Saravanan666/pypi-org huggingface dataset:
Package: feed-seeker
It slant rhymes with “heat seeker”A library for finding atom, rss, rdf, and xml feeds from web pages. Produced at themediacloudproject. An incremental improvement overfeedfinder2, which was itself based onfeedfinder, written by Mark Pilgrim, and maintained by Aaron Swartz until his untimely death.InstallationThe library is available onPyPI:pipinstallfeed_seekerQuickstartBy default, the library usesrequeststo grab html and inspect it and find the most
likely feed url:fromfeed_seekerimportfind_feed_url>>>find_feed_url('https://github.com/mitmedialab/feed_seeker')'https://github.com/mitmedialab/feed_seeker/commits/master.atom'To do a more thorough search, usegenerate_feed_urls, which returns more likely candidates first.fromfeed_seekerimportgenerate_feed_urls>>>forurlingenerate_feed_urls('https://xkcd.com'):...print(url)...https://xkcd.com/atom.xmlhttps://xkcd.com/rss.xmlFor the most thorough search, add aspiderargument to do depth-first spidering of urls on the same hostname. Note the below call takes nearly four minutes, compared to 0.5 seconds forfind_feed_url.>>>forurlingenerate_feed_urls('https://github.com/mitmedialab/feed_seeker',spider=1):...print(url)...https://github.com/mitmedialab/feed_seeker/commits/master.atom,https://github.com/mitmedialab/feed_seeker/commits/95cf320796c487df8b70f9c42281d8f26452cc31.atom,https://github.com/mitmedialab/feed_seeker/commits/3e93490cb91f7652325c2fe41ef29a5be4558d6a.atom,https://github.com/mitmedialab/feed_seeker/commits/659311b8853c4c4a67e3b4bc67a78461d825a064.atom,https://github.com/mitmedialab/feed_seeker/commits/a8f7b86eac2cedd9209ac5d2ddcceb293d2404c9.atom,https://github.com/index.atom,https://github.com/articles.atom,https://github.com/dfm/feedfinder2/commits/master.atom,https://github.com/blog.atom,https://github.com/blog/all.atom,https://github.com/blog/broadcasts.atom,https://github.com/ColCarroll.atomIn a hurry?If you have a long list of urls, you might want to set a timeout withmax_time:>>>forurlin('https://httpstat.us/200?sleep=5000','https://github.com/mitmedialab/feed_seeker'):...try:...print('found feed:\t{}'.format(find_feed_url(url,max_time=3)))...exceptTimeoutError:...print('skipping{}'.format(url))skippinghttps://httpstat.us/200?sleep=5000foundfeed:https://github.com/mitmedialab/feed_seeker/commits/master.atomDifferences withfeedfinder2The biggest difference is that all functions are implemented as generators, and are evaluated lazily. Candidate feed links are actually accessed and inspected to determine whether or not they are a feed, which can be quite time consuming. We expose a function to find the most likely feed link, and another to lazily generate links in rough order from most prominent to least.There are also a few more heuristics based on our experience atmediacloud.
Package: feedsubs
RSS feed reader for Python 3.Features:Support for RSS, Atom and JSON feedsBackground synchronizationCaching and resizing of image embedded in feedsRemoval of tracking pixelsGrouping of feeds with tagsMulti-usersMIT licensedHosted serviceA free hosted version runs Feedsubs atfeedsubs.com,
it is the easiest way to start using the software without installing anything.Development guideFeedsubs is a typical Django project, anyone familiar with Django will feel
right at home. It requires:Python 3.6+Postgresql databaseRedis server for background tasksQuickstart:git clone git@github.com:NicolasLM/feedsubs.git
cd feedsubs/
python3 -m venv venv
source venv/bin/activate
pip install -e .[dev]
touch .env  # Put SECRET_KEY=foo and DB_PASSWORD=foo there
manage.py migrate
manage.py runserverBackground task workers can be started with:manage.py spinachSelf-hostingFeedsubs is a feed reader primarily focused toward large multi-users
installations, it may not be the easiest choice to host as a personal reader.
That being said, Docker makes it simple to deploy:Make your own settings module based onfeedsubs/settings/prod.pydocker run-d-vpath/to/my_settings.py:/my_settings.py-eDJANGO_SETTINGS_MODULE=my_settings-p8000:8000 nicolaslm/feedsubs waitressServe the port 8000 through a reverse proxy like nginx or caddyUsers can also deploy Feedsubs with pip instead of Docker:pip install feedsubs[prod]
manage.py waitressCopyright (c) 2018 Nicolas Le ManchetPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: feed-to-sqlite
feed-to-sqliteDownload an RSS or Atom feed and save it to a SQLite database. This is meant to work well withdatasette.Installationpipinstallfeed-to-sqliteCLI UsageLet's grab the ATOM feeds foritems I've shared on NewsBlurandmy instapaper favoritessave each its own table.feed-to-sqlitefeeds.dbhttp://chrisamico.newsblur.com/social/rss/35501/chrisamicohttps://www.instapaper.com/starred/rss/13475/qUh7yaOUGOSQeANThMyxXdYnhoThis will use a SQLite database calledfeeds.db, creating it if necessary. By default, each feed gets its own table, named based on a slugified version of the feed's title.To load all items from multiple feeds into a common (or pre-existing) table, pass a--tableargument:feed-to-sqlitefeeds.db--tablelinks<url><url>That will put all items in a table calledlinks.Each feed also creates an entry in afeedstable containing top-level metadata for each feed. Each item will have a foreign key to the originating feed. This is especially useful if combining feeds into a shared table.Python APIOne function,ingest_feed, does most of the work here. The following will create a database calledfeeds.dband download my NewsBlur shared items into a new table calledlinks.fromfeed_to_sqliteimportingest_feedurl="http://chrisamico.newsblur.com/social/rss/35501/chrisamico"ingest_feed("feeds.db",url=url,table_name="links")Transforming data on ingestWhen working in Python directly, it's possible to pass in a function to transform rows before they're saved to the database.Thenormalizeargument toingest_feedis a function that will be called on each feed item, useful for fixing links or doing additional work.It's signature isnormalize(table, entry, feed_details, client):tableis a SQLite table (from sqlite-utils)entryis one feed item, as a dictionaryfeed_detailsis a dictionary of top-level feed information, as a dictionaryclientis an instance ofhttpx.Client, which can be used for outgoing HTTP requests during normalizationThat function should return a dictionary representing the row to be saved. Returning a falsey value for a given row will cause that row to be skipped.DevelopmentTests usepytest. Runpytest tests/to run the test suite.
Package: feedwarrior
NAMEfeedwarrior - Feeds, warrior styleDESCRIPTIONInspired by, this project seeks to simplify
the creation and maintenance of engineering work logs.Entries of each individual log has core text content. Additionally,
every entry may have any number of file attachments associated with it.SYNOPSISfeedwarrioradd [ -c config_file] [ -z ] [ --task-id ] [
--task-uuid ] < -s subject >feedwarriorattach [ -c config_file] [ -z ] < -e entry > filefeedwarriorcreate [ -c config_file] [ --alias name ]feedwarriorentry [ -c config_file] [ -z ] [ --task-id ] [
--task-uuid ] < -s subject > < path_to_content >feedwarriorlist [ -c config_file]feedwarriorshow [ -c config_file] < -l feed_id >Common options-llog_id:   Log id to operate on. May be the full uuid or the alias defined at
creation time.-cconfig_file:   Load configuration from file.Options for show--headers:   Include multipart-headers in output.Options for attach-eentry_id:   uuid of entry to attach file to.Options for create--aliasalias_id:   Create log id alias that may be used as argument to-lfor
future operations.Options for add and entry-ssubject:   Entry subject.--task-idid:   Associate the log entry with the given taskwarrior numeric id. This
is a quasi-magical instruction which will resolve to the actual
taskwarrior uuid.--task-uuiduuid:   Associate the log entry with the given taskwarrior numeric uuid.ATTACHMENTSBothfeedwarrior addandfeedwarrior entrywill return the
created entry id.This id can be used further with the-eflag for attachments.Any file in any format may be an attachment. The feedwarrior tool
imposes little if no restriction on what data qualifies as valid
attachment data.INTERACTIVE ENTRIESThefeedwarrior addcommand enables interactive definition of
textual update entry contents.It will open an editor (at the moment hardcoded tovim) to receive
the contents.DATA STORELog entries are stored as email-like MIME Multipart messages, wrapped in
a json metadata structure.Only the default, simple filesystem structure is currently available as
storage backend.EXAMPLE# create feed
feedwarrior create --alias myfeed

¤ add log entry
ENTRY=`feedwarrior -l myfeed entry -s "first update" /path/to/contents.txt

# attach file to entry
feedwarrior attach -e $ENTRY <attachment_file>

¤ interactively add log entry contents
entry=`feedwarrior -l myfeed add -s "second update" 

# show current state of feed (chronologically)
feedwarrior -l myfeed showLICENSEThis documentation and its source is licensed under the Creative Commons
Attribution-Sharealike 4.0 International license.The source code of the tool this documentation describes is licensed
under the GNU General Public License 3.0.COPYRIGHT AND CONTACTLouis Holbrookhttps://holbrook.noPGP: 59A844A484AC11253D3A3E9DCDCBD24DD1D0E001SOURCE CODEhttps://holbrook.no/src/feedwarrior/log.htmlABOUT THIS DOCUMENTThis document was generated usingpandoc -f man -t markdown ...
Package: feedwerk
feedwerkWerkzeugused to have an Atom/RSS feed
generator, which was pretty great, even if it didn't make the most sense as part
of the Werkzeug package.This repository is a straight-forward extract of theatom codefrom Werkzeug version0.16.1, including unit tests.install & migrate from werkzeug.contrib.atom$pipinstallfeedwerkRename importswerkzeug.contrib.atom→feedwerk.atom- from werkzeug.contrib.atom import AtomFeed, FeedEntry+ from feedwerk.atom         import AtomFeed, FeedEntrybugs & maintenanceThis project exists to help kick the can of migrating atom libraries when
upgrading werkzeug, so the only goal is compatibility with0.16.1ofwerkzeug.contrib.atom. No new features will be considered. Bug fixes may be
rejected if they might be relied-on by anyone still using old Werkzeug.On the other hand, if you like the API that Werkzeug used to have for atom
feeds, please feel free to fork this project and build and improve on it! I
managed (painfully!) to retain the git history for this module and its tests,
so I think it's a good starting place :)changesv1.2.0 2024-01-28Python deprecateddatetime.datetime.utcnow():Usedatetime.now(timezone.utc)with.utcnow()fallback for python 2.7#6@uniphilwith thanks to@Siecjeforreporting it.v1.1.0 2022-04-24Some features we depended on were deprecated in werkzeug 2.0 and removed in 2.1:removewerkzeug.utils.escapein favour of MarkupSafe#2@mar1aduseResponseinstead ofwerkzeug.wrappers.BaseResponse#3@uniphilBoth changes are backwards-compatible with previous werkzeug back to 1.0. Client code that depends on the return type ofAtomFeed.get_response()may need to be updated in rare cases.work on feedwerkrun tests$python-mpytestlints$flake8--max-line-length=100*.pytestsfeedwerkAcknowledgementsThe atom feed generator was written by Werkzeug developers from 2007–2019. The
project history affecting this code has been retained in version control; seecontributors. The
full source and history of Werkzeug is available atpalletes/werkzeug. Werkzeug and this
project are free to use under the terms of theBSD 3-Clause License.This project is not affiliated with Werkzeug.
Package: feedzai-altair-theme
feedzai-altair-themeFeedzai's theme forAltaircharts.Sneak peekQuickstartInstallationViapip:pipinstallfeedzai-altair-themeViaPipenv:pipenvinstallfeedzai-altair-themeViaPoetry:poetryaddfeedzai-altair-themeViaPDM:pdmaddfeedzai-altair-themeViaPyflow:pyflowinstallfeedzai-altair-themeUsageimportaltairasaltalt.themes.enable("feedzai")You can find some example charts in thedemo.ipynbnotebook.DevelopmentCreate the development environment:makeinitAfter implementing changes, run the command to check the types and to format and lint the code (you can also use the individual commands defined in theMakefile):makeallTo see the changes applied to some example charts, use thedemo.ipynbnotebook:makedemoMiscInstall the feedzai-altair-theme package in an environment without an Internet connectionFirst, download the repo and move its root folder to your environment. From there, you can install the package with pip by pointing to the folder path and adding some extra flags:pipinstall-efeedzai-altair-theme/--no-deps--no-build-isolationThis command assumes that the feedzai-altair-theme packagedependenciesare already installed in your environment. For more information about the additional flags, check thepip install documentation.
Package: feedzilla
============================Feedzilla Django Application============================Changelog=========0.21::------* Bug fixes and a little code clean up* pt_BR localization* ``django-tagging`` replaced by ``django-taggit``* Using tag slug in URL0.20::----* lxml instead BeautifulSoup* New dependencies: lxml, grab* Templates for ATOM/RSS feed moved to templates/feedzilla/feed directoryAbout feedzilla===============This is Django application that adds to your site ability to aggregateATOM/RSS feeds and display them in single stream. In other words you canuse feedzilla to build planet site.Project page: http://bitbucket.org/lorien/feedzillaInstallation============* Use ``pip`` or ``easy_install`` to install *feedzilla* package.* Install dependencies (see below).* Add feedzilla to INSTALLED_APPS.* Run ``manage.py syncdb`` or ``manage.py syncdb --migrate`` if you use South.* Include ``url('', include('feedzilla.urls'))`` in url config.* Setup Site instance via Django admin interface.* Setup feedzilla settings via settings.py. See available settings below.You have to import default settings with ``from feedzilla.settings import *``line.* Setup static files. You should copy or symlink ``static/feedzilla`` directorycontents from feedzilla installation directory to your ``%MEDIA_ROOT%``.* Play with templates. Probably you'll want override some of default templates.Dependencies============See* django-common* django-taggit* south* feedparser* lxml* grab* transliterateSettings========* ``FEEDZILLA_PAGE_SIZE`` - number of items per page* ``FEEDZILLA_SUMMARY_SIZE``* ``FEEDZILLA_SITE_TITLE`` - used in feed generation* ``FEEDZILLA_SITE_DESCRIPTION`` - used in feed generationFor actual list of settings look into ``feedzilla/settings.py``
Package: feel-it
FEEL-IT: Emotion and Sentiment Classification for the Italian LanguageAbstractSentiment analysis is a common task to understand people’s reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?An abundance of approaches has been introduced for tackling both tasks. However, at least for Italian, they all treat only one of the tasks at a time. We introduce FEEL-IT, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: anger, fear, joy, sadness. By collapsing them, we can also do sentiment analysis. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results.We release an open-source Python library, so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.LicenseCode comes from HuggingFace and thus our License is an MIT license.For models restrictions may apply on the data (which are derived from existing datasets) or Twitter (main data source). We refer users to the original licenses accompanying each dataset and Twitter regulations.Data AccessSend us an email :)FeaturesEmotion Classification (fear, joy, sadness, anger) in ItalianSentiment Classification (positive, negative) in ItalianInstallingpipinstall-Ufeel-itJump start TutorialsNameLinkSentiment and Emotion Classification (stablev1.0.2)How To UseThe two classifiers are very easy to use. You can also directly use our colab tutorial!fromfeel_itimportEmotionClassifier,SentimentClassifieremotion_classifier=EmotionClassifier()emotion_classifier.predict(["sono molto felice","ma che cazzo vuoi","sono molto triste"])>>['joy','anger','sadness']sentiment_classifier=SentimentClassifier()sentiment_classifier.predict(["sono molto felice","ma che cazzo vuoi","sono molto triste"])>>['positive','negative','negative']CitationPlease use the following bibtex entry if you use this model in your project:@inproceedings{bianchi2021feel,
  title = {{"FEEL-IT: Emotion and Sentiment Classification for the Italian Language"}},
  author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
  booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
  year = "2021",
  publisher = "Association for Computational Linguistics",
}HuggingFace ModelsYou can find our HF Models here:NameLinkMilaNLProc/feel-it-italian-emotionEmotion ModelMilaNLProc/feel-it-italian-sentimentSentiment ModelDevelopment TeamFederico Bianchi<f.bianchi@unibocconi.it> Bocconi UniversityDebora Nozza<debora.nozza@unibocconi.it> Bocconi UniversityDirk Hovy<dirk.hovy@unibocconi.it> Bocconi UniversityNoteRemember that this is a research tool :)Other Resourceshttps://github.com/RacheleSprugnoli/Esercitazioni_SA(very nice dataset for emotion analysis)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.3 (2021-03-18)Release with starting documentation0.1.0 (2021-03-17)First release on PyPI.
Package: feeluown
FeelUOwn - feel your ownFeelUOwn 是一个稳定、用户友好以及高度可定制的音乐播放器。特性稳定、易用：一键安装，各流行平台均有打包（如 Arch Linux, Windows, macOS 等）有各媒体资源平台的插件，充分且合理的利用全网免费资源（如网易云、QQ、Bilibili 等）基础功能完善，桌面歌词、资源智能替换、多音质选择、nowplaying 协议等核心模块有较好的测试覆盖、核心接口保持较好的向后兼容可玩性强：提供基于 TCP 的交互控制协议基于文本的歌单，方便与朋友分享、设备之间同步支持基于 Python 的配置文件.fuorc，类似.vimrc和.emacs快速试用使用系统包管理器一键安装 FeelUOwn 及其扩展吧！对于 Arch Linux 和 macOS，你可以分别使用如下方式安装：# Arch Linuxyay-Sfeeluown# 安装稳定版，最新版的包名为 feeluown-gityay-Sfeeluown-netease# 按需安装其它扩展yay-Sfeeluown-kuwo
yay-Sfeeluown-bilibili# macOS（也可以下载打包好的二进制）brewtapfeeluown/feeluown
brewinstallfeeluown--with-battery# 安装 FeelUOwn 以及扩展feeluowngenicon# 在桌面生成 FeelUOwn 图标Windows 用户可以在 Release 页面下载预打包好的二进制。
Gentoo, NixOS, Debian 等 Linux 发行版也支持使用其系统包管理器安装！
详情可以参考文档：https://feeluown.readthedocs.io/，
也欢迎你加入开发者/用户交流群。免责声明FeelUown（以下简称“本软件”）是一个个人媒体资源播放工具。本软件提供的所有功能和资料
不得用于任何商业用途。用户可以自由选择是否使用本产品提供的软件。如果用户下载、安装、
使用本软件，即表明用户信任该软件作者，软件作者对任何原因在使用本软件时可能对用户自己
或他人造成的任何形式的损失和伤害不承担责任。任何单位或个人认为通过本软件提供的功能可能涉嫌侵犯其合法权益，应该及时向 feeluown
组织书面反馈，并提供身份证明、权属证明及详细侵权情况证明，在收到上述法律文件后，
feeluown 组织将会尽快移除被控侵权内容。（联系方式： yinshaowen241 [at] gmail [dot] com ）
Package: feerci
FEERCI: A Package for Fast non-parametric confidence intervals for Equal Error Rates********************************************FEERCI** is an opinionated, easy-to-use package for calculating EERs and non-parametric confidence intervals efficiently. It offers a single method, ``feerci.feerci``, that returns both an EER and CI for provided impostor and genuine scores. The only dependency is numpy.Installation=======``pip install feerci``What's New=======0.2.0--------- Switched output arguments around, to make more intuitive sense0.1.0--------- Initial release of packageLicense=====**FEERCI** is distributed under the MIT licenseVersion=====0.2.0Examples======Calculating just an EER::import feerciimport numpy as npimpostors = np.random.rand(100)genuines = np.random.rand(100)eer,_,_,_ = feerci.feerci(impostors,genuines,is_sorted=False,m=-1)Calculating an EER and 95% confidence interval::eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False)Calculating an EER and 99% confidence interval::eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False,ci=.99)Calculating an EER and 99% confidence interval on 1000 bootstrap iterations::eer,ci_lower,ci_upper,bootstrapped_eers = feerci.feerci(impostors,genuines,is_sorted=False,m=1000,ci=.99)
Package: feetech-servo-sdk
Feetech-Servo-SDKThis is source code fromofficial feetech repository.
The repository structure copies the structure of theoriginal libraryStructureroot dirrectory
     |---scservo_sdk 
     |---scsservo_sdk_exampleThescsservo_sdk_exampledirectories contain examples of using the library.The source code of the library is located in thescservo_sdkdirectory.Thescsservo_sdk_sourcedirectory contains the original archive with the source code of the library from the developer.UsageTested onLinux raspberrypi 5.15.61-v8+ #1579 SMP PREEMPT Fri Aug 26 11:16:44 BST 2022 aarch64.
Python versionPython 3.9.2.Method 1. Clone repositry$ cd /usr/src/
$ sudo git clone https://github.com/Adam-Software/Feetech-Servo-SDK.git
$ sudo chown -R pi Feetech-Servo-SDK
$ cd Feetech-Servo-SDK/scsservo_sdk_example
$ python3 ping.py
Succeeded to open the port
Succeeded to change the baudrate
[ID:001] ping Succeeded. SCServo model number : 1540Method 2. Install pip packageCopy the sample file to any location convenient for you. In the example I use/home/pi/FeetechTestFiles$ pip install feetech-servo-sdk
$ cd /home/pi/FeetechTestFiles
$ python3 ping.py
Succeeded to open the port
Succeeded to change the baudrate
[ID:001] ping Succeeded. SCServo model number : 1540