Saravanan666/pypi-org huggingface dataset:
Package: auguste
augusteA python module for minimum-strain symmetrization of Bravais lattices.  Symmetrization produces a strain tensor, which is reduced to a scalar quantity.  This quantifies the 'distance' of a lattice from each Bravais class.  The distance can be used for classification of Bravais lattices, and visualization in an abstract Bravais space.Installation:To install the module with pip (recommended):pip install --user augusteTo install directly from the git repository:pip install --user git+https://github.com/pmla/augusteTo do a manual build and installation:python3 setup.py build
python3 setup.py install --userUsage:To import the module:>>> import augusteTo symmetrize a lattice (with rows as unit vectors):>>> cell = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
>>> distance, symmetrized = auguste.symmetrize_lattice(cell, "primitive tetragonal")
>>> distance
0.4714045207910317
>>> symmetrized
array([[-0.37037037,  0.74074074,  0.74074074],
       [ 0.74074074, -0.37037037,  0.74074074],
       [ 0.74074074,  0.74074074, -0.37037037]])Supported lattice names are stored in the module variables:>>> auguste.names
('primitive triclinic', 'primitive monoclinic', 'base-centred monoclinic', 'primitive orthorhombic', 'base-centred orthorhombic', 'body-centred orthorhombic', 'face-centred orthorhombic', 'primitive tetragonal', 'body-centred tetragonal', 'primitive rhombohedral', 'primitive hexagonal', 'primitive cubic', 'body-centred cubic', 'face-centred cubic')
>>> auguste.pearson
('aP', 'mP', 'mS', 'oP', 'oS', 'oF', 'oI', 'tP', 'tI', 'hP', 'hR', 'cP', 'cF', 'cI')To calculate the symmetrization distances from all Bravais types:>>> auguste.calculate_vector(cell)
array([0.00000000e+00, 4.28372991e-01, 5.24426159e-16, 4.71404521e-01,
       4.28372991e-01, 3.80565279e-16, 1.92296269e-16, 4.71404521e-01,
       4.07921987e-16, 1.92296269e-16, 4.60895951e-01, 4.71404521e-01,
       2.61971659e-01, 0.00000000e+00])InformationIf you use auguste in a publication, please cite:Peter M. Larsen, Edward L. Pang, Pablo A. Parrilo, and Karsten W. Jacobsen; "Minimum-strain symmetrization of Bravais lattices"; Phys. Rev. Research 2, 013077Article available athttps://doi.org/10.1103/PhysRevResearch.2.013077Preprint available athttps://arxiv.org/abs/1910.03433.
Package: augustine-text
augustineSimple text generation with document training.Table of ContentsAbout The ProjectGetting StartedUsageAdditional DocumentationRoadmapContributingLicenseContactAbout The ProjectAugustine is a fairly simple text generation library. The name is taken from St. Augustine of Hippo, the most prolifict of early church writers.No hard dependencies100% pure pythonSimpleWorks out of the boxExtensible to mimic specific documentsGetting StartedTo get a local copy up and running follow these simple steps.Installing with pippipinstallaugustine_textFor information about cloning and dev setup see:ContributingUsageHere is an example showing basic usage.fromaugustine_text.sample_textimportwords# 75K words of procedurally generated text# This is about the length of novel.text=words(75000)text_length=len(text)print(text[:100])ContributingContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Add tests, we aim for 100% test coverageUsing CoverageCommit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestCloning / Development setupClone the repo and installgitclonehttps://github.com/kajuberdut/augustine-text.gitcdaugustine
pipenvinstall--devRun testspipenvshell
wardFor more about pipenv see:Pipenv GithubLicenseDistributed under the BSD Two-clause License. SeeLICENSEfor more information.ContactPatrick Shechet -patrick.shechet@gmail.comProject Link:https://github.com/kajuberdut/augustine
Package: augwrap
AugWrapInstallation$pipinstallaugwrapBasic UsageBase dataset을 선택합니다. Pytorch 기반 데이터셋을 만들려면TorchBaseDataset을 사용합니다. Tensorflow 기반 데이터셋을 만들려면TFBaseDataset을 사용합니다.importaugwrapasawdataset=aw.data.TorchBaseDataset(images,labels,classes)augwrap.data안의 모듈을 이용하여 학습 전 데이터를 처리합니다.dataset=aw.data.LoadImages(dataset)dataset=aw.data.ResizeImages(dataset,(256,256))dataset=aw.data.OneHotLabels(dataset)데이터 로더를 생성하여 학습에 사용합니다.fromtorch.utils.dataimportDataLoaderdata_loader=DataLoader(dataset,batch_size=16,shuffle=False,num_workers=4)Augmentations잘 알려진 어그멘테이션 도구인Albumentations을 활용할 수 있도록 만들었습니다.augwrap.data.Augmentations의 생성자 인자로 base dataset에서 파생된 객체와 Albumentations 객체를 받습니다.Albumentations와 함께 사용할 수 있는 어그멘테이션 모듈을augwrap.augmentations에 추가했습니다.importalbumentationsasAimportaugwrapasawaugmentations=A.Compose([A.RandomRotate90(p=1),A.GridDistortion(p=0.8),A.GaussNoise(p=0.75),aw.augmentations.CutMix(dataset,p=0.8),])dataset=aw.data.Augmentations(dataset,augmentations)
Package: au-ipnd
Australian IPND (Integrated Public Number Database) ClientThis is a python 3 port ofxrobau/ipndMany thanks to this author!~Licenced under the AGPLv3.You can install this module via pip:pip install au-ipndExample Codefrom ipnd import record, IPND

person = record.Person()
person.set_name("Herp L. Derpinson", "Mr")
person.set_contactnum("0402000000")

business = record.Business()
business.set_name("Extremely Long Name Pty Ltd, Trading as Stupidly Long Name Incorporated")
business.set_contactnum("0402000000")

address = record.HouseAddress()
address.set_street_number("1")
address.set_street_name("FAKE", "ST")
address.set_locality("0200", "ANU", "ACT")

# This is the File Sequence Number
i = IPND(source="XXXXX", seq=2)

nums = (("0749700000", person), ("0749700001", business))

for num, entity in nums:
    t = record.Transaction()

    t.add_entry(record.CSPCode("999"))
    t.add_entry(record.DPCode("YYYYYY"))

    t.add_entry(record.PublicNumber(num))
    t.add_entry(record.UsageCode(entity))
    t.add_entry(record.ServiceStatusCode("C"))
    t.add_entry(record.PendingFlag("N"))
    t.add_entry(record.CancelPendingFlag("N"))
    t.add_entry(record.CustomerName(entity))
    t.add_entry(record.FindingName(entity))
    t.add_entry(record.ServiceAddress(address))
    t.add_entry(record.DirectoryAddress(address))

    t.add_entry(record.ListCode("UL"))
    t.add_entry(record.CustomerContact(entity))

    i.add_transaction(t)

output = i.generate_to_string()

print(output)
Package: aukit
aukitaudio toolkit: 语音和频谱处理的工具箱。安装pip install -U aukit注意可能需另外安装的依赖包：tensorflow, pyaudio, sounddevice。tensorflow<=1.13.1pyaudio暂不支持python37以上版本直接pip安装，需要下载whl文件安装，下载路径：https://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudiosounddevice依赖pyaudio。aukit的默认音频采样率为16k。版本v1.4.6audio_cli命令行，播放音频，去除背景噪声，音频格式转换。
支持递归处理文件夹内的全部音频。命令行说明用位置参数来控制。名称说明inpath：输入音频路径或目录。outpath：输出音频路径或目录，如果为目录，则输出的子目录按照inpath的子目录格式输出。sr：音频采样率，默认16000或自动识别采样率。in_format：输入音频格式，主要用以限制为指定后缀名的文件，如果不设置，则处理目录的全部文件。out_format：输出音频格式，主要用以音频格式转换，设置输出音频的后缀名。中括号【[]】里面的是可选参数。工具auplay: 播放音频auplay inpath [sr] [in_format]aunoise: 语音降噪aunoise inpath outpath [in_format]auformat: 音频格式转换auformat inpath outpath out_format [in_format]audio_changer变声器，变高低音，变语速，变萝莉音，回声。
基于librosa的变声。audio_editor语音编辑，切分音频，去除语音中的较长静音，去除语音首尾静音，设置采样率，设置通道数。
音频格式相互转换，例如wav格式转为mp3格式。
切分音频，去除静音，去除首尾静音输入输出都支持wav格式。
语音编辑功能基于pydub的方法，增加了数据格式支持。audio_griffinlimgriffinlim声码器，线性频谱转语音，梅尔频谱转语音，TensorFlow版本转语音，梅尔频谱和线性频谱相互转换。audio_io语音IO，语音保存、读取，支持wav和mp3格式，语音形式转换（np.array,bytes,io.BytesIO），支持【.】操作符的字典。audio_noise_remover语音降噪，降低环境噪声。audio_normalizer语音正则化，去除音量低的音频段（去除静音），调节音量。
语音正则化方法基于VAD的方法。audio_player语音播放，传入文件名播放，播放wave数据，播放bytes数据。audio_spectrogram语音频谱，语音转线性频谱，语音转梅尔频谱。audio_tuner语音调整，调整语速，调整音高。audio_worldworld声码器，提取语音的基频、频谱包络和非周期信号，频谱转为语音。调音高，调机器人音。历史版本v1.4.6变声采用librosa包的stft。v1.4.5logger改为__file__。v1.4.4Dict2Obj方法增加parse方法。v1.4.3修正Dict2Obj的get方法达不到预期的bug。修正world声码器变声静音报错的bug。v1.4.1修正安装依赖报错的bugs。set系列改为convert系列，如set_sample_rate改为convert_sample_rate。Dict2Obj用dotmap模块的DotMap代替。v1.4.0增加音频格式转换方法。命令行批量处理语音，支持音频播放、语音降噪、音频格式转换。增加命令行工具：auplay, aunoise, auformat。调整日志输出方法。v1.3.12减少不必要的依赖，最低依赖只保留pydub,  scipy, numpy, librosa。
Package: aukpy
aukpyA reimplementation ofaukin Python.Note that this isnota direct port: aukpy does not useawkto access data. Instead it provides a similar API, but uses sqlite to index and query the dataset.For detailed usage,see the full documentationUsageTo load and filter a set of eBird observations stored inobservations.txt:from aukpy import db, queries
db_conn = db.build_db_pandas('observations.txt')
df = queries.species('Sturnus vulgaris').run(db_conn)
Package: aulas-publicacao-pacote
aulas_publicacao_pacoteaulas_publicacao_pacoteÃ© um simples pacote para exibir nÃºmeros de 1 a 9 em Python.DependÃªnciasPython 3.6ou posteriorPacotenumpyComeÃ§ando o usoVocÃª vai precisar instalar o pacoteaulas_publicacao_pacote, para isso basta executar:pip install aulas_publicacao_pacoteFunÃ§Ã£oexibir ()- Mostra nÃºmeros de 1 a 9
Package: aul-compiler
IntroductionAnnoyingly Uncodeable Language (AUL), officially The Annoyingly Uncodeable Language Project, is anesoteric programming languagedeveloped byAndrew Chenthat aims to be so hard to code that it is annoying. The AUL compiler is freely availableon GitHubwhere it is licensed under theGNU General Public License v3.0. Our tutorial assumes that you have a decent experience in computer science, specifically in the Python language, so if you don't, try and learn a programming language like Python first. Without further ado, let's dive into the tutorial!Installing AUL
Package: aumbry
Aumbry is general purpose library for handling configuration within your
Python applications. The project was born from constantly needing a simple
interface for configuration models that came from multiple data sources.Behind the scenes, Aumbry uses Alchemize to handle the conversion of the
configuration data into application specific data models for your project.Data Sources supported:FileConsulEtcd2AWS Parameter StoreConfiguration Formats Supported:YamlJsonInstallation# Install base packagepipinstallaumbry# Install consul supportpipinstallaumbry['consul']# Install etcd2 supportpipinstallaumbry['etcd2']# Install yaml supportpipinstallaumbry['yaml']# Install parameter store dependenciespipinstallaumbry['param_store']# Installing multiple dependenciespipinstallaumbry['etcd2','yaml']
Package: aunly-bbot
BBot for Ariadne基于Graia-Ariadne搭建的高效、高性能哔哩哔哩推送QQ机器人BBot
B，是 26 个英文字母里的第二个，可意为我个人的第二个机器人
B，也代表 BiliBili，这个 Bot 将专注于哔哩哔哩的推送等服务🤖 BBot 现在能干什么订阅 UP 主废话推送直播（开播及下播）废话推送动态废话视频链接解析🌟 特色大量使用并发gRPC 接口，推送效率远超使用 REST Api 的哔哩哔哩机器人且目前未见有风控（-421）风险使用登录和非登录两种方案，对于财大气粗的用户可以登录后再次提升效率不建议使用动态使用 Web 端截图，虽然会吃那么点性能，但这都是值得的可自由配置是否在群内 @全体成员、对于直播和动态的分别控制等可针对不同群聊对订阅的 UP 主进行昵称替换可限制每个群可订阅的最大 UP 主数量视频解析可使用 OpenAI ChatGPT 进行内容总结内存少的话可以使用 Lite 版来减少部分依赖的占用🛠️ 使用pip 一键安装>pipinstallaunly-bbot>bbotUsage:bbot[OPTIONS]COMMAND[ARGS]...Options:--helpShowthismessageandexit.Commands:configBBot配置向导run运行BBot>bbotrunBBot 使用文档Docker 部署请查看这里📝 TODO增加群内配置功能增加菜单（/help 指令）使用数据库存储推送记录增加动态自动点赞功能支持 up 全名搜索增加可选的动态推送样式（App 样式）定时刷新 token，防止过期针对 Windows 和 Linux 平台，增加 Release 打包版本增加非登录式的推送更新逻辑更换 BiliBili 请求库为更成熟的BiliReq可能会增加不需要浏览器的动态截图获取方式增加 Docker 部署方案自定义动态页字体上传至 PyPI 并支持 CLI 方式启动迁移至 PDM增加针对视频和专栏解析的内容总结，支持 ChatGPT 和 WordCloud 两种模式字体修改为网络获取，缩小程序体积增加 Lite 版本，不包含浏览器等依赖增加 Web 端管理界面增加简单的推送数据分析及报告丰富管理员指令增加订阅组（同时订阅多个设定好的 up，如和谐有爱五人组...）可能会增加其他平台的推送More...🙏 感谢HarukaBot学习对象bilibili-API-collect易姐收集的各种 BiliBili Api 及其提供的 gRPC Api 调用方案ABot-Graia永远怀念最好的 ABot 🙏Well404为本项目编写文档以及部署教程视频八萬项目 Logo 画师⏳ Star 趋势
Package: aunnl
AUNNL is another unnecessary neural network library for Python 3.x. It is intended to help create and train basic neural networks very easily.Getting StartedInstallationIt is recommended you install via pip for Python 3:pip install aunnlAfter this, you can import it into your python program with:import aunnlBasic ExampleThe following example trains a neural network to classify handwritten digits from the MNIST dataset. The dataset is loaded using themnist_webmodule, which is not packaged with AUNNL. Download and install it with the commandpip install mnist_web.import aunnl

from mnist_web import mnist
data, labels, _, _ = mnist(path="dataset")

model = aunnl.NeuralNetwork([784, 256, 10], ["relu", "sigmoid"])

epochs, lr, batch_size = 1, 0.1, 64

model.fit(data, labels, epochs, batch_size, lr, aunnl.losses.MSE)
model.save("mnist.aunn")In the above example, a neural network with a hidden layer of 256 neurons is trained - its activation being ReLU and the output layer activation being sigmoid. The model, which is anaunnl.NeuralNetworkobject, is then saved to the filemnist.aunn. The model can be loaded from the file withaunnl.loadModel('mnist.aunn').To use the model, simply pass the image in the form of a flat numpy array (denoted here asimg_arr) to the model withmodel.feedForward(img_arr). ThefeedForwardfunction returns a list of the values outputted by the model.
Package: au-notebook
au_notebookDescriptionThis module is intended for use with Archives Unleashed Cloud derivative data and theArchives Unleashed Cloud Notebooks.Installationau_notebook.pyis a single-file Python module that you can drop into your project as needed or you can install globally with:git clone https://github.com/archivesunleashed/au_notebook.gitcd au_notebookpython setup.py installor:pip install au_notebookTest suitepython setup.py testContributingPlease seecontributing guidelinesfor details.Bug reportsPull requestsare welcome onau_notebookLicenseApache 2.0AcknowledgmentsThis work is primarily supported by theAndrew W. Mellon Foundation. Any opinions, findings, and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the sponsors.
Package: auntminnieupdate
auntminnnie-updatethis package for retirve auntminnie recent news, webinars and radiology conferencehow it work?his package will scarape inauntminnnieto get latest news, webinars and radiology conference.
and then produce output of excel & csv file or JSON thats ready to use in your web or mobile apps.there are three features in this package:News()Conferences()webinar()in some of these function there are serveral function:.info() for info how to use.test() for testing connection the web.singlepage() for scrap data with spesific page. result json, excel & csv file in resultfile. deafult is scrap page 1.allpage() for scrap data as many pages availabel. result json, excel & csv file per page & all page in file resultfileall in file resultfile with file name according to the features usedConferences() only has .singlepage()i.e auntminnieupdate.News(2) for access page two in news.other package that need to be instaledBeautifulSoup4requestspandas(openpyxl)
Package: auntnee
# AuntneeTODO:support encrypted json (Aegiscompatible)appimage releasedesign an ui# About
This program is an TOTP Authenticator for terminal, initialy the json with all 2FA codes is generated from an apk calledAegisAuthenticator, so this program reads the json file generated with the apk and show you 2FA code from the console.
Now support creation of empty template and is not demanding theAegisjson.# Supportcreating empty templateusing existing json fromAegisunencryptedadd new issuer to the jsondelete provided issuer from the jsonchange path of json built-in# Instalation### Fisrt startWhen you first runauntnee
Package: auooo-sample
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written using ReStructured Text. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: auptitcafe
❔ AboutFinally a Python package to makegetting Au p'it café's menus a piece of (cheese) cake.😅With thispackageyou can:✔️ Getmenus✔️ Gettakeways... and get them as list of objects orcsv.👉 See it live onKaggle👈🔖 Social networksOfficial websiteTripAdvisorInstagramFacebookGoogle Maps🤓 Nerd resources🐍pypi😋 Au p'tit café (pypi package intro on Kaggle) 🚀🚀 QuickstartFor the impatients, here is a quick and ready to use code snippet:# Install the package!pipinstallauptitcafe--upgrade# Make some importsfromauptitcafe.menusimportMenusimportpandasaspd# Create the main utility instancemenu_instance=Menus()# Dump menus as a csv filemenus='menus.csv'menu_instance.to_csv(menus)# Load menus in a panda dataframedf=pd.read_csv(menus)# Diplay dataframedfBuild itpoetryshow--treepoetrybuild
poetryinstalltestpoetryrunpytestPublishpoetryconfigpypi-token.pypi$PYPI_TOKENpoetrypublish
Package: aupyom
# aupyom[![Build Status](https://travis-ci.org/pierre-rouanet/aupyom.svg?branch=master)](https://travis-ci.org/pierre-rouanet/aupyom)Real-time Audio time-scale and pitch modification Python library.# DocumentationAupyom is the pure-python library designed to allow for quick and easy sounds mixing. It has been designed to let you shift the pitch and change the time-scale of sounds in real time.As aupyom API is really simple, you only need to know a few methods to start playing, the documentation is given as few demonstration notebooks:[Playing and mixing multiple sounds](./examples/Playingandmixingmultiplesounds.ipynb)[Live modification of the pitch and time-scale of sounds](./examples/Livemodificationofthepitchandtime-scaleofsounds.ipynb)# InstallationThe last stable release is available on PyPI. It can be install via:`bash pip install aupyom `You can also install it from source:`bash python setup.py install `## DependenciesAupyom works with Python >= 2.7 and Python>=3.4.Aupyom requires different libraries:
* [numpy](http://www.numpy.org): for the low-level sound processing
* [librosa](https://github.com/bmcfee/librosa): for higher-level sound processing and IO
* [sounddevice](http://python-sounddevice.readthedocs.org/): a python bindings for the [PortAudio](http://www.portaudio.com) library - used to play sounds# Integration with Poppy robotsAupyom has been originally designed in the context of the [Poppy-project](https://www.poppy-project.org) aiming at developing open-source and easy-to-use robots. They are notably used in educational context for teaching computer science and discover IoT and robotics.[Dedicated demonstration notebooks]() have been designed showing how the movement of a robot can be used to lively modify sound pitch and time-scale![![Vimeo Preview](./doc/vimeo-preview.jpg)](https://vimeo.com/107599850)Thanks to that, aupyom has also been connected to [Snap!](http://snap.berkeley.edu) a variant of the famous visual programming language allowing the design of interface for intuitively modifying and mixing of sounds.![IMAGE] (TODO IMAGE)
Package: aur
aur is a Python library that makes it easy to access and parse data
from theArch User Repository API.Usage>>>yturl=aur.info('yturl')>>>yturl.description'YouTube videos on the command line'>>>poco=aur.search('poco')>>>poco[<Package:poco>,<Package:flopoco>,<Package:libpoco-basic>]DocumentationDocumentation is available onReadTheDocs.InstallationFrom pip:pip install aurManually:python setup.py installDocumentationpydoc aurTestingpython setup.py test
Package: aur-deploy
AUR DeployA script that allows to easily and effectively build and publish your python packages up to PyPI and AURFeaturesCompare between local and deployed versionsUpdating AUR PKGBUILD on setup.py package's version changeUploading to PyPI and AUR repositoriesCustomizable and configureableInstallationFrom AUR install system package:python-aur-deployFrom PyPI:$ pip install aur-deployUsageRun like:$ aur-deploy --help
Package: aurem
AuReM pickerAutoRegressiveModels for seismic phase pickingVersion:1.1.0Author:Matteo BagagliDate:09/2021IntroductionThis package provides a variety ofAUtoREgressiveModels seismic pickers.
It contains the AIC and REC picking algorithm, and theirCF calculationsare fullywritten in Cfor faster run times "under the hood".
But, you'll just need to use python ... :)See references for more details.SetupCurrently there are 2 different ways to install the package.DeveloperIf you prefer you can create a python environment with conda or pyenv, and then clone the project$ conda create -n aurem python=3.7
$ conda activate aurem
$ cd where/the/code/is
$ pip install .
# Install pytest and check everything works fine
$ pip install pytest ; pytestJupyter notebook example and tutorials can be found in the project'sbooksfolder.
Library dependencies are stored inrequirements.txtContributors are welcome as well as any suggestion for the project improvement!PyPIFrom version1.1.0the package is hosted to PyPI repository. Therefore, once the environment is created, you would just need to type:$ conda activate aurem
$ pip install auremand be ready to go ...ReferencesAICMaeda 1985: A method for reading and checking phase times in autoprocessing system of seismic wave dataRECMadarshahian at al. 2020: Bayesian Estimation of Acoustic Emission Arrival Times for Source Localization
Package: aureus
AureusAureus is a lightweight web application framework. It supports
Model-View-Controller (MVC) architectural pattern. It is designed to
make getting started quick and easy, with the ability to scale up to
complex applications.InstallingInstall and update usingpip:pipinstall-UaureusA Simple ExamplefromaureusimportAUREUSapp=AUREUS()@app.route('/',methods=['GET'])defhello():return'<h1>Hello Aureus!</h1>'app.run()$python3main.py*Runningonhttp://127.0.0.1:8080/(PressCTRL+Ctoquit)A MVC pattern ExamplefromaureusimportAUREUSfromaureus.viewimportView,ControllerclassBaseView(View):methods=['GET, POST']defpost(self,request,*args,**options):passdefget(self,request,*args,**options):passdefdispatch_request(self,request,*args,**options):methods_meta={'GET':self.get,'POST':self.post,}ifrequest.methodinmethods_meta:returnmethods_meta[request.method](request,*args,**options)else:return'<h1>Unknown or unsupported require method</h1>'classHello(BaseView):defget(self,request):return'<h1>Hello, Aureus!</h1>'app=AUREUS()aureus_url_map=[{'url':'/','view':Hello,'endpoint':'hello'},]hello_controller=Controller('hello',aureus_url_map)app.load_controller(hello_controller)app.run()$python3main.py*Runningonhttp://127.0.0.1:8080/(PressCTRL+Ctoquit)LinksLicense:BSDReleases:https://pypi.org/project/Aureus/Code:https://github.com/YouwangDeng/AureusIssue tracker:https://github.com/YouwangDeng/Aureus/issues
Package: aur-info
AUR-INFOHelper program to show package info for a package from Arch User Repository.Installation```pip install aur-info```UsageWhile inside the interpreter or writing a script:import aur_info as ai
    pkginfo = ai.get_info('package')... which returns a result of the form:pkginfo = {'name': 'Package',
 'maintainer': 'goofy',
 'last_packager': 'goofy',
 'votes': 1,
 'popularity': 0.000000,
 'created': '2016-01-07 07:50',
 'description': 'Package Description.',
 'git_url': 'https://aur.archlinux.org/packagerepo.git',
 'updated': '2020-01-05 11:33',
 'version': '3.1.1-3',
 'submitted': '2016-01-07 07:50'}
Package: aurlcutter
aurlcutterSimple asynchronous python url shortener api wrapperSource code:https://github.com/mesiriak/aurlcutterInstallation guide:pip install aurlcutterFor watcing full list of services:fromaurlcutterimportCuttercutter_instance=Cutter()print(cutter_instance.cutters)>>>["tinyurl","isgd","dagd",...]Usage example:fromaurlcutterimportCuttercutter_instance=Cutter()your_link="www.google.com"# you can choose api what you need by yourselfcutted_link=cutter_instance.tinyurl.cut(your_link)