Saravanan666/pypi-org huggingface dataset:
Package: paikwiki-hello-cli
Hello CLIThis is a Python CLI app designed to facilitate learning how to configure Python CLI applications.세팅하기먼저 Poetry로 프로젝트를 시작한다.poetryaddpaikwiki-hello-clipaikwiki_hello_cli/main.py파일을 생성하고, 아래처럼 입력한다.# main.pydefrun():print("Hello, World!")if__name__=="__main__":run()pyproject.toml에 아래의 설정을 추가한다.[tool.poetry.scripts]hello="paikwiki_hello_cli.main:run"터미널에서 가상환경을 시작한다.$poetryshell
Spawningshellwithin/Users/{USER_NAME}/Library/Caches/pypoetry/virtualenvs/paikwiki-hello-cli-{HASH}-py3.123.12
$emulatebash-c'. /Users/{USER_NAME}/Library/Caches/pypoetry/virtualenvs/paikwiki-hello-cli-{HASH}-py3.123.12/bin/activate'(hello-cli-py3.12)$아래 명령어로paikwiki-hello-cli를 설치한다.poetryinstall실행이 잘 되는지 테스트한다.(paikwiki-hello-cli-py3.12)$hello
Hello,World!DeployPyPi API 토큰을 설정한다.poetryconfigpypi-token.pypi{API_TOKEN}패키지를 빌드한다.$poetrybuild
Buildingpaikwiki-hello-cli(0.0.1)-Buildingsdist-Builtpaikwiki_hello_cli-0.0.1.tar.gz-Buildingwheel-Builtpaikwiki_hello_cli-0.0.1-py3-none-any.whl패키지를 배포한다.$poetrypublish
Package: pail
pail – Python Adaptive Images Librarypailis aWSGImiddleware providingAdaptive Images. Delivering
small images to small devices.It detects your visitor’s screen size and automatically creates, and
delivers device appropriate re-scaled versions of your web page’s
embeded HTML images. No (major) mark-up changes needed. It is intended
for use withResponsive Designsand to be combined withFluid
Imagetechniques.This package is based on the ideas of Matt Wilcox and (more loosely)
on hisPHP scriptfor the same purpose. Matt is in no way to blame for
any shortcomings of this Python port.pailprovides special support for use withPaste.Please note, that this package is still in a very early state and
changes, also to the API, are likely to happen in near future.Comments and patches are welcome. Please send these to uli at gnufix
dot de.InstallationThe package can be installed by:$ pip install pailAfterwards you should be able to use pail in anyWSGIenvironment. See thedocumentationfor details.LinksFulldocumentation(including deployment examples)Fork me on GitHubChanges0.2 (2013-05-17)Add very plain mobile detection. Now, if no device width is given
(via cookie) detected mobile devices get images with the smallest
predefined resolution while non-mobile devices get the images with
the largest predefined resolution.Removed zc.buildout stuff (we do not need it).0.1 (2013-05-07)First implementation based on Matt Wilcox’ PHP script.Download
Package: pailab
pailabpailab is an integrated machine learning workbench to version, analyze and automatize the machine learning model building processes and deployments.
It keeps track of changes in your machine learning pipeline (code, data, parameters) similar to classical
version control systems considering special characteristics of AI model building processes.It provides:Versioning of all objects of the ML development cycleFull transparency over the whole ML development cycleSupport to work in a team on the same AI projects, sharing data, algorithms and resultsConsistency checks across the whole pipelineDistributed execution of parallel jobs, e.g. for parameter studiesStandardized analysis plotsJupyter widgets to administrate and control the ML repoAl objects added to the repository are split into a part containing big data and a part with the remaining
data handling them separately with different technqiues. For example
one may use git to administrate the remaining data part. It also adds information to each object such as
the version, the author, date etc. and each object is labeled with a category defining the role in the ml process.
Package: paillierlib
paillier-libThis package provides a simple implementation of the Paillier cryptosystem using gmpy2.Usagefrom paillierlib import paillier
from gmpy2 import mpz

key_pair = paillier.keygen()  # Optional param.: bit size (default = 2048)

m1 = mpz(10)
m2 = mpz(1)
c1 = paillier.encrypt(m1, key_pair.public_key)
c2 = paillier.encrypt(m2, key_pair.public_key)

# Example homomorphic operations
# Addition
paillier.decrypt(c1 + c2, key_pair.private_key) # => 11
paillier.decrypt(c1 - c2, key_pair.private_key) # => 9
paillier.decrypt(c1 + c1 + c2, key_pair.private_key) # => 21

# Multiplication (ciphertext with plaintext)
m3 = mpz(2)
paillier.decrypt(c1 * m3, key_pair.private_key) # => 20Requirementsgmpy2 (tested v2.0.8)Planned WorkEncode inputs, rather than requiring GMP MPZ objects.
Package: painface
PainFace Data Analysis SuiteWelcome to the official package for PainFace grimace data analysis!This package offers a few handy methods that you can use to extract useful metrics and action unit data from the raw .csv data exported by PainFaceInstallationOpen Command Prompt/Terminal or whatever shell you choose.If you do not already have pip installed, follow the instructions at this link:https://pip.pypa.io/en/stable/installation/Once pip is installed, simply use the below command to install.pipinstallpainfaceUsageWindows:Once painface is installed, ensure that all of the raw .csv file data that you have exported from PainFace is in one folder/directory.Then, once you are inside the folder/directory in which all of your csv files are located, right click in the Explorer, and select Open in Terminal. This is especially important, as you must be in the directory with all of your data in order for the data filtering commands to work properly.Once the terminal has opened, Open a python environment by typing inpythonfollowed byimportpainfaceThe painface pacakage contains two key methods: batch_grimace and AU_grimaceBatch_grimace:This method is the first method you will typically use in order to generate individualized csv files which serve as the initial filtering of the data. In addition to these csv files, a data_log.csv file will be generated with a summarized set of results for your data.The method parameters are threshold, start time, stop time, and the threshold for the number of action units (the threshold should be between 1 and 5 units)To use the method, use the command:painface.batch_grimace(threshold,start,stop,AUThreshold)and replace each of the parameters with your desired values.
You will likely be prompted with the number of files that you wish to be processed, so just type Y or N depending on if the number of files looks correct or not.AU_grimace:This method is used to generate a timeseries analysis of each of the possible action unit scoring combinations, and uses the .csv files generated by the batch_grimace command as an input.The method parameters are threshold, start time, and stop timeTo use the method, use the command:painface.AU_grimace(threshold,start,stop)and replace each of the parameters with your desired values.KEEP IN MIND THAT YOU MUST USE batch_grimace BEFORE USING AU_grimaceLicenseMIT
Package: painlessdb
PainlessDB - Taking Your Pain away to the moon 🚀Contribute·Community·Documentation🎫 Introduction :PainlessDB is a Python-based free and open-source Database. PainlessDB makes
it really easy when it comes to basic database operations like['Create', 'Get', 'Update', 'Delete']and a Really easy way to create your
Database Schema.It's really easy and Simple to get started with your database. Just create your
Database Schema and enjoy your new database.The whole purpose of creating this Database was to make it really easy to do Database
stuffs when you need it, maybe you need it in a specific situation where you don't
want to do all those geeky stuffs just to create and do some simple database tasks.✅Easy & Simple to Understand and Implement✅Easy to build your database Schema✅Core database Functionalities: [Get, Create, update, Delete]✅Supports multiple Datatypes [str, int, float, bool, list, dict, datetime.datetime]✅Searching/Query FunctionalityRead TheDocsTo quickly get Started! 🍰With our continuous updates you will keep getting new features and bug fixes.How to install?via pip (recommended) :pip install PainlessDBorvia pip + github:pip install git+https://github.com/AidenEllis/PainlessDB.gitorDownload this repo and use it.🥞 Wanna Support me?I am a high schooler doing these Stuffs and I Love it! 🍰 Star ⭐ the repo to encourage me to do more Open source stuff!ContributionsPainlessDB is open to contributions.Please readCONTRIBUTING.mdfor details on this project.Code of ConductPlease readCODE_OF_CONDUCT.mdfor details on our code of conduct.
Package: paintbox
PaintboxPaintbox is a parametric modeling and fitting toolbox designed to build and
model ths spectral energy distribution of galaxies from any kind of
spectro-photometric observations. The paintbox is designed to allow easy
construction of very general models by the combination of a few basic
classes of objects that compose the spectral features, such as stellar
population continuum and emission lines.FeaturesOne of the premises of the Paintbox is to allow large flexibility to the
user in the modeling of data. Some of the main features already present in
the package includeModular structure that separates the task of building the model from the fitting process.Interpolation tools for any pre-computed stellar population models.Modeling using templates.Simple kinematic tool to model the LOSVD.Easy extension to any other type of SED features.Complete control over SED model propertiesInstallationInstall Paintbox by running:pip install paintboxContributeSource Code: github.com/cebarbosa/paintboxLicenseThis project is Copyright (c) Carlos Eduardo Barbosa and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theAstropy package templatewhich is licensed under the BSD 3-clause license. See the licenses folder for
more information.
Package: painted-word
painted-word=========================This is a tool that allows people to take a photo from the browser, add a caption that will attach a logo of your choice, and submit it.  When an admin approves the image, it will render on the photo campaign index page.  Think of this as an out of the box 'wearethe99percent', without having to sign up for Tumblr and deal with their limits on photo submissions.In the future, there may or may not be integration using the Actionkit CRM, as that is what we use at CEL, and this might allow people to instantly see their image upload without requiring approval.Written mostly by Mike Vattuone at Citizen Engagement Laboratory, with support from Josh Levinger.Installation=========Pretty dang easy:* Add 'paintedword' to your INSTALLED_APPS.* Add the app to your urls.pyurlpatterns = patterns('',url(r'^photo/', include('paintedword.urls')))* In the admin, create a Photo Campaign, including an example photo and logo, and view the campaign index page or take a picture at yourdomain.com/photo/campaign_slug* ...profit?While I am imagining a system that will easily integrate with your base template and make it so that you don't have to do any front-end to get this to play nicely, this is probably idealistic.  One thing we could try to do is create a TEMPLATE_PATH in which you would render your paintedword views to, but this will be something for later on...FAQ=========Q: This used to be called django-webcam-photoupload? What's with the stupid name?A: If a successful build tool can be named after a brunch-lunch hybrid, then I can name my app after a Television Personalities song. Simple, really.Get involved=========Have an idea for how to make this more useful?  A handy abstraction?  Please let me know!
Package: painter
Painter is an ANSI coloring library based on the excellentchalkandcolors.jslibraries for Node.js.  However, painter attempts to provide an even more
expressive API which reads like English.Painter is fully tested with 100% coverage and also completely Flake8
compliant too!Quick StartInstall Painter in your virtualenv as follows:pipinstallpainterAnd now, go ahead and use it to output colors to your terminal:from__future__importprint_functionfrompainterimportpaint# Simple printing of colorsprint('Welcome to Painter!',paint.red('I can paint things red'),paint.blue('and blue'))# Chaining colors and stylesprint(paint.blue.on_red.bold.underline('and far more complex combos too'))print()# Using color patternsprint(paint.rainbow('Awww look, a pretty rainbow :)'))print(paint.zebra('and a scary looking zebra!'))print()# Nested paintingprint(paint.on_red('I can also use a background color across',paint.blue('multiple'),paint.yellow('foreground colors')))# Custom separatorprint(paint('and','allow','you to use',paint.red('custom separators'),sep='-'))print()# Creating themescool_theme=paint.green.on_red.underline.boldprint('Creating',cool_theme('your own theme'),'is easy')# Easily disable painting of colorspaint.enabled=Falseprint('and I allow you to easily',paint.red('disable'),paint.blue('me'))print()paint.enabled=Trueprint('Hope you have a',paint.blue('lovely day!'),paint.green(':)'))The output of this script looks something like this:DocumentationFor full details on using Painter, please check out thePainter GitHub Page.
Package: painter-tk
使用tkinter的Canvas控件制作的画板程序, 支持新建、打开、编辑和保存文档。A painter program using Python tkinter module, which supports the drawing of Vectorgraph and useslistto save image data.最近更新(v1.5.0): 精简了源代码, 程序更加可读。截图:项目已发布在github上:https://github.com/qfcy/Python/tree/main/painter作者CSDN主页:https://blog.csdn.net/qfcy_/
Package: painting
PaintingPaintingcan print characters, numbers, lists and dictionaries better on the terminal. Support the escape of Emoji expression.In addition, new components are added, which does not represent the final qualityInstallpipinstallpainting-UUsagePrint various typesfrompaintingimportPaintingconsole=Painting()console.log("Welcome to Painting")# - Welcome to Paintingconsole.log(3.1415926)# - 3.1415926console.log(["the Great Wall","the Palace Museum","the White House",])# - (1) the Great Wall# - (2) the Palace Museum# - (3) the White Houseconsole.log({"Beijing":"P.R.C.","Washington D.C.":"U.S.A.","London":"U.K."})# - [Beijing] P.R.C.# - [Washington D.C.] U.S.A.# - [London] U.K.sThe escape of Emojiconsole.log("Welcome to Painting :red_heart:")# - Painting is great fun ❤LinkWebsite:https://github.com/isclub/paintingReleases:https://pypi.org/project/paintingLicenseMIT LICENSE
Package: paintprint
PaintPrintThis module can colorize any text in your terminalAuthor:tankalxat34Installingpip install PaintPrintQuick startHello worldThis is a simple hello world:fromPaintPrintimport*bprint("Hello world!",FORMATTING.BOLD,FOREGROUND.RED,BACKGROUND.GREEN)In your console you can see something like this:Unreadable symbolsIf you see incomprehensible symbols instead of colors in the console, perform this function at the beginning of your code:fromPaintPrintimport*neutralizeColorProblem()TemplatesIn this module you can use some templates for beautiful print on terminal:fromPaintPrintimport*bprint("TEMPLATES",FORMATTING.BORDERED,FORMATTING.BOLD,FOREGROUND.MAGENTA,BACKGROUND.WHITE)print("Template for links: "+bformat("python.org",TEMPLATE.URL))bprint("This is a `positive` template",TEMPLATE.POSITIVE)bprint("\tAnd this is a `negative`",TEMPLATE.NEGATIVE)print("You also can write "+bformat("yes",TEMPLATE.YES)+" and "+bformat("no",TEMPLATE.NO)+" like here")print("If you like "+bformat("black and white",TEMPLATE.BLACKWHITE1)+" or "+bformat("white and black",TEMPLATE.BLACKWHITE2)+" you can using special templates!")Result:All functionsOpentest fileand perform in your PC. You will see something like this:
Package: paipa
Python pipeline library developed bySTYLIGHT.Installationpip install paipaQuick summaryThe lib allows forthreaded pipelinesas well as so-calledco-routine
pipelines. The main use-case is limiting peak memory usage when doing complex
operations on large-ish (or small-ish) data-sets.Quick teaser (for more examples, seedocs):>>> pipeline = paipa.Pipeline(
...     [
...         (DownloadImage, 4),
...         (StoreDatabase, 1),
...     ],
... )
>>> pipeline.run()This example will create 5 threads, 4 for downloading images, one for storing
stuff to the database. The outputs of allDownloadImagesteps will be
forwarded to the oneStoreDatabasethread via aQueue.Thread startup and tear-down is handled by the library and doesn’t concern the
programmer at all. All (known) failure cases lead to either a re-spawning of
the failed thread or a controlled shutdown of the system. In no case the system
should block and do nothing, if it does then it’s definitely a bug and needs
to be reported.Pipeline ingestion can be done via a separate thread or by consuming an
iterable. In the case of using an iterable, an ingestion thread is created
which consumes the iterable in a controlled manner.DocumentationThreaded PipelineIntroductionMore ways to use pipelinesHow to write stepsCoroutine PipelineHow to write a coroutine pipelineFeatures and non-featuresWhat it does:process stuff concurrently, but one step after anotherallows class based, iterator based and function based stepscan scale steps independent of each other (manually)don’t ever do deadlocksnever expose the developer to the concept of a thread
(if she doesn’t like to)run in finite batches where all threads are terminated at the endrun in continuous mode while being fed through a queueautomatically rate limit each step to minimize memory usageterminate the pipeline in case of an Exception and propagate the error
to the developer, handling graceful shutdown of all threads involvedWhat it explicitly doesn’t do (if it doesn’t, it’s not a bug!):pipelines with multiple different parents. Multiple parentsof
the same typeare handled automatically though.auto-scale threadsbake bread (haha)What it shouldn’t do (if it does, it’s a bug!):confuse the user/developerhang on terminationhog resources
Package: paipline-zkchong
What is Paip? And Why Paip?"Paip" is a Malay word of "pipe", a lightweight library that is alternative to Scikit Learn pipeline.In general, Scikit Learn's pipeline facilitates the chaining of transformers in series and execute them in sequential order. Then, the pipeline can be serialized and re-used easily at different environment. However, the existing Scikit Learn's pipeline only allows objects to be loaded into the step list. If there is a small piece of code that is needed in between transformers (e.g. change of column name of data frame), we need to revise the small piece of code into object-oriented paradigm in order to obtain the corresponding objects. Need less to say, the class definition has to be imported at the new environment during the unpicke process.To overcome this issue, we have developed a lightweight library named Paip that allows both runtime code and transformers to be executed in the pipelines.For example, we have a data frame,Xthat needs to go through two feature transformations, i.e.transformer1andtransformer2. In between the transformers, we need to rename the column, "x1" to "x1_new". The code is presented in the following.frompaiplineimportPaip# Somewhere here, we have created the data frame, `X`.# Somewhere here, we have created feature transformation, named `transformer1`.# Somewhere here, we have created feature transformation, named `transformer2`.step_list=[]# Add the transformer 1 into the step list.step_list.append({'name':'Transformer 1',# name'obj_dict':{'transformer1':transformer1},# The objects to be serialized in pickle.'run_dict':{'transform':['X = transformer1.transform(X)'],# Run this code.},'output_list':['X',]# Whitelist these variables will go to next step.})# Add the transformer 2 into the step list.step_list.append({'name':'Transformer 1','obj_dict':{'transformer1':transformer1},'run_dict':{'transform':['X = X.rename(columns={"x1": "x1_new")',# Rename column.'X = transformer1.transform(X)'],},'output_list':['X',]})paip1=Paip(step_list)paip1.run('transform',# run those command that tag with "transform"{'X':X,},# the variable to bring into the pipeline.debug=True)# Debug output# Retrieve the outputX_out=paip1.output_dict['X']And to serialize Paip, we use the existing joblib.import joblib
joblib('./test.paip', paip1)
Package: paips
PaipsRun complex Python pipelines from command line using configuration files.How it worksFirst define tasks that you want to be executed. These tasks are written in Python and consist of classes inheriting from paips.core.Task. Tasks can receive parameters and data from other tasks, return data, and be interconnected.Then, write one or more configuration files, which will tell Paips which tasks to run, with which parameters and how they will be connected. Configuration files are written in yaml, are modular and can be easily composed and also modified from command line.Finally, run in command line:paiprun <config_path>and the tasks declared in the configuration file will be executed in the right order.Now, hands on pipelining...Installing paips is really easy, just run:pip install paipsNow that paips is installed, take a look at theTutorialto learn how to make your life easier when building those large and complex pipelines.
Package: pairdist
Pairwise distancesLicenseThis project is Copyright (c) Manodeep Sinha and licensed under
the terms of the BSD 3-Clause license. This package is based upon
theAstropy package templatewhich is licensed under the BSD 3-clause licence. See the licenses folder for
more information.ContributingWe love contributions! pairdist is open source,
built on open source, and we’d love to have you hang out in our community.Imposter syndrome disclaimer: We want your help. No, really.There may be a little voice inside your head that is telling you that you’re not
ready to be an open source contributor; that your skills aren’t nearly good
enough to contribute. What could you possibly offer a project like this one?We assure you - the little voice in your head is wrong. If you can write code at
all, you can contribute code to open source. Contributing to open source
projects is a fantastic way to advance one’s coding skills. Writing perfect code
isn’t the measure of a good developer (that would disqualify all of us!); it’s
trying to create something, making mistakes, and learning from those
mistakes. That’s how we all improve, and we are happy to help others learn.Being an open source contributor doesn’t just mean writing code, either. You can
help out by writing documentation, tests, or even giving feedback about the
project (and yes - that includes giving feedback about the contribution
process). Some of these contributions may be the most valuable to the project as
a whole, because you’re coming to the project with fresh eyes, so you can see
the errors and assumptions that seasoned contributors have glossed over.This disclaimer was originally written by
`Adrienne Lowe <https://github.com/adriennefriend>`_ for a
`PyCon talk <https://www.youtube.com/watch?v=6Uj746j9Heo>`_, and was adapted by
pairdist based on its use in the README file for the
`MetPy project <https://github.com/Unidata/MetPy>`_.