py collectstaticin your Django projectLet Django know about the themeAdd'inyoka_theme_default'to theINSTALLED_APPSininyoka/development_settings.py:INSTALLED_APPS = INSTALLED_APPS + (
    'inyoka_theme_default',
)
Package: inyoka-theme-ubuntuusers
On development systems:Rungit clonegit@github.com:inyokaproject/theme-ubuntuusers.gitnext to
the cloned Inyoka repository. (Basically, it doesn’t matter were you clone
the theme repository, but for support reasons it might be better to use the
same base folder like for Inyoka). After cloning the file-structure should
look like:$ tree -L 1
.
├── inyoka
├── theme-ubuntuusers
└── maybe another-themeSwitch into the repository:cdtheme-ubuntusersActivate sourcesource~/.venvs/inyoka/bin/activateInstall as a development package:python setup.py developRunnpm installto installGruntRun./node_modules/grunt-cli/bin/gruntwatchto build all static files
and watch for file changes on the CSS / JS filesOn ProductionRunpip install-U"git+ssh://git@github.com:inyokaproject/theme-ubuntuusers.git@staging#egg=inyoka-theme-ubuntuusers"DeploymentRunnpm installto installGruntRun./node_modules/grunt-cli/bin/gruntto build all static filesRunmanage.py collectstaticin your Django projectLet Django know about the themeAdd'inyoka_theme_ubuntuusers'to theINSTALLED_APPSininyoka/development_settings.py:INSTALLED_APPS = INSTALLED_APPS + (
    'inyoka_theme_ubuntuusers',
)