Saravanan666/pypi-org huggingface dataset:
Package: jabbar
Just Another Beautiful progress BAR (some might replace Beautiful by Boring).jabbar is a python package implementing a simple progress bar. The output
looks like:75%|█████████████████|750/1000It is lightweight, easy to use and customizable.
As a special feature, it gracefully deals with seeing more items than expected, e.g. 1100/1000.
Also, it supports unicorns.For very serious work, you may want to try e.g. the slightly more professionaltqdmpackage.Installjabbar can be installed fromPyPIvia your favorite shell:$pipinstalljabbaror from the latest code onGitHubwith:$pipinstallgit+https://github.com/yannikschaelte/jabbar.gitGet startedjabbar is quite flexible and can operate in different modes.jabbar can simply wrap around any iterable to make loops show a little progress
bar:fromjabbarimportjabbarfor_injabbar(range(1000)):passThe updating scheme can also be individually specified:fromjabbarimportjabbarwithjabbar(total=1000,width=30)asbar:for_inrange(50):bar.inc(20)When usage of a context manager is undesirable, usejabbar.finish()to clean up the output.Further examples can be found in theexample/howto.ipynbnotebook.Licensejabbar is available under an MIT license.
Package: jabber-bot
Class jabber bot on pythonInstallpipinstalljabber-botExampleimportxmppfromjabber_botimportJabberBotconfig={'jid':'username@jabber.org','pass':'pass'}defmessage_handler(conn,mess):text=mess.getBody()user=mess.getFrom()reply=textconn.send(xmpp.Message(mess.getFrom(),reply))bot=JabberBot(config['jid'],config['pass'])bot.register_handler('message',message_handler)bot.start()LicenseMIT
Package: jabir
jabirWe developed the Python package namedjabirto generate atomic features. It can generate322 atomic features for any type of material, such as Electron Affinity, Electric Polarizability, Thermal Conductivity, Pettifor number, etc. Jabir calculates statistical relationships for each physical and chemical feature based on Element, Subscript, and Fraction.InstallationJaber employs some other libraries and packages to generate atomic features of materials, which need to be installed. These packages consist of pandas, numpy, mendeleev, pymatgen, chemparse and tqdm.Note:If you encounter any difficulties installing these packages on your local system, you can utilize theGoogle colabenvironment to install and utilize them seamlessly.How to use jabirAfter installing jabir, simply execute the following two lines of code.from jabir import mainprint(main())
Package: jabiru
jabiruA javascript language filter for pyspellingMore details can be found in theOnline Documentation.InstallationYou can install jabiru forPythonviapipfromPyPI.$ pip install jabiruNote: if using Python 3.4 the latest version of lxml only supports python 3.5 and above so install lxml 4.3.4Prerequisites:pyspellingDownload from PyPI.orghttps://pypi.org/project/jabiru/ContributingContributions are very welcome, consider using thefile an issueto discuss the work before beginning, but if you already have a Pull Request
ready then this is no problem, please submit it and it will be very gratefully
considered. TheContribution Guidelinesoutlines the jabiru commitment to ensuring all
contributions receive appropriate recognition.LicenseDistributed under the terms of theGPLv3license, "jabiru" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.Additional Documentation:Online DocumentationNews.Template Updates.Code of Conduct.Contribution Guidelines.
Package: jabledl
jabledl有的时候看的正好，想拖动一下，又要缓冲半天，所以在github上找了一下有没有合适的程序能够下载。然后发现了https://github.com/hcjohn463/JableTVDownload但是使用的时候还是会出现一些问题，所以我copy了整个代码，然后做了一些改动，包装了一下就有了这个包，初衷还是想让下载更加方便一些。所以我还做了docker。安装pipinstalljabledl或者极其推荐直接使用docker。怎么用jabletvdl[-h][--vtype{hot,new,all}][--urlsURLS]--outdirOUTDIR[--threadsTHREADS]JableTVDownloader

optionalarguments:-h,--helpshowthishelpmessageandexit--vtype{hot,new,all}Downloadlistofvideos--urlsURLSJableTVURLtodownload--outdirOUTDIROutputdirectory--threadsTHREADSthreadsnumber一般情况下，有特定的想看的，直接使用jabletvdl --urls [连接] --outdir [存储目录]就可以了。如果没有特定想看的，只是想先下载一些，然后以备不时之需，那推介使用jabletvdl --vtype hot --outdir [存储目录]。其中的hot表示最热的那一个列表里面进行下载（第一页），new表示最新的那一个列表里进行下载，all就是首页的进行下载（包括最新和最热）。Docker最推荐的还是直接使用dockerdockerrun-v【想要下载到哪个目录】:/work/downloads/videodownload/vd:latest例如dockerrun-v/home/sidepr/downloads/jabletv:/work/downloadsvideodownload/vd:latest然后就会下载最热的那一个列表中的电影了。
Package: jableparser
from __future__ import print_function
import requests
from jableparser import PageModel
html = requests.get("https://hollywoodmask.com/entertainment/jason-carroll-cnn-age-gay.html", verify=False).text
pm = PageModel(html)
result = pm.extract()

print("==title==")
print(result['title')
print("==content==")
for x in result['content']:
    if x['type'] == 'text':
        print(x['data'])
    if x['type'] == 'image':
        print("[IMAGE]", x['data']['src'])
    if x['type'] == 'html':
        print("Raw table string: )
        print(x['data'])
        print("Processed table data if two columns: )
        print(pm.processtable(x['data']))
Package: jabstract
[![Build Status](https://travis-ci.org/internap/jabstract.svg?branch=master)](https://travis-ci.org/internap/jabstract)# Welcome unit testers!## That's not cool:```def test_something():api_response = {"field1": "value1","field2": "value2",...."field37": "value37",...."field7632": "value7632"}myapi.return_value = api_responseresult = production_code()assert result == "value37"```^ This is annoying if your `production_code` method only uses field37 right?Of course you could only define the field37 in your test but if a log or something else irrelevant to THIS test uses another field, it has to be there even if it's irrelevant to THIS test.## That's prettier:```api_response = jabstract({"field1": "value1","field2": "value2",...."field37": "value37",...."field7632": "value7632"})def test_something():myapi.return_value = api_response(field37="value37")result = production_code()assert result == "value37"```^ The test is so much more beautiful!# USAGEDeclare your json responses somewhere:```from jabstract import jabstractapi_response = jabstract({... json-ish payload ...})```Then use it in your tests by defining only relevant fields:```.return_value = api_response(key=value)```It even supports complex payloads!```api_response = jabstract({"client": {"name": "John doe","email": "johndoe@example.org"}}).return_value = api_response(client=dict(name="Foobar"))```\* note that `response["client"]["email"]` will keep its default value.# Best practicesTests using jabstracted payload should define only what is relevant to the test, not less, not more, so that it is obvious to the human eye where a value come fromlet's test this code```def name_getter(payload):return payload["client"]["name"]```## **Good** example:```api_response = jabstract({"client": {"name": "John doe","email": "johndoe@example.org"}})def test_name_getter():payload = api_response(client=dict(name="Baboon 2.0"))assert name_getter(payload) == "Baboon 2.0"```**Reviewer says** : Ohhh so it takes the name of the client from the payload... +2## ~~BAD~~ example```api_response = jabstract({"client": {"name": "John doe","email": "johndoe@example.org"}})def test_name_getter():payload = api_response()assert name_getter(payload) == "John doe"```**Reviewer says** : Who the hell is john doe... click on payload... ahhh i see... meh, +1
Package: jacaranda
JacarandaTable of ContentsJacarandaDescriptionExamplesInstallationGitHubPipExamplesLicenseDescriptionJacaranda is a wrapper package around several Data Science and Machine Learning
librarys,  such asPyTorchXGboostwhich creates an easy interface to interact, and automatically tune models produced
by these libraries.ExamplesExamples for using the Jacaranda API to tune the following list of models is available in the examples folder.AutoencoderVariational AutoencodeXgboost decicion tree1D Convolutional Neural NetworkMultilayer PerceptronInstallationCurrently, there are various ways this package can be installed.
These includeGitHubpipGitHubTo install from GitHub there are two options,
the first option is to clone the repository and do a local installation from the cloned directory.gitclonegit@github.com:jacaranda-analytics/jacaranda.gitcdjacaranda/&&pipinstall.The second option is to install from GitHub without first cloning the repository,
to install the latest master branch, run the command.pipinstallhttps://github.com/jacaranda-analytics/jacaranda/archive/master.zipPipTo install through pip, simply runpipinstalljacarandaLicenseMIT
Package: jaccard-index
jaccard_indexJaccard Index ComputationThis package provides computation Jaccard Index based on n-grams for strings. This can be used as a metric for computing similarity between two strings e.g. Indentity resolutionInstallationInstall using pip:#  pip install jaccard-indexTo install using the archive, unpack it and run:# python setup.py installUsageA common use case is to compare strings for similarity:>>> from jaccard_index.jaccard import jaccard_index
>>> jaccard_index("abc","")
0
>>> jaccard_index("accessary", "accessory")
0.6
>>> jaccard_index("coffee", "coffee")
1
Package: jaccard-precalc
Jaccard Precalculated String MatcherThe Jaccard index measures exhaustive substring comparison of two strings. This package is a slightly modified Jaccard with pre-calculation accelerate results.fromjaccard_precalc.JaccardPrecalcimportJaccardPrecalcstring_list=['Andrew Matte, 123 Main St, Toronto, Canada']jac=JaccardPrecalc(string_list)# jac.search(query_string, number_of_results)results=jac.search('Andy Matte, Toronto, CA',1)# returns a list of dicts where each dict is {input: score}, sorted by top score
Package: jaccount-auth
Python Jaccount AuthA small plugin to help use jaccount login in python programs.Forked from tc-imba'spython-jaccount-cli.Exampleimportasynciofromgetpassimportgetpassfromjaccount_cliimportJaccountCLIAsyncIOasyncdefmain():asyncwithJaccountCLIAsyncIO("https://umjicanvas.com/login/openid_connect")ascli:awaitcli.init()captcha_ascii=cli.captcha_generate_ascii()print()print(captcha_ascii)print()# or you can use# cli.captcha_show_external()captcha=input("Please enter the shown captcha: ")username=input("Please enter jaccount username: ")password=getpass("Please enter password: ")awaitcli.login(username,password,captcha)asyncwithcli.session.get("https://umjicanvas.com/api/v1/users/self/favorites/courses?include[]=term&exclude[]=enrollments",headers={"Accept":"application/json"},)asresponse:print(awaitresponse.text())# print cookiescookies=cli.get_cookies()forkey,cookieincookies.items():print('Key: "%s", Value: "%s"'%(cookie.key,cookie.value))if__name__=="__main__":asyncio.get_event_loop().run_until_complete(main())LicenseMIT
Package: jaccount-cli
Python Jaccount CLIA small plugin to help use jaccount login in cli programs.Exampleimportasynciofromgetpassimportgetpassfromjaccount_cliimportJaccountCLIAsyncIOasyncdefmain():asyncwithJaccountCLIAsyncIO("https://umjicanvas.com/login/openid_connect")ascli:awaitcli.init()captcha_ascii=cli.captcha_generate_ascii()print()print(captcha_ascii)print()# or you can use# cli.captcha_show_external()captcha=input("Please enter the shown captcha: ")username=input("Please enter jaccount username: ")password=getpass("Please enter password: ")awaitcli.login(username,password,captcha)asyncwithcli.session.get("https://umjicanvas.com/api/v1/users/self/favorites/courses?include[]=term&exclude[]=enrollments",headers={"Accept":"application/json"},)asresponse:print(awaitresponse.text())# print cookiescookies=cli.get_cookies()forkey,cookieincookies.items():print('Key: "%s", Value: "%s"'%(cookie.key,cookie.value))if__name__=="__main__":asyncio.get_event_loop().run_until_complete(main())LicenseMIT
Package: jaccs
Jaccs allows you to pull data from JSON objects using string Python
expressions. For example:>>> from jaccs import access
>>> access({'a': 'b': [{'c': 1}, {'c': 2}]}, '_.a.b[1].c')
2This is expected to be most useful for combining specifications stored in
external text files (say JSON or YAML) with data objects in Python.Documentation is athttps://jiffyclub.github.io/jaccs/.
Package: jacdac-python
Jacdac PythonA Python client and server SDK for Jacdac.Jacdacis a plug-and-play hardware/software stack
formicrocontrollersand their peripherals (sensors/actuators),
with applications to rapid prototyping, making, and physical computing.Jacdac HomeJacdac Python DocumentationAPI ReferenceDiscussions athttps://github.com/microsoft/jacdac/discussionsIssues are tracked onhttps://github.com/microsoft/jacdac/issues
Package: jacinle
JacinleJacinle is a personal python toolbox.
It contains a range of utility functions for python development,
including project configuration, file IO, image processing, inter-process communication, etc.[Website][Examples][Jacinle References][JacLearn References][JacTorch References]InstallationClone the Jacinle package (be sure to clone all submodules), and add the bin path to your PATH environment.git clone https://github.com/vacancy/Jacinle --recursive
export PATH=<path_to_jacinle>/bin:$PATHOptionally, you may need to install third-party packages specified inrequirements.txtCommand Linejac-run xxx.pyJacinle comes with a command line to replace thepythoncommand:jac-run. In short, this command
will automatically add the Jacinle packages intoPYTHONPATH, as well as adding a few vendor Python packages
intoPYTHONPATH(for example,JacMLDash). Using this command
to replacepython xxx.pyis the best practice to manage dependencies.Furthremore, this command also supports a configuration file specific to projects. The command will search for
a configuration file namedjacinle.ymlin the current working directory and its parent directories. This file
specifies additional environmental variables to add, for example.project_root:true# tell the script that the folder containing this file is the root of a project. The directory will be added to PYTHONPATH.system:envs:CUDA_HOME:/usr/local/cuda-10.0# set needed environment variables here.path:bin:# will be prepended to $PATH/usr/local/binpython:# will be prepended to $PYTHONPATH/Users/jiayuanm/opt/my_python_libvendors:# load additional Python packages (root paths will be added to PYTHONPATH)pybullet_tools:root:/Users/jiayuanm/opt/pybullet/utilsalfred:root:/Users/jiayuanm/opt/alfredjac-crun <gpu_ids> xxx.pyThe same asjac-run, but takes an additional argument, which is a comma-separated list of gpu ids,
following the convension ofCUDA_VISIBLE_DEVICES.jac-debug xxx.pyThe same asjac-run, but sets the environment variableJAC_DEBUG=1before running the command.
By default, in the debug mode, anipdbinterface will be started when an exception is raised.jac-cdebug <gpu_ids> xxx.pyThe combinedjac-debugandjac-crun.jac-updateUpdate the Jacinle package (and all dependencies insidevendors/).jac-inspect-file xxx.json yyy.pklStart an IPython interface and loads all files in the argument list. The content of the files can be accessed viaf1,f2, ...Python LibrariesJacinle contains a collection of useful packages. Here is a list of commonly used packages, with links to the documentation.jacinle.*: frequently used utility functions, such asjacinle.JacArgumentParser,jacinle.TQDMPool,jacinle.get_logger,jacinle.cond_with, etc.jacinle.io.*: IO functions. Two of the mostly used ones are:jacinle.io.load(filename)andjacinle.io.dump(filename, obj)jacinle.random.*: almost the same asnumpy.random.*, but with a few additional utility functions and RNG state management functions.jacinle.web.*: the oldjacwebpackage, which is a customized wrapper around thetornadoweb server.jaclearn.*: machine learning modules.jactorch.*: a collection of PyTorch functions in addition to thetorch.*functions.
Package: jack
# jackA command line tool for time-based queries on log files.## How to installpip install jackTo run jack, `cd` to a directory with log files (`.log` or `.log.gz` extension) and run something like this:jack 8:15am to 8:20amjack 8:15am to 8:20am PDTjack 16:30 to 16:45jack May 1st 16:30 to May 1st 16:45jack 2014-03-01 8:15am to 2014-03-01 8:20am PDTjack yesterday to today## How to create and ship a change* Mark your change in a branch off `master` and submit a pull-request on GitHub. Ping @dbader to review it.#### Testing* Test the package locally and make sure it installs cleanly and works via `pip install -e .` (in the project root).#### Releasing* Bump `PACKAGE_VERSION` in `setup.py`* Create a git tag for the release:* `git tag 0.0.1 -m "The best release so far"`* `git push --tags origin master`* `python setup.py sdist upload -r pypi`## Roadmap* Turn this into a `pip install`able package* Add usage / nice errors for the CLI* Add unit tests* Set up TravisCI continuous integration## Changelog* `0.1.0`: First working PyPI release.## Glossary(todo)## Where to get helpTalk to [@dbader](https://twitter.com/dbader).
Package: jackalify
Искажение изображений (алгоритм Seam carving)Задача:получение из изображения искаженного изображения (видео).Возможности искажателяЗагрузка изображения через web и получение искаженного изображения/видео на выходе с возможностью скачивания;Искажение изображения и его сохранение с помощью командной строки.Пример работы алгоритма:До:После:Зависимостиfastapinumpycv2Пользовательские интерфейсыweb-интерфейс на HTML, CSS, JS;интерфейс командной строки.Макет web-интерфейсаСтраница загрузки файла:Страница с отслеживанием прогресса создания результата:Страница с результатом и возможностью скачивания:Интерфейс командной строкиpython-mjackalify[-h][-w][-g][-oOUTPUT_PATH] [INPUT_PATH]Если не указан флаг-g, то создастся статичная картинка. Также если не указано имя выходного файла, то искажённое изображение (или gif) будет создано рядом с исходной картинкой. Если не указан флаг-w, то обязательно наличие входного файла, однако если флаг-wуказан, никакие другие аргументы не должны присутствовать.Опции:-h- помощь;-w- запуск серверного интерфейса (fastapi), опционально;-o- имя выходного файла, опционально;-g- создание gif вместо статичной картинки, опционально;INPUT_PATH- путь к файлу с оригинальным изображением, опционально.Примеры:python-mjackalify wolf.png-owolf-jackal.png- получение искаженного изображенияwolf-jackal.pngpython-mjackalify wolf.png- получение искаженного изображенияwolf_jackalified.pngpython-mjackalify wolf.png-owolf-jackal.gif-g- получение искаженного видеоwolf-jackal.gifpython-mjackalify-w- запуск серверного интерфейсаПри установке пакета черезpipвместоpython-mjackalifyнужно вызыватьjackalify.
Package: jack-alsa-ctl
jack-alsa-ctlControl JACK audio with ALSA driver (or ALSA only) easily.Dependenciesjack-alsa-ctldepends on the following programs in your PATH:jack_control(only required byget_cardandset_card)amixerIn addition, JACK server should be configured to use ALSA driver.InstallationFrom pypi (recommended):pipinstalljack-alsa-ctlFrom git repo (for dev version)pipinstallgit+https://github.com/DCsunset/jack-alsa-ctlOr clone and install locally (for dev):gitclonehttps://github.com/DCsunset/jack-alsa-ctlcdjack-alsa-ctl
pipinstall.UsageCLIUse the commandjack-alsa-ctldirectly:jack-alsa-ctl--help
jack-alsa-ctlget_card
jack-alsa-ctlget_volumeSee help messages for more usage.Libraryfromjack_alsa_ctl.libimportget_jack_cardprint(get_jack_card())LICENSEAGPL-3.0. Copyright notice:jack-alsa-ctl
Copyright (C) 2022-2023 DCsunset

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
Package: jackattack
Jack AttackA repository for the purposes of learning to create python libraries and implements the JackAttack class.Methods for instantiation:Install through the Python Package IndexCloning this repository directlyPyPi InstallationFrom your command line, run:pip install jackattackCloning the RepoIn your working directory, use your command line to run:git clone https://github.com/jackrmcshane/jackattack.gitUsagefromjackattackimportJackAttackjack=JackAttack()jack.attack()...jack.run()OutputJack attacks you.
Jack attacks you.
Jack attacks you.
You die.
...
You scare Jack.
He runs away.Developing jackattackTo install jackattack along with the tools you will need to develop and run tests, run the following in your virtual environment:pipinstall-e.[dev]
Package: jack-bower
Installing frontend Django dependencies via bower.InstallationTo get the latest stable release from PyPipipinstalljack-bowerTo get the latest commit from GitHubpipinstall-egit+git://github.com/Celc/jack-bower.git#egg=bowerAddbowerto yourINSTALLED_APPSINSTALLED_APPS=(...,'bower',)UsageUse./manage.pybower_init<app_name>to bootstrap an app with.bowerrc,bower.jsonand.gitignore. Add your
dependencies tobower.json{"dependencies":{"backbone":"1.0.0","underscore":"1.4.4"}}Then just run./manage.pybower_installand it’ll install all the
dependencies in all theINSTALLED_APPSapps that has abower.json. Default install path isstatic/bower_components/<library>,
you can edit.bowerrcto change that.Use like normal in templates:{% load staticfiles %}
{% static 'components/bootstrap/bootstrap.js'%}ContributeIf you want to contribute to this project, please perform the following
steps:# Fork this repository
# Clone your forkmkvirtualenv-ppython2.7jack-bowernpminstall-gbowermakedevelopgitadd.&&gitcommit# Send us a pull request
Package: jacked
NOTE: This project is in ALPHA statejacked ~python on roidsA light and easy to use dependency injection framework.Injectobjects,functions,classesorlistscontaining any of these.Letjackedautomatically discover your injectables in a package.Loose couplingon the juice!Excellent for making your code testable!Installpip install jackedUsageInject instancesTo inject instances, mark a class with theinjectabledecorator:fromjackedimportinjectable@injectableclassCat:defsound(self):return'meow'You can now inject it in a function anywhere. Place theinjectdecorator on
top of the function or method. Letjackedknow what type to inject by type
hinting your parameters:@injectdefwhat_sound_does_it_make(cat:Cat):print(cat.sound())what_sound_does_it_make()Inject functionsInjecting functions works similarly. Just make sure that your function has the
proper type hints:@injectabledefsome_func(x:int,y:int)->str:returnf'The sum of{x}and{y}is{x+y}'And like with instances, inject as follows:@injectdefdo_something(func:Callable[[int,int],str]):print(func(21,21))do_something()Inject classesAssuming that we have the sameCatinjectable like before, we can inject
that class as follows:fromtypingimportType@injectdefdo_something(cat_type:Type[Cat]):print(cat_type.__name__)do_something()Inject listsLet's suppose that we have the following two injectables of the same parent:classAnimal(ABC):@abstractmethoddefsound(self):raiseNotImplementedError@injectableclassCat(Animal):defsound(self):return'meow'@injectableclassDog(Animal):defsound(self):return'bark'You can now inject them in a list:@injectdefwhat_sound_does_it_make(animals:List[Animal]):foranimalinanimals:print(f'The{animal.__class__.__name__}does{animal.sound()}')what_sound_does_it_make()You could have also injected alistof classes or functions by hintingList[Type[...]]orList[Callable[...]](the...replaced by your
injection target).SingletonsYou can annotate an injectable as singleton, meaning that if the injectable is
a class, only one instance is ever injected:@injectable(singleton=True)classDog(Animal):defsound(self):return'bark'Auto discoveryYou can letjackeddiscover injectables in some package using thediscoverfunction:fromjackedimportdiscoverdiscover('path/to/your/package')All python modules in that package are imported and the injectables are
registered.