Saravanan666/pypi-org huggingface dataset:
Package: raccy-orm
RACCY-ORMOVERVIEWRaccy-ORM is a small lightweight ORM, inspired by Django-ORM.REQUIREMENTSPython 3.7+Works on Linux, Windows, and MacINSTALLpipinstallraccy-ormEXAMPLEfromdatetimeimportdatetimeasdtimportroasmodelconfig=model.Config()config.DATABASE=model.SQLiteDatabase(':memory:')classAuthor(model.Model):author_id=model.PrimaryKeyField()name=model.CharField(max_length=75)age=model.IntegerField()lucky_number=model.IntegerField(default=90)salary=model.FloatField(default=50000)date=model.DateField()datetime=model.DateTimeField()adult=model.BooleanField(default=False)classPost(model.Model):author_id=model.ForeignKeyField(Author,'author_id')post=model.TextField(null=False)Author.objects.bulk_insert(dict(name='Kwame',age=45,date=dt.now().date(),datetime=dt.now(),lucky_number=99,salary=6400),dict(name='Yaw',age=32,date=dt.now().date(),datetime=dt.now(),lucky_number=56,salary=6400),dict(name='Fiifi',age=23,date=dt.now().date(),datetime=dt.now(),lucky_number=34),dict(name='Navas',age=21,date=dt.now().date(),datetime=dt.now()),dict(name='Jesus',age=34,date=dt.now().date(),datetime=dt.now(),salary=6400,adult=True))Post.objects.insert(post="This is Kojo's post",author_id=1)Post.objects.insert(post="This is Kwabena's post",author_id=2)Post.objects.insert(post="This is Baffour's post",author_id=3)Post.objects.insert(post="This is Derrick's post",author_id=1)fordinAuthor.objects.all():print(d.pk,d.name)1Kwame2Yaw3Fiifi4Navas5Jesus
Package: raccy-utils
RACCY-UTILSOverviewRaccy-Utils is a collection of utility functions and classes for various python libraries.RequirementsPython 3.7+Works on Linux, Windows, and MacExample, working with  config filesCurrently this library supports only json and text config files, and it does not support comments at the moment.fromruimportTextConfigconfig=TextConfig()print(config['foo'])foofromruimportJsonConfigconfig=JsonConfig()print(config['foo'])fooExample, working with seleniumfromruimportmanual_entryfromseleniumimportwebdriverfromselenium.webdriver.common.keysimportKeysdriver=webdriver.Chrome()driver.get('https://google.com')manual_entry(driver,'//input[@title="Search"]','raccy-utils',Keys.ENTER)Installationpipinstallraccy-utils
Package: racebert
RaceBERT -- A transformer based model to predict race and ethnicty from namesInstallationpip install racebertUsing a virtual environment is highly recommended!
You may need to install pytorch as instructed here:https://pytorch.org/get-started/locally/PaperTodoUsageraceBERT predicts race (U.S census race) and ethnicity from names.fromracebertimportRaceBERTmodel=RaceBERT()# To predict racemodel.predict_race("Barack Obama")>>> {"label": "nh_black", "score": 0.5196923613548279}The race categories are:RaceLabelNon-hispanic Whitenh_whiteHispanichispanicNon-hispanic Blacknh_blackAsian & Pacific IslanderapiAmerican Indian & Alaskan Nativeaian# Predict ethnicitymodel.predict_ethnicty("Arjun Gupta")>>> {"label": "Asian,IndianSubContinent", "score": 0.9612812399864197}The ethnicity categories are:EthnicityGreaterEuropean,BritishGreaterEuropean,WestEuropean,FrenchGreaterEuropean,WestEuropean,ItalianGreaterEuropean,WestEuropean,HispanicGreaterEuropean,JewishGreaterEuropean,EastEuropeanAsian,IndianSubContinentAsian,GreaterEastAsian,JapaneseGreaterAfrican,MuslimAsian,GreaterEastAsian,EastAsianGreaterEuropean,WestEuropean,NordicGreaterEuropean,WestEuropean,GermanicGreaterAfrican,AfricansGPUIf you have a GPU, you can speed up the computation by specifying the CUDA device when you instantiate the model.fromracebertimportRaceBERTmodel=RaceBERT(device=0)# predict race in batchmodel.predict_race(["Barack Obama","George Bush"])>>>
[
        {"label": "nh_black", "score": 0.5196923613548279},
        {"label": "nh_white", "score": 0.8365859389305115}
]# predict ethnicity in batchmodel.predict_ethnicity(["Barack Obama","George Bush"])HuggingFaceAlternatively, you can work with the transformers models hosted on the huggingface hub directly.Race Model:https://huggingface.co/pparasurama/raceBERTEthnicity Model:https://huggingface.co/pparasurama/raceBERT-ethnicityPlease refer to thetransformersdocumentation.
Package: racecar
A Python package collecting awesome MCMC sampling algorithms.Explore the docs »Examples|Report Bug|Request FeatureTable of ContentsWhy use Racecar?ExamplesInstallationLicenseContactWhy use Racecar?Racecar is a lightweight Python library for sampling distributions in high dimensions using cutting-edge algorithms. It can also be used for rapid prototyping of novel methods and application to large problems.Pass a function evaluating the log posterior and/or its gradient, and away you go. Ideal for usage with big data applications, neural networks, regression, mixture modelling, and all sorts of Bayesian inference and sampling problems.Easily to use and simple to extend with new methods and use cases.Designed for use with stochastic gradients in mind.ExamplesQuickstart exampleWe will sample points from the one-dimensional distributionusing the random walk Metropolis algorithm, and then plot the results.# Import racecar and numpyimportracecarasrcimportnumpyasnp# Define the target log posterior function# Note we only need it up to a constant multiple, so we do not need to# know its normalization constant.deflog_posterior(x):return{'llh':-(np.cos(2*x)+x**2/12)}# Create the sampler object and use Random Walk Metropolisinitial_condition=[0]learning_rate=0.5S=rc.sampler(initial_condition,learning_rate,log_posterior,algo="RWMetropolis")# Sample some points, outputting arrays of the position and the log posteriornumber_of_points=100000Pos_traj,LLH_traj=S.sample(number_of_points,output=['pos','llh'])# Plot the results using matplotlibMore examplesSome more detailed examples are given in the Jupyter notebooks belowBayesian inference for the mean of Gaussian dataLogistic regression using stochastic gradientsInstallationYou can install the package from source by cloning this repo and usingsetup.py, the only dependencies are onnumpyandscipy. Otherwise it is available on pip viapip install racecarLicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactMade by Charles Matthews -www.cmatthe.ws-mail@cmatthe.wsProject Link:https://github.com/c-matthews/racecar
Package: racedata
**********************Project CARS Race Data**********************.. image:: https://travis-ci.org/SenorPez/project-cars-race-data.svg?branch=0.1-devel:target: https://travis-ci.org/SenorPez/project-cars-race-data.. image:: https://codecov.io/gh/SenorPez/project-cars-race-data/branch/0.1-devel/graph/badge.svg:target: https://codecov.io/gh/SenorPez/project-cars-race-dataProcesses Project CARS Telemetry Data to create data about races.Current release: 0.1Current edge state: RoughCurrent mood: HopefulThe Project CARS Race Data (PCRD) is a Python package intended to process Project CARS Telemetry Data and make that data available for further use.Requirements============- `Python 3.3 <https://www.python.org/download/releases/3.3.0/>`_ or greaterInstallation============The Project CARS Race Data can be installed with ``pip``: ``pip install racedata``.Usage=====The Project CARS Race Data is a python package, and can be imported into your project:``from racedata import RaceData``
Package: race-report-2023
Report of Monaco 2018 RacingReport of Monaco 2018 RacingInstallationpip install -i https://test.pypi.org/simple/ race-report-2023Usingmain usagepy main.py --folder './Log_Data/'

- command conteins name of API "main.py"
- argument --folder which indicates the location of files
- path to files in str format like './Log_Data/'optionsdriver statisticspy main.py --folder './Log_Data/' --driver 'Carlos Sainz'

- --driver argument conteins string with name of racer
    and return only driver statistic infomationdesc sorting statisticspy main.py --folder './Log_Data/' --desc
- check-in statistics are displayed in reverse orderImport functions usingfrom report_generate import .....
and sellect from list of functions


Homepage: https://test.pypi.org/project/race-report-2023/
Package: rac-es
rac_esHelpers for Elasticsearch, including Analyzers and Documents.SetupMake sure this library is installed:$ pip install rac_esUsageYou can then userac_esin your Python code by importing it:import rac_esWhat's HereAnalyzersrac_es includes analyzers which provide custom processing of text fields.DocumentsThe Elasticsearch Document definitions in rac_es match the four main object
types in the RAC data model: Agents, Collections, Objects and Terms. In addition
to these definitions, rac_es provides custom search and save methods for these
Documents, including bulk save and delete methods.DevelopmentThis repository contains a configuration file for gitpre-commithooks which help ensure that code is linted before it is checked into version control. It is strongly recommended that you install these hooks locally by installing pre-commit and runningpre-commit install.LicenseThis code is released under anMIT License.
Package: races_report
races_reportTable of ContentsInstallationLicenseRunning races_report with CLIInstallationpip install races_reportLicenseraces_reportis distributed under the terms of theMITlicense.Running races_report with CLIusage:main.py [-h] -p PATH [-d DRIVER] [--asc | --desc]optional arguments:-h, --helpshow this help message and exit-p PATH, --path PATHpath to start and end data files-d DRIVER, --driver DRIVERdriver's full name--ascshows list of drivers in asc order--descshows list of drivers in desc orderfor example:python main.py -p "results" --descorpython main.py -p "results" -d "Sebastian Vettel"# main.py

from races_report import main


if __name__ == '__main__':
    main.main()The directoryresultsshould contain the following files of a certain structure (for example):# abbreviations.txt

DRR_Daniel Ricciardo_RED BULL RACING TAG HEUER
SVF_Sebastian Vettel_FERRARI
LHM_Lewis Hamilton_MERCEDES
KRF_Kimi Räikkönen_FERRARI
VBM_Valtteri Bottas_MERCEDES
EOF_Esteban Ocon_FORCE INDIA MERCEDES
FAM_Fernando Alonso_MCLAREN RENAULT
CSR_Carlos Sainz_RENAULT
SPF_Sergio Perez_FORCE INDIA MERCEDES
PGS_Pierre Gasly_SCUDERIA TORO ROSSO HONDA
NHR_Nico Hulkenberg_RENAULT
SVM_Stoffel Vandoorne_MCLAREN RENAULT
SSW_Sergey Sirotkin_WILLIAMS MERCEDES
CLS_Charles Leclerc_SAUBER FERRARI
RGH_Romain Grosjean_HAAS FERRARI
BHS_Brendon Hartley_SCUDERIA TORO ROSSO HONDA
MES_Marcus Ericsson_SAUBER FERRARI
LSW_Lance Stroll_WILLIAMS MERCEDES
KMH_Kevin Magnussen_HAAS FERRARI# start.log

SVF2018-05-24_12:02:58.917
NHR2018-05-24_12:02:49.914
FAM2018-05-24_12:13:04.512
KRF2018-05-24_12:03:01.250
SVM2018-05-24_12:18:37.735
MES2018-05-24_12:04:45.513
LSW2018-05-24_12:06:13.511
BHS2018-05-24_12:14:51.985
EOF2018-05-24_12:17:58.810
RGH2018-05-24_12:05:14.511
SSW2018-05-24_12:16:11.648
KMH2018-05-24_12:02:51.003
PGS2018-05-24_12:07:23.645
CSR2018-05-24_12:03:15.145
SPF2018-05-24_12:12:01.035
DRR2018-05-24_12:14:12.054
LHM2018-05-24_12:18:20.125
CLS2018-05-24_12:09:41.921
VBM2018-05-24_12:00:00.000# end.log

MES2018-05-24_12:05:58.778 
RGH2018-05-24_12:06:27.441
SPF2018-05-24_12:13:13.883
LSW2018-05-24_12:07:26.834
DRR2018-05-24_12:11:24.067
NHR2018-05-24_12:04:02.979
CSR2018-05-24_12:04:28.095
KMH2018-05-24_12:04:04.396
BHS2018-05-24_12:16:05.164
SVM2018-05-24_12:19:50.198
KRF2018-05-24_12:04:13.889
VBM2018-05-24_12:01:12.434
SVF2018-05-24_12:04:03.332
EOF2018-05-24_12:12:11.838
PGS2018-05-24_12:08:36.586
SSW2018-05-24_12:11:24.354
FAM2018-05-24_12:14:17.169
CLS2018-05-24_12:10:54.750
LHM2018-05-24_12:11:32.585
Package: race-strategist
F1 2021 - The Official Video Game from CodemastersA tool for forwarding the UDP telemetry data from the simulator game F1 2021 to InfluxDB
for display via any graphing application.UDP SpecificationData StorageIs done byInfluxDBwhich is a great time series
database.
SeeDocker Hub.dockerpullquay.io/influxdb/influxdb:latestGraphing Using GrafanaYou can use Grafana to connect to InfluxDB. See -https://grafana.com/docs/grafana/latest/installation/docker/dockerrun-d-p3000:3000grafana/grafanaConnection to InfluxDBConnecting to InfluxDB 2 requires Grafana 7.1 and above.Refresh RateTo enable sub second updates, alter the following in/etc/grafana/grafana.ini#################################### Dashboards History ##################[dashboards]# Number dashboard versions to keep (per dashboard). Default: 20, Minimum: 1;versions_to_keep = 20min_refresh_interval=100msNext add the refresh rate as an option in the dashboard setting, otherwise you won't be able
to select it.LinksExcellent Telemetry toolPXG F1Similar project usingKafkaand
withCamelDemoYouTube:Grafana DemoTelemetryWheel speed in meters per second.
Wheel slip is 0 -> 1https://www.reddit.com/user/jeppe96/posts/
Package: racing-report-SWAGhettie
Module Documentation: qualifying_reportThe qualifying_report module provides functionality to read data from two log files
containing start and end times for each driver's lap, calculates the lap times, sorts the results,
and writes the output to a new file.
It also provides functionality to print the leaderboard or driver's information from the generated report.Functionsbuild_report(folder_path: str = '') -> None
This function reads in data from two log files containing start and end times for each driver's lap,
calculates the lap times, sorts the results, and writes the output to a new file.print_report(folder_path: str = '', driver_name: str = '', desc: bool = False, asc: bool = False) -> None
This function prints the leaderboard or driver's information from the generated report.main() -> None
This function parses command-line arguments, builds a report or prints driver information
based on the provided arguments.
Package: racket
Serve models with confidence.Free software: GNU General Public License v3Documentation:https://racket.readthedocs.io/en/latest/OverviewLet’s face it. Building models is already challenging enough. But putting them into production is
usually a big enough challenge to grant the employment of an entire separate team. The goal of
the project is removing (or at least softening) the dependency on machine learning engineers and devops,
enabling data scientist to go from concept to production in minutes.NoteSTATUS: alpha. Active development, but breaking changes may come.Presented at PyData:video,slidesFeaturesEasy integration with TensorFlow Serving and KerasRESTful interface with interactive Swagger documentationModel introspection: ability to view model performance and input requirementsAbility to deploy automatically different models with a single commandRich CLI capabilities, going from project scaffolding to training, serving, and dashboardingSmall codebase, statically typed with mypy, and extensive docstringsComing SoonTM: Web-ui for managing, introspecting, and deploying models.DemoRoadmapWeb dashboard for model management and introspectionSupport for Pytorch using ONNXPath to production: docker-based deployments to major cloud providersSecurity capabilities with SSL encryptionCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.The icon was created bysmashicons.History0.1.0 (2018-11-02)First release on PyPI.0.2.0 (2018-11-13)Major feature implementation and documentationStatic typingTesting - 78% coverage0.3.0 (2018-11-20)Major internals refactoringAPI unchanged, although external API was made more clear and documented
Package: racks-on-tabs
racks-on-tabs(What you got?) Racks on tabs on tabs(He got) Racks on tabs on tabs(We got) Racks on tabs on taaabs~YC & Future, circa 2011, probablyCommand line app for browsing CSV files with row lazy loading.This is what opening1M rowsunder 100ms looks like.$racks-on-tabs<PATH_TO_CSV>Starts a local webserver that serves the app content, accessible atlocalhost:7482.InstallationRequires Python 3. Tested with Python 3.8.pipxracks-on-tabsis a Python app with a set of dependencies.
The recommended way to install it is viapipx:$pipxinstallracks-on-tabs
$racks-on-tabs<PATH_TO_CSV>virtualenvAlternatively, you can set up a local virtual env and installracks-on-tabsthere:$python3-mvenvvenv
$sourcevenv/bin/activate
$pip3installracks-on-tabs
$racks-on-tabs<PATH_TO_CSV>TroubleshootingIf for some reason you get "executable not found" errors, try locating the library installation path, and run:$python-mpath/to/pip/packages/racks_on_tabs.__main__<PATH_TO_CSV>
Package: rackspaceauth
This package provides plugins tokeystoneauth1,
the OpenStack Keystone authentication library, for Rackspace’s supported
authentication methods: API key, password, and token.UsageThe following example authenticates Mayor McCheese with his API key,
as found in hiscontrol panel.from rackspaceauth import v2
from keystoneauth1 import session

auth = v2.APIKey(username="Mayor McCheese",
                 api_key="OMGCHEESEISGREAT")

sess = session.Session(auth=auth)
sess.get_token()To use in aclouds.yamlfile, for use with shade, ansible, os-client-config, and other tools, one might
add a section like this:clouds:
  rackspace-iad
    profile: rackspace
    auth:
      username: mayor-mccheese
      api_key: OMGCHEESEISGREAT
    auth_type: rackspace_apikey
    region_name: IAD
Package: rackspace-backup-client
***************************Cloud Backup API Python SDK***************************:version: 0.19.3:Presentations: `Introduction`_.. image:: https://requires.io/github/cabrera/python-cloudbackup-sdk/requirements.png?branch=master:target: https://requires.io/github/cabrera/python-cloudbackup-sdk/requirements/?branch=master:alt: Requirements Status.. image:: https://travis-ci.org/cabrera/python-cloudbackup-sdk.png?branch=master:target: https://travis-ci.org/cabrera/python-cloudbackup-sdk:alt: TravisCI StatusWelcome to the Python bindings to the Rackspace Cloud BackupAPI. These bindings will help you make the most of the Cloud Backupsystem and integrate it into your workflows.**Table of Contents**.. contents:::local::depth: 2:backlinks: none========Features========* Python 2.7 and 3.3+ supported* Easy to install (pip)* Easy to test (nose + tox)* Designed to be easy to use with `ipython`_- Tab-completion and smart introspection- verbs map to object functions: backup.start()- nouns map to object properties: backup.reportI'll let the code speak for ease of use:.. code-block:: pythonfrom rcbu.client.client import Clientfrom rcbu.client.connection import Connectionimport rcbu.client.backup_configuration as backup_configconn = Connection('username', 'dfw',apikey='981263y1hq82yh8y9q38q2')client = Client(conn)myconf = backup_config.from_file('backup_config.json', conn)# Upload a new backup configuration to the Backup APImyconf.create()backup = client.create_backup(myconf)status = backup.start()# block here until the backup completes# polls once a minute by defaultbackup.wait_for_completion(poll_interval=.5)# easy reporting and checking for successreport = backup.reportreport.raise_if_not_ok()Check out the `backup_config.json`_=======Install=======Make sure you have libgmp, libssl, and the Python development headers installed::sudo apt-get install libgmp-dev libssl-dev python-devsudo apt-get install libgmp-dev libssl-dev python3-dev  # for Py3On Windows, make sure that the proper Visual Studio path is configured::SET VS90COMNTOOLS=%VS100COMNTOOLS%  # MSVS 2010SET VS90COMNTOOLS=%VS110COMNTOOLS%  # MSVS 2012.. code-block:: bashpip install git+https://github.com/rackerlabs/python-cloudbackup-sdk============Contributing============Some simple guidelines:* Unit tests for new features* Keep the code clean - flake8* Be wary of warnings and errors - pylint* >95% code coverage - keep it strong* Be `Pythonic`_For more details, checkout the `Contributing`_ guide.If you have any questions, please check in with Alejandro Cabrera<alejandro.cabrera@rackspace.com>... _Pythonic: http://www.python.org/dev/peps/pep-0020/.. _backup_config.json: https://github.com/rackerlabs/python-cloudbackup-sdk/blob/master/examples/create_a_backup/backup_config.json.. _ipython: http://ipython.org/.. _Introduction: https://one.rackspace.com/download/attachments/21615636/python-sdk.pdf.. _Contributing: https://github.com/rackerlabs/python-cloudbackup-sdk/blob/master/CONTRIBUTING.rst
Package: rackspace-neutronclient
Metapackage to install rackspace-python-neutronclient and Rackspace extensionsInstallpip install rackspace-neutronclientUsageThis metapackage will ensure that rackspace-python-neutronclient and these
extensions are installed that are compatible with the Rackspace cloud:rackspace-auth-neutronclientextpython_neutronclient_ip_address_extension
Package: rackspace-novaclient
Metapackage to install python-novaclient and Rackspace extensionsInstallpip install rackspace-novaclientUsageThis metapackage will ensure that python-novaclient and these extensions
are installed that are compatible with the Rackspace cloud:rackspace-auth-openstackos_diskconfig_python_novaclient_extrax_scheduled_images_python_novaclient_extos_networksv2_python_novaclient_extos_virtual_interfacesv2_python_novaclient_extrax_default_network_flags_python_novaclient_extip_associations_python_novaclient_ext
Package: rackspace-python-neutronclient
This is a client library for Neutron built on the Neutron API. It
provides a Python API (theneutronclientmodule) and a command-line tool
(neutron).License: Apache License, Version 2.0PyPi- package installationOnline DocumentationLaunchpad project- release managementBlueprints- feature specificationsBugs- issue trackingSourceDeveloper’s Guide
Package: rackspacesdk
This plugin enables support for Rackspace authentication and services
with theOpenStack SDK.UsageThe following example connects to the Rackspace Public Cloud and lists
containers stored in Cloud Files within the IAD datacenter.from rackspace import connection
conn = connection.Connection(username="my_user",
                             api_key="123abc456def789ghi",
                             region="IAD")

for container in conn.object_store.containers():
    print(container.name)DocumentationRackspace-specific documentation is not currently available.Documentation for the OpenStack SDK is available athttp://developer.openstack.org/sdks/python/openstacksdk/RequirementsPython 2.7+, Python 3.4+openstacksdk>=0.6LicenseApache 2.0
Package: rackspace-ticket-report
A script to gather the week over week ticket counts for the specified time
range. This was specifically created for the Rackspace Private Cloud Insights
API.InstallTo install theget_ticket_report.pyscript:pip install rackspace-ticket-reportRunTheget_ticket_report.pyscript has a complete help text by runningget_ticket_report.py -h. Usage:$ get_ticket_report.py --start <start_date> --end <end_date>
Enter auth token: <your auth token>
date range, customer generated, employee generated, system generated, total
...
Package: rackspace-token-generator
A script to generate authentication tokens for use with Rackspace Cloud
Identity. This was specifically created for the Rackspace Private Cloud
Insights API, though it is generally applicable to anything that needs
to be given a token.InstallTo install theget_token.pyscript:pip install rackspace-token-generatorRunTheget_token.pyscript has a complete help text by runningget_token.py -h. The following are the most common usages:$ get_token.py --username <your username> --password
Enter password: <your password>
Token:
  <token>

$ get_token.py --username <your username> --api-key
Enter API key: <your API key>
Token:
  <token>
Package: racktables-api
rtapiRacktables APIPython module for accessing and manipulating racktables objects.Installationpip install racktables-apiPyPi projecthttps://pypi.org/project/racktables-api/Documentationhttps://rtapi.readthedocs.ioExampleimport MySQLdb
import rtapi

# Create connection to database
try:
    # Create connection to database
    db = MySQLdb.connect(host='hostname',port=3306, passwd='mypass',db='racktables',user='racktables')
except MySQLdb.Error:
    e = sys.exc_info()[1]
    print("Error %d: %s" % (e.args[0],e.args[1]))
    sys.exit(1)

# Initialize rtapi with database connection
rt = rtapi.RTObject(db)

# List all objects from database
print rt.ListObjects()