Saravanan666/pypi-org huggingface dataset:
Package: bart
THIS IS RESEARCH SOFTWARE THAT DOESN’T REALLY WORK YET. USE AT YOUR OWN
RISKBARTis a set of code for modeling the light curves of exoplanet transits.The core light curve routines are written in Fortran and wrapped in Python.InstallationFirst, clone:git clone https://github.com/dfm/bart.git
cd bartNext, set up yourvirtual environment:virtualenv venv --distribute
source venv/bin/activateThen, install the prerequisites (note:on Mac OS, it’s worth setting the
environment variableCC=clang):pip install 'numpy>=1.6.2'
pip install -r requirements.txtFinally, either build the Fortran and C extensions:python setup.py interface
python setup.py build_ext --inplaceUsageTake a look atthis example.
Package: bart-api-ulloa
BART API Wrapper for Python 3+ (w/ JSON)Bart API Documentation:https://api.bart.gov/docs/overview/index.aspxEvery amenity from the Bay Area Rapid Transit (BART) API is implemented in some way.
The methods sections below shows the available methods.Each API method will return relevant data, usually with new line characters so that when
the string is printed, well-formed information is printed. The second branch of this
project, BartApiForPrintingInfo, will always print the data.One exception: the help class methods, which both return data and print it to screen.Use the help class methods to see what the syntax is for invoking a bart command.For example, the general help function:Inputbart = Bart()   # note that key defaults to BART API's universal key
bart.help()OutputCommands: help, routes, routeinfo 
route_help(), routes(), route_info()

Commands: bsa, count, elev, help 
bsa(), train_count(), elev(), elev_help()

Commands: etd, help 
etd(), etd_help()

Commands: help, stns, stninfo 
stn_help(), stninfo(), stnaccess(), stns()

Commands: arrive, depart, fare, help, holiday, routesched, scheds, special, stnsched 
arrive(), depart(), fare(), sched_help(), holiday(), routesched(), scheds(), special(), stnsched()Also, there is thorough documentation. The following...help(classname.methodname)
   help(classname)...will give you all of the necessary details about a method or class.Methods----- Bart API -----
bart = Bart(key)  # key is optional, defaults to universal BART API key


Advisories
-----------
bsa(orig)
train_count()
elev()
elev_help()


Real-Time Estimates
-------------------
etd(orig, plat, direction)
etd_help()


Route Information
-----------------
routeinfo(route_num, sched_num, date)
routes(sched_num, date)
route_help()


Schedule Information
--------------------
arrive(orig, dest time, b, a)
depart(orig, dest, time, b, a)
fare(orig, dest, date, sched)
holiday()
routesched(route, date, time, sched)
scheds()
special()
stnsched(orig, date)
sched_help()


Station Information
-------------------
stn_help()
stninfo(orig)
stnaccess(orig)
stns()


Version Information
-------------------
version()InstallingThere's a package on PyPI.pip3 install bart-api-ulloaJust make sure you have python3 and the requests library (don't even need an API key,
since BART has a universal key and it's built into this wrapper).
Package: bartbroere-streamlit
Welcome to Streamlit 👋A faster way to build and share data apps.Streamlit lets you turn data scripts into shareable web apps in minutes, not weeks. It’s all Python, open-source, and free! And once you’ve created an app you can use ourCommunity Cloud platformto deploy, manage, and share your app.InstallationOpen a terminal and run:$pipinstallstreamlit
$streamlithelloIf this opens our sweetStreamlit Helloapp in your browser, you're all set! If not, head over toour docsfor specific installs.The app features a bunch of examples of what you can do with Streamlit. Jump to thequickstartsection to understand how that all works.QuickstartA little exampleCreate a new filestreamlit_app.pywith the following code:importstreamlitasstx=st.slider("Select a value")st.write(x,"squared is",x*x)Now run it to open the app!$ streamlit run streamlit_app.pyGive me more!Streamlit comes in witha ton of additional powerful elementsto spice up your data apps and delight your viewers. Some examples:Input widgetsDataframesChartsLayoutMulti-page appsFunOur vibrant creators community also extends Streamlit capabilities using  🧩Streamlit Components.Get inspiredThere's so much you can build with Streamlit:🧬Science & technology apps💬NLP & language apps👀Computer vision apps🏦Finance & business apps🗺Geography & society appsand more!Check outour gallery!🎈Community CloudDeploy, manage and share your apps for free using ourCommunity Cloud! Sign-uphere.ResourcesStreamlitdocs,community forumandblog.Extend Streamlit's capabilities by installing or creating your ownStreamlit Components.Help others find and play with your app by using the Streamlit GitHub badge in your repository:[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](URL_TO_YOUR_APP)LicenseStreamlit is completely free and open-source and licensed under theApache 2.0license.
Package: bartender
Bartender is the beer-garden backend. It’s responsible for maintaining plugin health/status and running the actual plugins.Getting StartedPre-requisitesPython >= 2.7, < 3.9pipConnectivity to MongoDB Server  - (Tested on 3.4)Connectivity to Rabbitmq-Server - (Tested on 3.6)Get Up and RunningWhen installed from pip you can simply:$bartender-c/path/to/config.jsonThese are the minimum required steps once you have all the pre-requisites up and running.$gitclonehttps://github.com/beer-garden/bartender.git$cdbartender$pipinstall-rrequirements.txt$./bin/app.sh# Requires brew-view to be runningThere are several plugins that are loaded by default. You can view them in the brew-view UI.TestingRunning the tests:$maketestGenerating coverage:$makecoverageLinting:$makelintCreditsDoug Hellmann (@doughellmann) - Doug originally owned the bartender name but was willing to allow us to have it so that we didn’t have to change a lot of documentation. Thanks very much Doug!
Package: barter-auth
Barter authentication packageThis package allows you to authorize users through a shared redisInstall packagepipinstallbarter-authDefine env variablesREDIS_AUTH_URLdefault 'redis://localhost:6378/1' # depracatedREDIS_AUTH_HOSTdefault '127.0.0.1'REDIS_AUTH_PORTdefault 6379REDIS_AUTH_PASSWORDdefault NoneREDIS_AUTH_DBdefault 0REDIS_AUTH_ACCESS_PREFIXdefault = 'access'REDIS_AUTH_REFRESH_PREFIXdefault = 'refresh'REDIS_AUTH_TOTP_PREFIXdefault = 'totp'REDIS_AUTH_PROFILE_PREFIXdefault = 'profile'REDIS_AUTH_TOKEN_STORAGEdefault = 'headers'Use in view# in djangofromrest_framework.permissionsimportAllowAny,IsAuthenticatedfrombarter_auth.authimportApiTokenRedisAuthenticationclassSomeView(APIView):authentication_classes=[ApiTokenRedisAuthentication]permission_classes=[IsAuthenticated]# barter_auth BaseUser() is available in request.user  in DRF APIViewUse in AppConfig   for  request.extuser  and  request.profie# you can add request user or profile in apps django config  <app_name>.apps.pyclassBloggerConfig(AppConfig):name='apps.base'defready(self):HttpRequest.extuser=property(get_user)HttpRequest.profile=property(get_profile)defget_user(self):frombarter_auth.authimportget_token_from_cookies,get_token_from_headerfrombarter_auth.providersimportRedisAccessClientfromdjango.contrib.auth.modelsimportAnonymousUsertoken=get_token_from_header(self)ifnottoken:token=get_token_from_cookies(self)iftoken:token_service=RedisAccessClient()user=token_service.get_user(token)returnuserorAnonymousUser()returnAnonymousUser()defget_profile(self):frombarter_auth.modelsimportAnonymousProfileextuser=self.extuserifextuser.is_authenticated:try:profile=RedisProfileClient().get_model(key=self.headers.get('Profile'))except:profile=AnonymousProfile()ifprofile.user_idandprofile.user_id==extuser.id:# print(f'profile: {profile.id}')returnprofilereturnAnonymousProfile()# in settingsREST_FRAMEWORK={"DEFAULT_AUTHENTICATION_CLASSES":["barter_auth.auth.ApiTokenRedisAuthentication",],"EXCEPTION_HANDLER":'<your path>.api_exception_handler',# ...}# file with  api_exception_handlerfromrest_framework.responseimportResponsefromrest_framework.viewsimportexception_handler,set_rollbackfrombarter_auth.exceptionsimportBAuthAPIExceptiondefapi_exception_handler(exc,context):# Call REST framework's default exception handler first,# to get the standard error response.ifisinstance(exc,BAuthAPIException):headers={}ifgetattr(exc,'auth_header',None):headers['WWW-Authenticate']=exc.auth_headerifgetattr(exc,'wait',None):headers['Retry-After']='%d'%exc.waitifisinstance(exc.detail,(list,dict)):data=exc.detailelse:data={'detail':exc.detail}set_rollback()returnResponse(data,status=exc.status_code,headers=headers)response=exception_handler(exc,context)# Now add the HTTP status code to the response.ifresponseisnotNone:response.data['status_code']=response.status_codereturnresponse
Package: barter-fastapi-auth
Barter authentication packageThis package allows you to authorize users through a shared redisInstall packagepipinstallbater-fastapi-authDefine env variablesREDIS_AUTH_ACCESS_PREFIXdefault ''REDIS_AUTH_DBdefault 0REDIS_AUTH_HOSTdefault 127.0.0.1REDIS_AUTH_PORTdefault 6379REDIS_AUTH_PASSWORDdefault NoneREDIS_AUTH_TOKEN_STORAGEdefault 'headers' should be 'cookies'Define startapp and shutdown logicfromcontextlibimportasynccontextmanagerfromfastapiimportFastAPIfromsrc.bater_fastapi_authimporttoken_configfrombater_fastapi_auth.providersimportRedisSingleton@asynccontextmanagerasyncdeflifespan(app:FastAPI):RedisSingleton.init_redis(token_config.HOST,token_config.PORT,token_config.DB,token_config.PASSWORD)yieldawaitRedisSingleton().close()app=FastAPI(lifespan=lifespan)Use in viewfrombater_fastapi_authimportUserRedisAuth@app.get("/")asyncdefread_root(user=Depends(UserRedisAuth())):returnuser.to_dict()
Package: bart-extract-ga
Bart Extract GAExtract event views to Google AnalyticsInstall$ pip install bart-extract-gaModulesextract.processor.extract_yesterday: exports data from D-1extract.processor.extract_by_date_ranger: exports data from date specified in the parameters,start_dateandend_dateUsagefromextract.processorimportextract_yesterdayextract_yesterday(ga_viewId="ga:123456789",ga_credential="/tmp/credentials/GA_credentials.json",s3_path="s3://prd-lake-raw-bart",)Obss3_path: the path can be a local path or a path to an AWS S3 bucket
Package: bart-py
Behavioural Analysis involves the expressing the general
expectation of the state of the system while targeting a single or set of heuristics.
This is particularly helpful when there are large number of factors that can change
the behaviour of the system and testing all permutations of these input parameters
is impossible. In such a scenario an assertion of the final expectation can be
useful in managing performance and regression.The Behavioural Analysis and Regression Toolkit is based on TRAPpy. The primary goal is
to assert behaviours using the FTrace output from the kernel
Package: bart-simulator
Bart Simulator (CLI)Send event views to Google Analytics and Generator customers or productsInstall$ pip install bart-simulatorUsageusage: simulator [-h] [--loglevel LOGLEVEL] [--version]  ...

Send event views to Google Analytics and Generator customers or products data-
set (bart-recs CLI)

optional arguments:
  -h, --help           show this help message and exit
  --loglevel LOGLEVEL
  --version            show program's version number and exit

Commands:

    generation         Gera os data set simulados para as recomendações
    send-data-ga       Envia dados simulados para o Google AnalyticsGenerate DataSets# Generation customers csv
$ simulator generation customers -f csv

# Generation customers json
$ simulator generation customers -f json

# Generation products csv
$ simulator generation products -f csv

# Generation products json
$ simulator generation products -f jsonFull Optionsusage: simulator generation [-h] [--desc-path DESC_PATH] [--rows ROWS]
                            --format {csv,json} [{csv,json} ...]
                            {customers,products}

positional arguments:
  {customers,products}  Arquivo que sera gerado pelo processo

optional arguments:
  -h, --help            show this help message and exit
  --desc-path DESC_PATH, -d DESC_PATH
                        Pasta onde sera salvo os dataset gerados
  --rows ROWS, -r ROWS  Quantidades de Linhas geradas
  --format {csv,json} [{csv,json} ...], -f {csv,json} [{csv,json} ...]
                        Formato do arquivo de saida que sera salvo,pode ser
                        adiciona mais de um tipo ao mesmo tempoSend events to GA$ simulator send-data-ga pageview \
    -c https://github.com/cesarbruschetta/bart-recs/datasets/customers.csv \
    -p https://github.com/cesarbruschetta/bart-recs/datasets/products.csv \
    -i 10 \
    -gaId "ga:123456789"Full Optionsusage: simulator send-data-ga [-h] --customers CUSTOMERS --products PRODUCTS
                              [--interactions INTERACTIONS]
                              {pageview}

positional arguments:
  {pageview}            Tipo de evento que sera enviado ao GA

optional arguments:
  -h, --help            show this help message and exit
  --ga-track-id GA_TRACK_ID, -gaId GA_TRACK_ID
                        Id de acompanhamento para o sua conta do GA
  --customers CUSTOMERS, -c CUSTOMERS
                        Caminho para o dataset de customers, em csv
  --products PRODUCTS, -p PRODUCTS
                        Caminho para o dataset de products, em csv
  --interactions INTERACTIONS, -i INTERACTIONS
                        Quantidades de interações geradas
  --random-interactions
                        Gerar uma quantidades de interações randomicas
Package: baruda
Baruda"Baruda"("바루다") is a pure Korean word that means "fix or correct things"Namu Wiki: List of pure Korean wordsSetupSource installpython setup.py installPYPI installpip install barudaUsagejamofix(): Fix Hangul Jamo-level typos and synthesize broken Hangul syllables.ex. "ㅇㅣㄱㅓ 진짴ㅋㅋ ㄷㅚ욬ㅋ" -> "이거 진짜ㅋㅋㅋ 되요ㅋ"frombarudaimportjamofixs="ㅇㅣㄱㅓ 진짴ㅋㅋ ㄷㅚ욬ㅋ"print(jamofix(s))# >>> "이거 진짜ㅋㅋㅋ 되요ㅋ"ReferenceJDongian/jamo: Used for faster jamo decomposition.Version 0.1.3Released Apr 5 2020.Add special case of Jungseong handling. ('ㅜ', 'ㅠ', 'ㅗ')Version 0.1.0Released Mar 29 2020.The initial release.
Package: baruwa
Baruwa (swahili for letter or mail) is a web 2.0 MailScanner front-end.It provides an easy to use interface for managing a Anti Spam system
built on the best of breed open source packages. It is used to perform
operations such as managing configurations, quarantine management,
bayesian learning, management of approved and banned sender lists,
monitoring the health of the services and a lot more.Baruwa is implemented using web 2.0 features (AJAX) where deemed fit.
Baruwa has full support for i18n, enabling you to translate it into any
language of your choosing. Baruwa has already been translated into to
over25 languages.It includes reporting functionality with an easy to use query builder,
results can be displayed as message lists or graphed as colorful and
pretty interactive graphs.Baruwa includes full text search functionality that allows you to find
information very fast and easily. Advanced searching options available
in leading web search engines are supported in Baruwa.A Custom MailScanner module is provided for asynchronous logging of
messages to a PostgreSQL database with SQLite backup as well as
updating of the realtime search index.Baruwa is open source software, written in Python/Perl using the Pylons
Framework and uses a PostgreSQL backend, it is released under the GPLv3
and is available for free download.FeaturesAJAX support for most operationsUltra fast full text searchReporting with AJAX enabled query builderI18n support, allows use of multiple languagesSignature management / BrandingMail queue management and reportingMessage delivery/relay informationDKIM managementReporting graphsEmailed PDF reportsAudit trailsArchiving of old message logsSQLite backup prevents data loss when DB is unavailableMTA integrationMulti TenancyUser profile aware approved/banned sender managementIP / network addresses supported in approved/banned list managerSQL based MailScanner configuration managementSystem status informationIPv6 SupportAsynchronous MailScanner loggingImport and Export of User accounts and DomainsAD/Exchange integration to auto populate account and group informationEasy plug-in authentication to external authentication systemsAD/LDAP, POP3, IMAP, SMTP, RADIUS Authentication supportTools for housekeeping tasksEasy clustering of multiple serversWorks both with and without Javascript enabledInstallationBaruwa can be installed usingpip,easy_installor fromsource.Using pippip install -U baruwaUsing easy_installeasy_install -U baruwaUsing sourceDownload the latest version of Baruwa 2.0 fromhttp://pypi.python.org/pypi/baruwa/tar xjvf baruwa-0.0.0.tar.bz2
cd baruwa-0.0.0
python setup.py installThe last command must be executed as a privileged user if you are not currently
using a virtualenv.PackagesBinary packages for Ubuntu/Debian and RHEL/SL/CENTOS are available fromBaruwa EnterpriseDocumentationDocumentation is included in the docs directory of the tar ball and can also be
accessedonlineSupportCommunity supportYou can get free and friendly support from thelistCommercial supportPaid for support is available from theDeveloperor
othercompanies that support baruwa.
Package: barviz
BarVizA simple python module to visualize barycentric data.1/ Aboutbarvizis a minimalist python module, built on top ofplotly, allowing to display data in a barycentric space.1/ InstallationTo install barviz :pip install --upgrade barvizIdeally, barviz is designed to be used from aJupyter labnotebook.To create a complete virtual environment :python -m venv barviz-env
source barviz-env/bin/activate
pip install --upgrade barviz jupyterlab
jupyter lab2/ Using itFor the impatients :from barviz import Simplex

my_simplex = Simplex.build(5)
my_simlex.plot()3/ DocumentationUser manualis available inhtml formatand as aJupyter lab notebook.Reference API : In progres... - Hum, it will be necessary to wait a little more ;-)
Package: basacommons
This project joins some useful scripts designed to speed up project development.Main scripts are:configuration.pyThis script generates a configparser.configuration instance.Configuration load from different sources:AWS Systems manager parameter storeGiven that AWS CLI is configured and credentials loadedconfiguration=load_config('ssm',environment)SSM variable key must have format:
/$environment/$section
and value in json formatKeyValue/pre/ddbb{"host":"localhost","port":3306,"username":"root","password":"changeit"}Local fileconfiguration=load_config('file',environment,file_name)File expected in standard INI file structure.[ddbb]
host=localhost
port=3306
username=root
password=changeitEnvironment variables.configuration=load_config('env',environment,projectname)Variables should have the following format:
$projectname_$environemnt_$sectionnameMYPROJECT_PRO_DDBB_HOST=localhost
MYPROJECT_PRO_DDBB_PORT=3306
MYPROJECT_PRO_DDBB_USERNAME=root
MYPROJECT_PRO_DDBB_PASSWORD=changeitDatabase.pyDatabase connection and query executiondatabase=Database.ofConfiguration(configuration)To execute query, use methodDatabase.run_queryrecords=database.run_query('SELECT * FROM table WHERE mycolumn =%sAND myothercolumn = '%s',('value1', 'value2' ))Pagination is supported using classdatabase.Page:page_number=1page_size=50page=Database.Page(page_number,page_size)records=database.run_query('SELECT * FROM table WHERE mycolumn =%sAND myothercolumn = '%s', ('value1', 'value2' ), page)alerts.pyAlert sending, via AWS simple email service.
Package: basaka
# BerserkerBerserker (BERt chineSE woRd toKenizER) is a Chinese tokenizer built on top of Google's [BERT](https://github.com/google-research/bert) model.## Installation```pythonpip install basaka```## Usage```pythonimport berserkerberserker.load_model() # An one-off installationberserker.tokenize('姑姑想過過過兒過過的生活。') # ['姑姑', '想', '過', '過', '過兒', '過過', '的', '生活', '。']```## BenchmarkThe table below shows that Berserker achieved state-of-the-art F1 measure on the [SIGHAN 2005](http://sighan.cs.uchicago.edu/bakeoff2005/) [dataset](http://sighan.cs.uchicago.edu/bakeoff2005/data/icwb2-data.zip).The result below is trained with 15 epoches on each dataset with a batch size of 64.|                    | PKU      | CITYU    | MSR      | AS       ||--------------------|----------|----------|----------|----------|| Liu et al. (2016)  | **96.8** | --       | 97.3     | --       || Yang et al. (2017) | 96.3     | 96.9     | 97.5     | 95.7     || Zhou et al. (2017) | 96.0     | --       | 97.8     | --       || Cai et al. (2017)  | 95.8     | 95.6     | 97.1     | --       || Chen et al. (2017) | 94.3     | 95.6     | 96.0     | 94.6     || Wang and Xu (2017) | 96.5     | --       | 98.0     | --       || Ma et al. (2018)   | 96.1     | **97.2** | 98.1     | 96.2     ||--------------------|----------|----------|----------|----------|| Berserker          | 96.6     | 97.1     | **98.4** | **96.5** |Reference: [Ji Ma, Kuzman Ganchev, David Weiss - State-of-the-art Chinese Word Segmentation with Bi-LSTMs](https://arxiv.org/pdf/1808.06511.pdf)## LimitationSince Berserker ~~is muscular~~ is based on BERT, it has a large model size (~300MB) and run slowly on CPU. Berserker is just a proof of concept on what could be achieved with BERT.Currently the default model provided is trained with [SIGHAN 2005](http://sighan.cs.uchicago.edu/bakeoff2005/) [PKU dataset](http://sighan.cs.uchicago.edu/bakeoff2005/data/icwb2-data.zip). We plan to release more pretrained model in the future.## ArchitectureBerserker is fine-tuned over TPU with [pretrained Chinese BERT model](https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip). It is connected with a single dense layer which is applied to all tokens to produce a sequence of [0, 1] output, where 1 denote a split.## TrainingWe provided the source code for training under the `trainer` subdirectory. Feel free to contact me if you need any help reproducing the result.## Bonus Video[<img src="https://img.youtube.com/vi/H_xmyvABZnE/maxres1.jpg" alt="Yachae!! BERSERKER!!"/>](https://www.youtube.com/watch?v=H_xmyvABZnE)
Package: basal
name:basaldescription:memory storage structure for directoriestutorial:basal.JSON needs to be added to a folder:'''basal.JSON {"basilosauri": {"port": ""},"basal ganglia": {"path": ""},"PFC": {"path": ""}}'''## This sets up the basal climateimport basalbasal.start ()import basal.ganglia.list as basal_ganglia_listlist = basal_ganglia_list.start ()structure:archivessandbox:climate.py <- setup the basal dbopen.py <- uses basalmodules_pipbasalclimate.JSON <- tinydbmodules.pip.UTF8
Package: basata
BASATA is the Arabic word for simplicity. The purpose of the BASATA library is to simplify coding while providing profound insights. You can use BASATA to write code that is easy to read, understand, and maintain.The BASATA library is used to automate machine learning tasks. Building on the spirit of AutoML, BASATA is a framework for analysing data and building machine learning models.The library has been tested on reel use cases and will be the number one tool for professional Data Scientists.The current version supports supervised machine learning and will be expanded to unsupervised followed by Time Series analysis. The project is open source and will grow through contributions by its users.Official Documentation:LinkExamples to get started:LinkInstallingpip install basataUsagefrom basata.datasets import get_data
from basata.supervised import *Upgradepip install --upgrade basataorpip install --upgrade basata==<VERSION_NUMBER>LicenseBASATA is completely free and open-source and licensed under theMITlicense.Contributors