Saravanan666/pypi-org huggingface dataset:
Package: nawano
```___ ___ _ _ _ ___ ___ ___|   | .'| | | | .'|   | . ||_|_|__,|_____|__,|_|_|___|100% OSS, BSD-2```Nawano is a simple, fast and secure way to manage your Nano funds locally—without doing any syncing, and without having to trust a wallet provider on the *World Wide Web*.Seeds are encrypted with AES-256 and never leaves your computer.Node communication is done over RPC; locally or using a remote backend (such as Canoe).**Want to know more about Nano?**- Check out the [official website](https://nano.org/en/about).**Want some free Nano to get started?**- You can get a small amount for testing purposes at [nano-faucet.org](https://nano-faucet.org)Demo----Shows some common wallet operations.[![asciicast](https://asciinema.org/a/HevbcFFyi2OT7KJ6kpLyVbqJo.png)](https://asciinema.org/a/HevbcFFyi2OT7KJ6kpLyVbqJo)Installing----------**Linux (Ubuntu)**available setup (pip) environment variables:- USE_GPU (install OpenCL libs and set to 1 to use the GPU for work generation)```bash$ sudo apt-get install libb2-dev python3$ sudo pip3 install nawano --upgrade$ nawano```**MacOS**available setup (pip) environment variables:- USE_GPU (enabled by default, set to 0 to use CPU for work generation)- LINK_OMP (enabled by default, set to 0 to use gcc with builtin GOMP)```bash$ brew install gcc libb2 libomp python3$ sudo pip3 install nawano --upgrade$ nawano```**Windows**Nawano can be installed in Microsoft Windows using WSL.1. Enable WSL: https://docs.microsoft.com/en-us/windows/wsl/install-win102. Using **Microsoft Store**, install **Ubuntu Linux** and start it.3. Follow the **Ubuntu Linux** install instruction above.You may now close the terminal window and simply run ```wsl nawano``` to start the application.Project status--------------While Nawano does work, it's currently under heavy development and should probably be considered unstable as it lacks tests and extensive real-world use.**Quality improvements**- [ ] API documentation- [ ] Usage documentation (Wiki)- [ ] Unit tests & TravisCI**Features**- [ ] Transaction history- [ ] Application logging**Install guides**- [X] Linux- [X] MacOS- [X] WindowsFuture plans------- Ledger integration- Yubikey integration- Official Debian packageLicense-------Please bare in mind that—just as the official Nano software—Nawano uses the *BSD 2-Clause License* (i.e. no liability for loss of funds).Author------Robert Wikman \<rbw@vault13.org\>
Package: naxxatrapy
python-package-boilerplateBoilerplate for a Python PackagePackageBasic structure of package is├── README.md
├── packagename
│   ├── __init__.py
│   ├── packagename.py
│   └── version.py
├── pytest.ini
├── requirements.txt
├── setup.py
└── tests
    ├── __init__.py
    ├── helpers
    │   ├── __init__.py
    │   └── my_helper.py
    ├── tests_helper.py
    └── unit
        ├── __init__.py
        ├── test_example.py
        └── test_version.pyRequirementsPackage requirements are handled using pip. To install them dopip install -r requirements.txtTestsTesting is set up usingpytestand coverage is handled
with the pytest-cov plugin.Run your tests withpy.testin the root directory.Coverage is ran by default and is set in thepytest.inifile.
To see an html output of coverage openhtmlcov/index.htmlafter running the tests.Travis CIThere is a.travis.ymlfile that is set up to run your tests for python 2.7
and python 3.2, should you choose to use it.
Package: nayose
nayose is a Python module for data cleansing for Japanese address and is distributed under the MIT license.InstallationYou can install the latest this module with the command:pip install nayoseQuick startnayose has two main function of data cleansing for Japanese address.complement_addressis a function to complement dirty address. For example, the followings is a case of missing state(都道府県).from nayose import complement_address

complement_address("千代田区大手町1−1−1大手町あいうえビル")
> "東京都千代田区大手町1−1−1大手町あいうえビル"split_addressis a function to split full address to state(都道府県), city(市区郡) and street(その他).from nayose import split_address

split_address("東京都千代田区大手町1−1−1大手町あいうえビル")
> ('東京都', '千代田区', '大手町1−1−1大手町あいうえビル')Important linksOfficial source code repo:https://github.com/sonesuke/nayoseDownload releases:https://pypi.org/project/nayose/Issue tracker:https://github.com/sonesuke/nayose/issuesCitationIf you use nayose in a publication, we would appreciate citations:https://github.com/sonesuke/nayoseFor contributorsYou can contribute it quickly by using docker image with the command.git clone https://github.com/sonesuke/nayose.git
docker-compose run nayose bashFor test.bash scripts/testIf you add any modules, please re-build docker image after 'poetry update' .
Package: nazgul
nazgulWhat is this?Nazgul is a framework for performing GRB localization via fitting non-parametric models to their data time-series and computing the the time delay between them. It is currentrly built upon the magic of Stan and implements a parallel version of non-stationary Random Fourier Features. The idea is get away from heuristic methods such as cross-correlation which do not have a self-consistent statitical model.The idea is that satellites throughout the Sol system observe gamma-ray bursts at different times due to the finite speed of light. This creates a time delay in their observed light curves which can be used to triangulate the gamma-ray burst position on the sky. These triangulation create annuli or rings on the sky which Nazgul searchs for so that it, in the darkness, it can bind them to a location on the sky. (We are nerds, get over it).The heriarchical model is shown below and details can be found inlink here. If you find the method and/or code useful in your research we ask that you please cite the paper.The sister program to simulate time-delayed light curves ispyIPNand can be used to generate time-delayed light curves for algorithm testing.This work is a joint effort by:J. Michael BurgessEwan CameronDmitry SvinkinInstallationpipinstallnazgulNote!Nazgul is still under heavy development! Expect problems!
Package: nazo-image-utils
Nazo Image UtilsNazo Image Utils 是一个用于处理随机图像和图像处理的 Python 包。它包含两个类：RandImage和ProcessImage。安装您可以使用 pip 安装 Nazo Image Utils：pipinstallnazo-image-utilsProcessImageProcessImage 是一个用于处理图像的类。初始化fromnazo_image_utilsimportProcessImageprocess_image=ProcessImage(flush:bool=False,filter:bool=True,gallery_path:str="./gallary",pc_filter_size:Size=PC_SIZE,mobile_filter_size:Size=MOBILE_SIZE,)gallery_path为待处理图像的目录。当flush为True时，会忽视已生成的json文件中的内容。当filter为True时，会对图像进行过滤，只保留合格的图像。默认情况下, pc 端的合格图像尺寸为 1920x1080，移动端的合格图像尺寸为 1080x1920。初始化 ProcessImage 类的实例。方法try_processprocess_image.try_process()尝试处理所指定文件夹中的所有图片, 会分别存储到webp和jpeg文件夹中。默认情况下一个图片会分别生成三张图片, 分别为不同的分辨率的图片, 定为:source,th,md。还会生成两个文件分别为:manifest.json和manifest_mobile.json, 里面格式为:{"md5":{"source":"file_name"}}RandImageRandImage 是一个用于生成随机图像 URL 的类。初始化fromnazo_image_utilsimportRandImagerand_image=RandImage(pc_json_path="./manifest.json",mobile_json_path="./manifest_mobile.json",domain="https://example.com")初始化 RandImage 类的实例。方法processresult=rand_image.process(ua:bytes,number:int,platform:Optional[bytes]=b"pc",encode:Optional[bytes]=b"",size:Optional[bytes]=b"source",)处理程序，生成随机图像的 URL。参数ua(bytes): 用户浏览器 user-agent 标识，用于检查 WebP 支持。number(int): 请求的图像 URL 数量，范围为 1-10。platform(bytes): 平台（决定竖图还是横图），传递b"pc"或b"mobile"。encode(bytes): 编码，仅传递b"json"或者不传。size(bytes): 尺寸（仅决定返回的图片尺寸格式）。返回值bytes或list[str]: 返回图像 URL 列表或 JSON 字符串。最终格式为:[domain]/[webp|jpeg]/[md5].{size}.[webp|jpeg]，例如:https://nazo.run/webp/0a4d55a8d778e5022fab701977c5d840.source.webp。许可证本项目采用 MIT 许可证进行许可 - 请参阅LICENSE文件获取更多详细信息。
Package: nazo-ip2asn
ip2asnaiptoasntoolsPlease download the latest version and unzip：ip2asn-v4.tsv&&ip2asn-v6.tsvQuickstartfromnazo_ip2asnimportIp2Asnip2asn=Ip2Asn(ipv4file="ip2asn-v4.tsv",ipv6file="ip2asn-v6.tsv")print(ip2asn.lookup(b"8.8.8.8"))
Package: nazo-rand
nazo_randInspired to write fromPyewacketA fast random number generator for pythonrand_int.choice(seq: list|Tuple) -> Anyexample：fromnazo_randimportchoicetemp=[1,2,3,4,5,6]choice(temp)rand_int.randbelow(a: int) -> intexample:fromnazo_randimportrandbelowrandbelow(10)# -> [0, 10)randbelow(0)# -> [0, 0) => 0randbelow(-10)# -> (-10, 0]rand_int.randint(a: int , b: int) -> intexample:fromnazo_randimportrandintrandint(1,10)# -> [1, 10]randint(10,1)# -> [1, 10]randint(10,10)# -> [10, 10] => 10rand_int.randrange(start: int , stop: int = 0 , step: int = 1 )example:fromnazo_randimportrandrangerandrange(10)# -> [0, 10) by whole numbersrandrange(1,10)# -> [1, 10) by whole numbersrandrange(1,10,2)# -> [1, 10) by 2, odd numbersrandrange(-10)# -> [-10, 0) by 1randrange(10,1)# -> [1, 10) by 1randrange(10,0,2)# -> [0, 10) by 2, even numbersrandrange(10,10,0)# -> [10, 10) => 10
Package: nb
Nota Bene is a simple program that keeps tracks of your notes. It functions similar to git. You add your notes to it, and it provides you with a list of your notes and their tags. What is different about Nota Bene is that, your tags have to be in your files, you cannot tag them via NB.Note StructureNota Bene depends on your notes having a certain structure. It looks for YAML front matter in your note. If you provide a title and tags in the YAML front matter of your note, NB will store that information along with your note’s path. This way you can filter your notes using the tags in them.This way, NB can keep track of notes without caring about the markup you use in your notes.YAML Front MatterYAML front matter should look like this:---
title: My Interesting Title
tags: [life, unverse, everything]
date: 2014-11-30 18:18:18
---Date can also be in the followin format:date: 2014-11-30But these two are the only acceptable date formats. If you format dates differently, sorting behavior is undefined.Seehttp://jekyllrb.com/docs/frontmatter/for details.Why?Nota Bene is a simle tool that I wrote for myself in a couple of hours. I don’t even know whether I will use it or not, but hey, here it is.LicenseCopyright (c) 2014 Eren Inan CanpolatThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU 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 General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: nb2chan
nb2channonebot2酱（推送服务插件）nonebot2chan (push service plugin)Install itpipinstallnb2chan# or poetry add nb2chan# pip install git+https://github.com/ffreemt/nb2chan# poetry add git+https://github.com/ffreemt/nb2chan# To upgrade# pip install nb2chan -U# or poetry add nb2chan@latestUse it# bot.pyimportnonebot...nonebot.init()importnb2chan...(参看bot.py)目标qq号（例如QQ号 1234）加nonebot2机器人qq号好友nonebot2部署至外网ip，例如uvicorn --host 0.0.0.0 bot:app(火墙需放行nonebot2的端口)给qq号发消息(浏览器地址栏或curl/httpie或python reqests/httpx或云函数/claudflare workeretc.)：http://...:port/nb2chan/?Token=DEMO_TOKEN&qq=1234&msg=hello例如，qq 1234 加2129462094为好友后，即可从以下url发推送消息给 1234。（qq 2129462094 在okteto里提供推送消息服务。）https://nb2chan-dattw.cloud.okteto.net/nb2chan/?Token=DEMO_TOKEN&qq=1234&msg=hello1令牌也可在headers里设定，例如curl http://...:port/nb2chan/?qq=1234&msg=hello -H "token: DEMO_TOKEN"
http -v "http://...:port/nb2chan/?qq=1234&msg=hello world" "token: DEMO_TOKEN"其他nb2chan采用简单令牌鉴权。 有效令牌可在.env.nb2chan里设定。 默认有效令牌为['DEMO_TOKEN', 'SECRET_TOKEN'](参看config.py）
Package: nb2hugo
nb2hugonb2hugois a simple way to convert a Jupyter notebook to a Hugo markdown page.MotivationJupyter Notebook is a great way to create a single document that contains code that can be executed, formatted text to provide detailed explanations, as well as figures. Hugo is a simple yet very powerful static site generator. While a few solutions to convert a Jupyter notebook to a Hugo markdown with front matter already exist,nb2hugoput an emphasis on getting a result that looks similar to the original Jupyter notebook.InstallationUsing pip:pip install nb2hugoUsageIn your Jupyter notebook, start by using one or more markdown cells that will contain the front matter information. Next, add an html comment as a front matter divider: everything in the notebook before the End Of Front Matter divider<!--eofm-->will be the front matter. This approach is similar to the one used forcontent summaries.A markdown title before the<!--eofm-->divider will automatically become the front matter title. You can also provide other front matter fields by writting pairs of "key: value" on different lines.Below is an example of a notebook markdown cell that will become a front matter:# My notebook title

Date: 2018-06-01  
Author: firstname lastname  
Categories: category1, category2  
Tags: tag1, tag2, tag3  
<!--eofm-->All content after the<!--eofm-->divider will be considered as normal notebook content.Once you have finished writing your notebook, you can convert it using the following command:nb2hugonotebook_file--site-dirhugo_website_directory--sectioncontent_sectionA workflow for easily publishing notebooksAdemo siteshows how to combinenb2hugo, Hugo and Netlify to easily blog with Jupyter notebooks.
The associated Git repository is available athttps://github.com/vlunot/nb2hugo-demo.AuthorVincent Lunot-Initial work.LicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details.Acknowledgementsnb2hugois based onnbconvert.
Package: nb2pb
License: MIT OR Apache-2.0
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Source Code,https://github.com/dragazo/nb2pbProject-URL: Issues,https://github.com/dragazo/nb2pb/issues#nb2pb[NetsBlox](https://netsblox.org/) is an educational block-based programming environment (based on [Snap!](https://snap.berkeley.edu/)) which has a focus on advanced CS topics such as distributed computing, cybersecurity, and the internet of things.
[PyBlox](https://github.com/dragazo/PyBlox) is an educational Python environment which supports most of the same features as NetsBlox, but which has students program in native (unrestricted) Python rather than the block-based language.nb2pbis a rust crate that allows for the compilation of NetsBlox (nb) project files into PyBlox (pb) project files.## How to UseIn addition to being a native rust crate available on crates.io,nb2pbalso has (simplified) [Python bindings](https://pypi.org/project/nb2pb/), which are used by PyBlox.
As the python bindings are our main interest, they will be kept up to date as the rust crate evolves.## InstallationTo usenb2pbas a Python package, you can simply install it through pip.`sh pip install nb2pb `We build wheels for several operating systems and versions of python (>=3.6).
However, if there is not a wheel for your platform, pip will have to compile the rust crate from source, which will require installing [cargo](https://doc.rust-lang.org/cargo/getting-started/installation.html).
If this is the case, feel free to [submit an issue](https://github.com/dragazo/nb2pb/issues/new) including your operating system and processor architecture, and we can see if your system can be officially supported (without needing to be compiled by users) in the future.## Building WheelsTo build a wheel on the local system for installed versions of CPython and PyPy, run the following command:`sh maturin build--release`
Package: nb2pdf
Jupyter Notebook to PDF ConverterThis package converst Jupyter Notebooks (.ipynbfiles) to PDFs using nbconvert. It is designed to create manual submissions for thePython local graderbeing developed by the UC Berkeley Division of Data Science and Information.UsageThe converter by default leaves all cells alone butremovescode cells without an image in the output. (This is designed to output graphs generated in notebooks in the PDF.) Using metadata tags, cells can be included (with theincludetag) or excluded (with theignoretag).Changelogv0.4.0:Added autosave on export from Jupyter Notebook environment
Package: nb2py
Dumps marked code cells from a Jupyter notebook into a text file.FeaturesReads a Jupyter Notebook and export marked code cells with the
comment#~to a single.pyfile.Exports cells marked with custom comments.Exports a list of cells into a single file (useful to export markdown
cells as a README.md file)DependenciesNoneUsageimport nb2py
nb2py.dump('NB2PY_SOURCE.ipynb','nb2py.py')
nb2py.dump_indices('NB2PY_SOURCE.ipynb','README.md',[3,2])Notes: check that you saved your notebook before you usedumpto see
the changes!For examples, check thetutorial.CreditsCreated byHugo Guillen, 2017.
Package: nba_api
nba_apiAn API Client Package to Access the APIs of NBA.comnba_apiis an API Client forwww.nba.com. This package intends to make the APIs ofNBA.comeasily accessible and provide extensive documentation about them.Getting Startednba_apirequires Python 3.7+ along with therequestsandnumpypackages. Whilepandasis not required, it is required to work with Pandas DataFrames.pipinstallnba_apiNBA Official Statsfromnba_api.stats.endpointsimportplayercareerstats# Nikola Jokićcareer=playercareerstats.PlayerCareerStats(player_id='203999')# pandas data frames (optional: pip install pandas)career.get_data_frames()[0]# jsoncareer.get_json()# dictionarycareer.get_dict()NBA Live Datafromnba_api.live.nba.endpointsimportscoreboard# Today's Score Boardgames=scoreboard.ScoreBoard()# jsongames.get_json()# dictionarygames.get_dict()Additional ExamplesRequests/Response OptionsProxy Support, Custom Headers, and Timeout SettingsReturn Types and Raw ResponsesStatic Data SetsReduce HTTP requests for common and frequently accessed player and team data.Jupyter NotebooksPractical examples in Jupyter Notebook format, including making basic calls, finding games, working with play-by-play data, and interacting with live game data.DocumentationTable of ContentsPackage StructureEndpointsStatic Data Setsplayers.pyteams.pyJoin the CommunitySlackJoinSlackto get help, help others, provide feedback, see amazing projects, participates in discussions, and collaborate with others from around the world.Stack OverflowNot a Slack fan? No problem. Head over toStackOverflow. Be sure to tag your post withnba-api.ContributingSeeContributing to the NBA_APIfor complete details.EndpointsA significant purpose of this package is to continuously map and analyze as many endpoints on NBA.com as possible. The documentation and analysis of the endpoints and parameters in this package are some of the most extensive information available. At the same time, NBA.com does not provide information regarding new, changed, or removed endpoints.If you find a new, changed, or deprecated endpoint, open aGitHub IssueBugsEncounter a bug,report a bug.License & Terms of UseAPI Client PackageThenba_apipackage is Open Source with anMIT License.NBA.comNBA.com has aTerms of Useregarding the use of the NBA’s digital platforms.
Package: nba-apiv3
nba_apiv3An API Client package to access the APIs version 3 for NBA.comnba_apiv3is an API Client forwww.nba.com. This package intends to make the APIs ofNBA.comeasily accessible and provide extensive documentation about them.nba_apiv3is forknba_apipackageand contains only endpoints that work with API version 3.nba_apiv3contains two endpoints, which not work innba_api:BoxScoreDefensiveV2BoxScoreMatchupsV3Getting Startednba_apiv3requires Python 3.7+ along with therequestsandnumpypackages. Whilepandasis not required, it is required to work with Pandas DataFrames.pipinstallnba_apiv3NBAfromnba_apiv3.stats.endpointsimportboxscorematchupsv3matchups=boxscorematchupsv3.BoxScoreMatchupsV3(game_id='0021700807')## players statsmatchups.get_data_frames()[0]DocumentationEndpointsContributingSeeContributing to the NBA_APIV3for complete details.BugsEncounter a bug,report a bug.License & Terms of UseAPI Client PackageThenba_apiv3package is Open Source with anMIT License.NBA.comNBA.com has aTerms of Useregarding the use of the NBA’s digital platforms.
Package: nbabattle
Generate NBA battle Picture GeneratorA lightweight python library to generate two nba team battle image.Installation$pipinstallnbabattleUsagefromnbabattleimportNbaBattlePhoto=NbaBattle('GSW','DET')battle=Photo.create_battle_image()# Show an imagebattle.show()# Save to the folderbattle.save("generate/gsw-det.png")NoticePlease use the team's abbreviation as the input.
Package: nback
nback=====Backups files and databases (MySQL, PostgreSQL, MongoDB) to AWS S3version 0.1.16 2014-11-15author: nandersson900@gmail.comFeatures--------- backups any given files/directories- uses mysqldump to take complete SQL dump of all mysql dbs- uses pg_dump to take complete SQL dump of all postgresql dbs- uses mongodump to take complete BSON dump of all mongodb dbs- saves backups for 7 days back and the first of every month indefinitely- choose between gzip and bzip2 compression algorithms for fastest orsmallest possible footprint- notifies any given email address every time backup is done- use bash shell for prettier log coloringSystem requirements-------------------It might work with other versions of the software given below,however this is what I've tested with.- *nix-like environment- bzip2 or gzip- Python >= 2.7.3 (only dep. python-boto)- MySQL >= 5.0 (optional)- PostgreSQL >= 9.3 (optional)- MongoDB >= 2.0.4 (optional)Getting started---------------- install python-boto if you haven't already (on Debian/Ubuntu, $ sudoapt-get install python-boto)- edit settings.py with your settings/credentials- add a job to your crontab, and set it to run as often as you want backups
Package: nba-data-miner
nba_data_minerA package to help with searching and comparing players and teams in the NBA.Installation$pipinstallnba_data_minerUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensenba_data_minerwas created by Francis Baring. It is licensed under the terms of the MIT license.Creditsnba_data_minerwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: nba-data-scraper
NBA Data ScraperIntroductionNBA Data Scraper is a Python library designed to scrape game shots data from a specific basketball-related website (Basketball Reference). It is structured to handle requests efficiently and respectfully using rate limiting to avoid overloading the server (bot traffic). On that note, all use of data acquired should respect the website'sterms of use.📂 Structurenba-data-scraper/
│
├── nba-data-scraper/
│ ├── init.py
│ ├── utils/
│ │ ├── init.py
│ │ └── _logger.py
│ ├── _abstract.py
│ ├── _data_scraper.py
│ └── scraper.py
│
├── tests/
│ ├── init.py
│ └── ...
│
├── docs/
│ └── ...
│
├── examples/
│ └── ...
│
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── setup.py🔧 Installationpip install nba-data-scraperUsageScrape Player Datafromnba_data_scraperimportNBAScrapernba_scraper=NBAScraper()# Scrapes player data for the letter 'a'player_data=nba_scraper.scrape_player_data('a')Scrape Schedule Data# Scrapes games played in a specific year and monthschedule_data=nba_scraper.scrape_schedule_data(year='2023',month='january')# Scrapes games played in given list of years and monthsschedule_data=nba_scraper.scrape_schedule_data(year=['2022','2023'],month=['january','february'])# Scrapes all games played given a start and end yearschedule_data=nba_scraper.scrape_all_schedule_data(start_year=2020,end_year=2021)Scrape Game Data# Scrapes Game Data for games played within a schedule## First: Scrape for scheduleschedule_data=nba_scraper.scrape_schedule_data(year='2023',month='january')## Second: use return DataFrame as input to scrape_game_data methodgame_data=nba_scraper.scrape_game_data(schedule_df=schedule_data)LicenseSee theLICENSEfile for details.