Saravanan666/pypi-org huggingface dataset:
Package: fe25519
Pure-Python data structure for working with Ed25519 (and Ristretto) field elements and operations.PurposeThis library provides a native Python implementation ofEd25519field elements and a number of operations over them. The library makes it possible to fill gaps in application prototypes that may have specific limitations with respect to their operating environment or their ability to rely on non-Python dependencies.The implementation is based upon and is compatible with the corresponding implementation of Ed25519 and Ristretto field elements used inlibsodium. For more information and background about the underlying mathematical structures and primitives, consult materials aboutCurve25519, theRistrettogroup, and the relatedEd25519system.Installation and UsageThis library is available as apackage on PyPI:python-mpipinstallfe25519The library can be imported in the usual ways:importfe25519fromfe25519importfe25519DevelopmentAll installation and development dependencies are fully specified inpyproject.toml. Theproject.optional-dependenciesobject is used tospecify optional requirementsfor various development tasks. This makes it possible to specify additional options (such asdocs,lint, and so on) when performing installation usingpip:python-mpipinstall.[docs,lint]DocumentationThe documentation can be generated automatically from the source files usingSphinx:python-mpipinstall.[docs]cddocssphinx-apidoc-f-E--templatedir=_templates-o_source..&&makehtmlTesting and ConventionsAll unit tests are executed and their coverage is measured when usingpytest(see thepyproject.tomlfile for configuration details):python-mpipinstall.[test]python-mpytestConcise unit tests are implemented with the help offountains; new reference specifications for these tests can be generated by running the testing module directly:pythontest/test_fe25519.pyStyle conventions are enforced usingPylint:python-mpipinstall.[lint]python-mpylintsrc/fe25519test/test_fe25519.pyContributionsIn order to contribute to the source code, open an issue or submit a pull request on theGitHub pagefor this library.VersioningBeginning with version 0.1.0, the version number format for this library and the changes to the library associated with version number increments conform withSemantic Versioning 2.0.0.PublishingThis library can be published as apackage on PyPIby a package maintainer. First, install the dependencies required for packaging and publishing:python-mpipinstall.[publish]Ensure that the correct version number appears inpyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has anautomation rulethat activates and sets as the default all tagged versions. Create and push a tag for this version (replacing?.?.?with the version number):gittag?.?.?gitpushorigin?.?.?Remove any old build/distribution files. Then, package the source into a distribution archive:rm-rfbuilddistsrc/*.egg-infopython-mbuild--sdist--wheel.Finally, upload the package distribution archive toPyPI:python-mtwineuploaddist/*
Package: fe507
FE507FE507 is a simple yet very powerful, 'batteries included' intuitive package for data analysing.How to use?import thesettingsmodel to configure thedata_dirwhere all of your data is located. (Notice: FE507 expects all
your data to be incsvformat.)fromfe507importsettingssettings.data_dir="./data/"# you csv files is stored in the directory named `data` in your current directoryImport base classes from the packagefromfe507importData,DataSource,RateOfReturnType,TimeFrameType3. Enjoy.
# fe507Basic StructuresExamples:importplotly.expressaspximportmatplotlib.pyplotaspltsp500=Data(DataSource.SP500)bist100=Data(DataSource.BIST100)bistall=Data(DataSource.BISTALL)gold=Data(DataSource.GOLD)btceth=Data(DataSource.BTCETH)exchange_rates=Data(DataSource.EXCHANGE_RATES)year_range=YearRange(from_year=2015,to_year=2022)sp=Collection(sp500.data,name="S&P500",currency=USD).get_range(year_range.from_year,year_range.to_year).get(on="Index")b1=CurrencyAwareCollection(bist100,exchange_rates,name="BIST100",currency=TRY).get_range(year_range.from_year,year_range.to_year).get(on="IndexUSD")ba=CurrencyAwareCollection(bistall,exchange_rates,name="BISTALL",currency=TRY).get_range(year_range.from_year,year_range.to_year).get(on="IndexUSD")gd=Collection(gold.data,name="Gold",currency=USD).get_range(year_range.from_year,year_range.to_year).get(on='Price ($/t oz)')btc=Collection(btceth.data,name="Bitcoin",currency=USD).get_range(year_range.from_year,year_range.to_year).get(on='Bitcoin')g=CollectionGroup([sp,b1,ba,gd,btc])ror_sp_w=sp.frequency(WEEK).ror()ror_b1_w=b1.frequency(WEEK).ror()ror_ba_w=ba.frequency(WEEK).ror()ror_gd_w=gd.frequency(WEEK).ror()ror_btc_w=btc.frequency(WEEK).ror()g_ror_d=CollectionGroup([ror_sp_w,ror_b1_w,ror_ba_w,ror_gd_w,ror_btc_w])
Package: fead
feadThis is a tool for advertising other blogs you like on your own
by embedding the summary of their latest post(s) extracted from their web feed.
It is a rewrite ofopenringwith (rejected) concurrency support
in Python without any third-party library.InstallationOn POSIX systems, run the usualmake installwith configurablePREFIX.
The following programs are needed for building and installation:makehelp2maninstallPython 3.11+ is also required, both for generating the manual and at runtime.On other systems, apackage installer specific for Pythonmight be preferred.Usage$fead--helpUsage: fead [OPTION]...Generate adverts from web feeds.Options:-h, --help            show this help message and exit-v, --version         show program's version number and exit-F PATH, --feeds PATHfile containing newline-separated web feed URLs-f URL, --feed URL    addtional web feed URL (multiple use)-s, --strict          abort when fail to fetch or parse a web feed-n N, --count N       maximum number of ads in total (default to 3)-p N, --per-feed N    maximum number of ads per feed (default to 1)-l N, --length N      maximum summary length (default to 256)-t PATH, --template PATHtemplate file (default to stdin)-o PATH, --output PATHoutput file (default to stdout)Any use of -f before -F is ignored.TemplateThe template is used by Pythonstr.formatto generate each advert.
It can contain the following fields, delimited by braces ({and}).source_title: title of the web feedsource_link: URL to the feed's websitetitle: title of the feed itemlink: URL to the itemtime: publication timesummary: truncated content or descriptionThe publication time is a Pythondatetime.datetimeobject,
which supports at least C89 format codes, e.g.{time:%Y-%m-%d}.ExamplesGiven the these URLs in afeedsfile:https://adol.pw/index.xml
https://cnx.gdn/feed.xml
https://xrvs.net/index.xmlAdvertisement of the two latest blogs among them, along with articles
by Drew DeVault, can be generated via the following command.echo"<article><h3><a href='{link}'>{title}</a></h3>{summary}&mdash;<a href='{source_link}'>{source_title}</a>, {time:%F}</article>"|fead-Ffeeds-fhttps://drewdevault.com/blog/index.xml-n2ContributingPatches should be sent to~cnx/misc@lists.sr.htusinggit send-emailwith the following configurations:git config sendemail.to '~cnx/misc@lists.sr.ht'
git config format.subjectPrefix 'PATCH fead'Bug reports and feedbacks should be sent to the same list,
with the program's name mention in the subject.CopyingThis 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, eitherversion 3 of the License,
or (at your option) any later version.
Package: feagi-agent
What is Feagi-agent?The Feagi-agent is the core of the Feagi agent and it serves multiple purposes. It works behind the scenes and serves as an API for Feagi.
Keep in mind, you will need Feagi to connect with feagi_agent. Feagi can be running on a website, Docker, or locally on your computer.If you would like to learn more about how to run Feagi, please visit:https://github.com/feagi/feagi/tree/stagingQuick Start:You can start with any of the quick start options. The easiest and simplest method is to use feagi_agent_video_capture, which focuses solely on video capture functionality. Once you have installed it using pip3 install feagi_agent_video_controller, simply type python3 -m feagi_agent_video_controller --ip 127.0.0.1 to run it.If you would like to create your own code using the Feagi API and callfeagi_interfaceorretina, you can use the following code:from feagi_agent import feagi_interfacefrom feagi_agent import retinaFeagi Agent Packages:Currently, we have more than 2 packages:feagi_agent_video_controller,feagi_agent_freenove, andfeagi_agent_mycobot. You can find more packages here:https://github.com/feagi/feagi/tree/feature-mycobot-updated/peripheralsThe requirements forfeagi-agentcan be found here:feagi-agent requirements
Package: feagi-agent-mycobot
Quick start using feagi_agentgit clone https://github.com/feagi/feagi.gitcd feagi/dockerdocker compose -f feagi.yml buildWait until #3 step is complete.docker compose -f feagi.yml upload it in your preferred browser:http://localhost:3000/genome/modeClickSample GenomesSee Gazebo and brain activity loaded. Feel free to play with your robot!Any issue? Needs detailed documentation about docker?Deployment documentationWhere is feagi_agent?feagi_agentallows you to integrate with our FEAGI with your preferred robots.feagi_agenthas been tested onfreenove_smart_car, Gazebo, Godot, Tello, and Psychopy.You can use it on your computer or docker.What is inside feagi_agent?There are two large library: feagi_interface and retina.feagi_interface.pyhandles the bridge between your project and FEAGI. It allows FEAGI to communicate/controls your robot.retina.pyis the vision where it can see things through any type of camera.configuration.py is REQUIREDYou will need to have your own configuration where you can adjust setting to see the different results. If you don't have one, use the template below:app_name = 'embodiment'

network_settings = {
    "feagi_host": "feagi",
    "feagi_api_port": "8000",
    'TTL': 2,
    'last_message': 0,
}

capabilities = {
    "vision": {
        "type": "ipu",
        "disabled": False,
        "count": 1,
        "width": 8,
        "height": 8,
        "deviation_threshold": 0.05,
        "retina_width_percent": 60,
        "retina_height_percent": 40,
        "central_vision_compression": [64, 64],
        "peripheral_vision_compression": [8, 8],
        "previous_data": {}
    }
}

message_to_feagi = {"data": {}}See examples here:Tello's configurationFreenove_smart_carGazeboWhere is feagi_agent being used?It is being used in python code.Just install through pip.Windows:pip install feagi-agentLinux/Mac:pip3 install feagi-agentand after that, you can just usefrom feagi-agent import feagi_interfaceSee examples:Tellos' code
Package: feagi-agent-video-capture
Quick startConnect to your webcampip3 install feagi_agent_video_capturepython3 -m feagi_agent_video_captureWhat is feagi_agent_video_capture?It allows you to feed videos related to FEAGI using a webcam, gif, mp4, picamera, or screen recording. It can also utilize multiple flags. The purpose of this is to enable FEAGI to interact with and work with its peripherals.Requirements.txtThese are the requirements which will be updated over time. The script will use this requirement to scan.Here:requirements.txtFlags:--loop: True or False (Default is False)
--ip: (xxx.xxx.xx.xxx) Default is 127.0.0.1
--device: Target the port or index to use. Default is 0.
--video: You need to provide the file path.(mp4, gif, or wav) Default: None
--port: Change the API port of FEAGI. Default is 8000.Let's say you have a file called earth.gif. If you want to run gif or mp4 only, but it is on your desktop, you will need to specify the file path. So, if you are in the Desktop folder while the file is also in the desktop folder, you can simply do this:python3 -m feagi_agent_video_capture --video earth.gif(mac and linux)python -m feagi_agent_video_capture --video earth.gif(Windows)But if you are on the Desktop while your earth.gif is in the Downloads folder, do this:python3 -m feagi_agent_video_capture --video path/to/directory/earth.gifIf you want it to loop, just append--loopin the command.python3 -m feagi_agent_video_capture --video earth.gif --loop trueIf you have two webcams and you want to use the second webcam, you can do this:python3 -m feagi_agent_video_capture --device 1(Depending on what device index your webcam has, 1 is an assumption for the index of the second webcam. 0 is the default.)If you want to send your webcam feed to FEAGI on another computer, you will need the computer's local IP. Do it like this:python3 -m feagi_agent_video_capture --ip xxx.xxx.xx.xxx(Replace the x with the other machine's IP address.)
Package: feagi-agent-webcam
Quick startpip3 install feagi_agent_webcampython3 -m feagi_agent_webcam --ip 127.0.0.1What is feagi_agent_webcam?Feagi_agent_webcam enables you to connect your webcam to Feagi while it's running. This allows you to see what your webcam sees on the brain activity. The response rate can be adjusted depending on the refresh rate you set, which can range from slow to fast.In addition, Feagi_agent_webcam can be used with various devices, including picamera, webcam, and robots' vision.Requirements.txtThese are the requirements which will be updated over time. The script will use this requirement to scan.Here:requirements.txt
Package: fealpy
FEALPy: Finite Element Analysis Library in PythonWe want to develop an efficient and easy to use finite element software
package to support our teach and research work.We still have lot work to do.关于 FEALPy 的中文帮助与安装信息请查看：FEALPy 帮助与安装InstallationCommonTo install the latest release from PyPi, usepipinstall-UfealpyIf you have norootaccess on Linux/MacOS, please trypython-mpipinstall-UfealpyUsers in China can install FEALPy from mirrors such as:AliyunTsinghuaUSTCFrom Sourcegitclonehttps://github.com/weihuayi/fealpy.gitcdfealpy
pipinstall.For developers, please usepip install -e .to install it in develop mode.On Linux system such as Ubuntu or Fedora, or MacOS, maybe you should usepip3 install -e .to install it in
develop mode.UninstallationpipuninstallfealpyDockerTo be added.Reference and AcknowledgementWe thank Dr. Long Chen for the guidance and compiling a systematic documentation for programming finite element methods.http://www.math.uci.edu/~chenlong/programming.htmlhttps://github.com/lyc102/ifemCitationPlease citefealpyif you use it in you paperH. Wei and Y. Huang, FEALPy: Finite Element Analysis Library in Python,https://github.com/weihuayi/fealpy,Xiangtan University, 2017-2021.@misc{fealpy,title={FEALPy: Finite Element Analysis Library in Python. https://github.com/weihuayi/fealpy},url={https://github.com/weihuayi/fealpy},author={Wei, Huayi and Huang, Yunqing},institution={Xiangtan University},year={Xiangtan University, 2017-2021},}
Package: feapder
FEAPDER简介feapder是一款上手简单，功能强大的Python爬虫框架，内置AirSpider、Spider、TaskSpider、BatchSpider四种爬虫解决不同场景的需求。支持断点续爬、监控报警、浏览器渲染、海量数据去重等功能。更有功能强大的爬虫管理系统feaplat为其提供方便的部署及调度读音:[ˈfiːpdə]文档地址官方文档：https://feapder.comgithub：https://github.com/Boris-code/feapder更新日志：https://github.com/Boris-code/feapder/releases爬虫管理系统：http://feapder.com/#/feapder_platform/feaplat环境要求：Python 3.6.0+Works on Linux, Windows, macOS安装From PyPi:精简版pipinstallfeapder浏览器渲染版：pipinstall"feapder[render]"完整版：pipinstall"feapder[all]"三个版本区别：精简版：不支持浏览器渲染、不支持基于内存去重、不支持入库mongo浏览器渲染版：不支持基于内存去重、不支持入库mongo完整版：支持所有功能完整版可能会安装出错，若安装出错，请参考安装问题小试一下创建爬虫feapdercreate-sfirst_spider创建后的爬虫代码如下：importfeapderclassFirstSpider(feapder.AirSpider):defstart_requests(self):yieldfeapder.Request("https://www.baidu.com")defparse(self,request,response):print(response)if__name__=="__main__":FirstSpider().start()直接运行，打印如下：Thread-2|2021-02-0914:55:11,373|request.py|get_response|line:283|DEBUG|--------------FirstSpider.parserequestfor----------------url=https://www.baidu.commethod=GETbody={'timeout':22,'stream':True,'verify':False,'headers':{'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36'}}<Response[200]>
Thread-2|2021-02-0914:55:11,610|parser_control.py|run|line:415|DEBUG|parser等待任务...
FirstSpider|2021-02-0914:55:14,620|air_spider.py|run|line:80|INFO|无任务，爬虫结束代码解释如下：start_requests： 生产任务parse： 解析数据参与贡献贡献之前请先阅读贡献指南感谢所有做过贡献的人!爬虫工具推荐爬虫在线工具库：http://www.spidertools.cn爬虫管理系统：http://feapder.com/#/feapder_platform/feaplat验证码识别库：https://github.com/sml2h3/ddddocr微信赞赏如果您觉得这个项目帮助到了您，您可以帮作者买一杯咖啡表示鼓励 🍹也可和作者交个朋友，解决您在使用过程中遇到的问题学习交流知识星球：17321694作者微信： boris_tmQQ群号：485067374加好友备注：feapder
Package: feapder-pipelines
FEAPDER 管道扩展简介此模块为feapder的pipelines扩展，感谢广大开发者对feapder的贡献随着feapder支持的pipelines越来越多，为减少feapder的体积，特将pipelines提出，使用者可按需安装管道PostgreSQL贡献者：沈瑞祥联系方式：ruixiang.shen@outlook.com安装pip install feapder_pipelines[pgsql]使用在feapder项目的setting.py中使用如下配置# PostgreSQLPGSQL_IP=PGSQL_PORT=PGSQL_DB=PGSQL_USER_NAME=PGSQL_USER_PASS=ITEM_PIPELINES=["feapder_pipelines.pipelines.pgsql_pipeline.PgsqlPipeline"]细节注：入库时 ON CONFLICT(key) 默认为id或通过如下sql查出来的第一个值selectcolumn_namesfrom(selectt.relnameastable_name,i.relnameasindex_name,array_to_string(array_agg(a.attname),', ')ascolumn_namesfrompg_classt,pg_classi,pg_indexix,pg_attributeawheret.oid=ix.indrelidandi.oid=ix.indexrelidanda.attrelid=t.oidanda.attnum=ANY(ix.indkey)andt.relkind='r'andt.relnamelike'%'groupbyt.relname,i.relnameorderbyt.relname,i.relname)asreswheretable_name='table_name';
Package: feaplat
pydictSame as json.dumps or json.loads, pydict supprot pydict.dumps and pydict.loadsExample:>>>importpydict>>>pydict.dumps({"a":True,"b":False,"c":[1,2,3]}){"a":True,"b":False,"c":[1,2,3]}>>>pydict.loads('{"a": True, "b": False, "c": [1, 2, 3]}'){'a':True,'b':False,'c':[1,2,3]}
Package: feapson
feapsonSame as json.dumps or json.loads, feapson support feapson.dumps and feapson.loadsExample:>>>importfeapson>>>feapson.dumps({"a":True,"b":False,"c":[1,2,3]})'{"a": True, "b": False, "c": [1, 2, 3]}'>>>feapson.dumps({"a":True,"b":False,"c":[1,2,3]},indent=4,ensure_ascii=False)'''{"a": True,"b": False,"c": [1,2,3]}'''>>>feapson.loads('{"a": True, "b": False, "c": [1, 2, 3]}'){'a':True,'b':False,'c':[1,2,3]}>>>feapson.loads('{"a": true, "b": false, "c": [1, 2, 3]}'){'a':True,'b':False,'c':[1,2,3]}
Package: fear-and-greed
Python wrapper for CNN'sFear & Greed Index.Fetches CNN's website, parses the index value and returns the data as a three-element tuple.Installationpipinstallfear-and-greedUsage exampleimportfear_and_greedfear_and_greed.get()Returns a three-element namedtuple with (a) the current value of the Fear & Greed Index, (b) a description of the category into which the index value falls (from "Extreme Fear" to "Extreme Greed") and (c) the timestamp at which the index value was last updated on CNN's website. Example:FearGreedIndex(value=31.4266,description='fear',last_update=datetime.datetime(2022,4,25,16,51,9,254000,tzinfo=datetime.timezone.utc)),)Requests to CNN's website are locallycachedfor 1m.
Package: fear-data
fear_dataPython package used to analyze files generated from Med-Associates VideoFreeze software.
An example notebook is provided indocs/.InstallationThe easiest way to install fear_data is withpip. First, clone the
repository.git clone https://github.com/kpuhger/fear_data.gitNext, navigate to the cloned repo and type the following into your terminal:pip install .Note:The installation method currently is not likely to work.
For the time being it is recommended to add a .pth file to yoursite-packagesfolder to add the repo to your system's path.Use the terminal to navigate to yoursite-packagesfolder (e.g.,cd opt/miniconda3/lib/python3.10/site-packages)Add.pthfile pointing to your repo path> touch `fear_data.pth` # create pth file
> open `fear_data.pth` # add path to repo in this fileFeaturesExperiment configuration filesThe recomended way to set up an experiment is to use aexpt_config.yamlfile (see herefor an overview of YAML).
This allows you to use a template notebook to analyze data from different experiments by simply providing the path to theexpt_config.yamlfile. An example configuration file can be found indocs/expt_config.yaml.The functionfd.create_expt_config(...)can be used to automatically generate anexpt_config.yamlfile from template.The functionfd.update_expt_config(update_dict, ...)allows you to update an expt_config with information provided in update_dict.NOTE:The keys in update_dict should be identical to expt_config.Loading dataTo load Video Freeze data:Defineconfig_pathvariable.Load data usingfd.load_tfc_data(...)Group labels can be added viafd.add_group_labels(...)Visualizing dataPlot aesthetics are applied via @style_plot decorator.Can pass arguments to modify axes info (e.g., labels, labelsize, title, fig_size, ranges (xlim/ylim) -- check docs for more info.Setsave_fig=Trueto apply @savefig decorator and save figure, can setfig_pathif desired (default set to Desktop).plot_fc_bins: pointplot across time for every 'Component'sessionsets plot aes (label tone bins for train/tone, label shock for train)plot_fc_phase: usekindfor two ways to plot data by phase (baseline, tone, trace, iti)kind='point': pointplot by phase.kind='bar': barplot by phase.adds swarmplot of subject data by default setpts=Falseto remove.Analyzing dataUse thepingouin python packagefor statistcal analysis.
An example analysis can be found indocs/stats-eample_analysis.ipynb.
Package: feast-azure-blob-connect
Feast on AzureThis project provides resources to enable aFeastfeature store on Azure. There are two options for operating Feast on Azure:Feast Azure Provideris a simple, light-weight architecture that acts as a plugin to allow feast users to connect to registry store.Feast Azure ProviderTheFeast Azure provideracts like a plugin that allows Feast users to connect to:Azure blob storage for the feastregistry store🐱‍👤 Getting StartedIf you want to install locally:pipinstallfeast-azure-blob-connect