Saravanan666/pypi-org huggingface dataset:
Package: bc-python-hcl2
NoticeThis is a fork of thePython HCL2repo by Amplify and is
officially supported by Bridgecrew. We contributed as much as possible to the upstream project, but the two projects
are now deviating in a way that pushing new changes upstream doesn't make sense anymore.Python HCL2A parser forHCL2written in Python usingLark. This parser only supports HCL2 and isn't backwards compatible
with HCL v1. It can be used to parse any HCL2 config file such as Terraform.About AmplifyAmplify builds innovative and compelling digital educational products that empower teachers and students across the
country. We have a long history as the leading innovator in K-12 education - and have been described as the best tech
company in education and the best education company in tech. While others try to shrink the learning experience intothe technology, we use technology to expand what is possible in real classrooms with real students and teachers.Learn more athttps://www.amplify.comGetting StartedPrerequisitespython-hcl2 requires Python 3.7 or higher to run.InstallingThis package can be installed usingpippipinstallbc-python-hcl2Usageimporthcl2withopen('foo.tf','r')asfile:dict=hcl2.load(file)Building From SourceFor development,tox>=2.9.1is recommended.Running Testspython-hcl2 usestox. You will need to install tox withpip install tox.
Runningtoxwill automatically execute linters as well as the unit tests.You can also run them individually with the-eargument.For example,tox -e py37-unitwill run the unit tests for python 3.7To see all the available options, runtox -l.Responsible DisclosureIf you have any security issue to report, contact project maintainers privately.
You can reach us atmailto:github@amplify.comContributingWe welcome pull requests! For your pull request to be accepted smoothly, we suggest that you:For any sizable change, first open a GitHub issue to discuss your idea.Create a pull request.  Explain why you want to make the change and what it’s for.We’ll try to answer any PR’s promptly.
Package: bcr-api
Brandwatch Consumer Research API ClientIntroductionThe Brandwatch Consumer Research API Client was designed to address many of the challenges involved in building complex applications which interact with RESTful API's in general and Brandwatch's Consumer Research API, in particular:The object hierarchy roughly mirrors the API's resource hierarchy, making the code intuitive for those familiar with the Brandwatch Consumer Research platformAll required parameters are enforced, and most optional parameters are supported and documentedTypical workflows are supported behind the scenes; for instance, one can validate, upload, and backfill a query with a single function callThe library is designed to support simple and readable code: sensible defaults are chosen for rarely used parameters and all resource IDs are handled behind the scenesFrom the user's perspective, the basic structure of the library is as follows.  One first creates an instance of the classBWProject; this class handles authentication (via a user name and password or API key) and keeps track of project-level data such as the project's ID.  (Behind the scenes, the user-level operations are handled by the classBWUserfrom whichBWProjectis inherited.)  One passesBWProjectinstance as an argument in the constructor for a series of classes which manage the various Brandwatch resources: queries, groups, tags, categories, etc.  These resource classes manage all resource-level operations: for example a singleBWQueriesinstance handles all HTTP requests associated with queries in its attached project.InstallationBe sure to install the latest version of Python 3.x. You can install the library on your machine by running the following command:pip install bcr-apiThis allows you to run scripts that import bwproject or bwresources from anywhere on your computer.ExamplesPlease see the Jupyter notebook DEMO.ipynb for examples.  This notebook was built as a beginner's guide to using the library, so it has example code, as well as detailed instructions for use.DisclaimerThis is not an official or supported Brandwatch library, and should be implemented at the users' own risk.
Package: bcrpy
bcrpyUn cliente API para la extraccion, consulta y analisis de la base de datosBCRPDatadelBanco Central de Reserva del Peru (BCRP)escrito para Python. Este cliente es unwrapperde laAPI para Desarrolladoresdel BCRP.VinculosDocumentación en linea (readthedocs)Manual bcrpy (pdf)pip package indexEn apoyo al código abierto, este repositorio se encuentra participando en Hacktoberfest 2023. Te invitamos a consultarCONTRIBUIRpara obtener instrucciones sobre cómo contribuir como colaborador y unirte a nuestra lista de colaboradores.For English guidelines in how to participate, check theCONTRIBUTINGfile.InstalacionEn su sistema local (laptop o computadora) bcrpy puede ser instalada con el comando pip install bcrpy. Aun asi, se
recomienda instalar bcrpy dentro de un ambiente virtual virtualenv. El protocolo para aquel seria el siguiente:virtualenvvenvsourcevenv/bin/activatepipinstallbcrpybcrpy ha sido desarrollado con un protocolo de programación orientada a objetos (tambien conocido comoObject
Oriented Programming (OOP)) lo cual se reduce a que objetos pueden ser usados a almacenar metodos (funciones),
datos, y su manejo de aquellos.
Package: bcselector
Documentation:https://kaketo.github.io/bcselector.Repository:https://github.com/kaketo/bcselector.What is it?Feature selection is a crucial problem in many machine learning tasks. Usually the considered
variables are cheap to collect and store but in some situations the acquisition of feature values
can be problematic. For example, when predicting the occurrence of the disease we may consider
the results of some diagnostic tests which can be very expensive.
The existing feature selection methods usually ignore costs associated with the considered
features. The goal of cost- sensitive feature selection is to select a subset of features which allow
to predict the target variable (e.g. occurrence of the diseases) successfully within the assumed
budget.The main purpose of this package is to provide filter methods of feature selection based
on information theory and to propose new variants of these methods considering feature costs.Installationbcselector can be installed from [PyPI] (https://pypi.org/project/bcselector):pip install bcselectorQuickstartFirst of all we must have a dataset with classification target variable and a cost assigned to each feature.
Good sample data could behepatitisfromUCIrepository [1].Lets say that that we have dataset loaded to Python, we need to createSelectorclass and callfitmethod with proper arguments on it:fromsklearn.linear_modelimportLogisticRegressionfromsklearn.metricsimportroc_auc_scorefrombcselector.variable_selectionimportFractionVariableSelectorfrombcselector.datasetsimportload_sample# Arguments for feature selection# r - cost scaling parameter,# beta - kwarg for j_criterion_func,# model - model that is fitted on data.r=1beta=0.5model=LogisticRegression(max_iter=1000)# DataX,y,costs=load_sample()# Feature selectionfvs=FractionVariableSelector()fvs.fit(data=X,target_variable=y,costs=costs,r=r,j_criterion_func='cife',beta=beta)Now we can obtain feature selection results by calling simple getter:fvs.get_cost_results()Or we can score and plot our results with any sklearn model and classification metric:fvs.score(model=model,scoring_function=roc_auc_score)fvs.plot_scores(compare_no_cost_method=True,model=model,annotate=True)Which results in BC-plot:OnOX axiswe have accumulated cost and onOY axiswe see test set score of currently selected set of features:Blue lineis cost-sensitive method selected features order.Red lineis NO-cost method selected features order.Blue vertical lineis maximum budget avaliable (user parameter)Small numbers above or below the curve are indexes of selected features. Therefore we can see that first variable selected by cost-sensitive method is on 14th column in datasetX.Bibliography[1] Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.CitationsTBD
Package: bcst
Beautiful Custom Start PageBasic usagebcstallow you to create a beautiful start page very quickly. To installbcstrunpip install bcst. Then choose a theme (screenshots are availaible on the package repository):bcst -lNext step, extract the theme resources:bcst -e <your-theme> > resources.jsonThis, will create aresources.jsonin the current directory. Now, you can customize it to your needs. Then, generate your start page:bcst -g <your-theme> resources.json <start-page-destination>And voila!Submit your own themeTODO
Package: bcTesting
This is a simple testing framework to make the unittesting easy.Author:Barish Chandran B1Introduction1.1What is bcTesting?1.2What is it good for?1.3How is bcTesting licensed?1.4Contact details2Installation2.1Prerequisites2.2Installation using pip2.3Installation From Source3Package contents4Contributors1Introduction1.1What is bcTesting?bcTestingis a simple unittesting framework. Which is a combination of mox, mock and fixtures.1.2What is it good for?New testing framework for tool developementEasy integration of mox and fixuresExtra assert functionalities1.3How is bcTesting licensed?MIT License1.4Contact detailsDrop me an email tobarishcb@gmail.comfor any questions regardingbcTesting. For reporting problems withbcTestingor submitting feature requests, the best way is to open an issue on thebcTesting project page.2Installation2.1PrerequisitesbcTestingwas tested only on Python 2.7, on both Linux and Windows. It should work on any later version as well.2.2Installation using pipAlternatively, sincebcTestingis listed in thePython Package Index(PyPI), you can install it with:> pip install bcTesting2.3Installation From SourceInstallingbcTestingis very simple. Once you download and unzip the package, you just have to execute the standardpython setup.py install. The setup script will then place thebcTestingmodule intosite-packagesin your Python’s installation library.Setps using normal python> cd <development_path>
> git clone https://github.com/barishcb/testing.git
> cd testing
> python setup.py build
> python setup.py test
> python setup.py installSetps using gnu make> cd <development_path>
> git clone https://github.com/barishcb/testing.git
> cd testing
> make build
> make test
> make install3Package contentsOnce you unzip thebcTestingpackage, you’ll see the following files and directories:README.rst:This README file.setup.py:Installation scriptbcTesting/:ThebcTestingmodule source code.tests/:Unit tests.4ContributorsBarish Chandran B
Package: bctsag-telliotcore
BackgroundCommand line tool and SDK for interacting with the Tellor Protocol.The packagetelliot-coreversion0.3.0forked from:https://github.com/tellor-io/telliot-coreInitial SetupPrerequisitesThe following tools are expected to be installed on your system to run this project:Python 3.9.xPip 23.3.xGitSetuppython3.9-mvenvtenvsourcetenv/bin/activate
pip3.9install.NPM ganache dependency required:npminstallganache--globalTestInstall development requirements:pip3.9install-rrequirements-dev.txtRun automated testing in all environments:toxRunpy39testing:tox-epy39Runstyletesting:tox-estyleRuntypingtyping:tox-etypingPublishCreate the$HOME/.pypircfile:[pypi]
  username = __token__
  password = pypi-AgEIcHlw... (your PyPI token)Build distribution:python3.9-mbuildDeploy distribution to PyPI repository:twineuploaddist/*UsageConfigurationCreate the default configuration files:telliot-coreconfiginitThe default configuration files are created in the folder~/telliot/or$HOME/telliot/.View your current configuration:telliot-coreconfigshowContributingBug reports and pull requests are welcome on GitHub at:https://github.com/BCTSAG/tellor
Package: bctsag-telliotfeeds
BackgroundReporting tools and datafeeds for Tellor oracles.The packagetelliot-feedsversion0.1.14forked from:https://github.com/tellor-io/telliot-feedsInitial SetupPrerequisitesThe following tools are expected to be installed on your system to run this project:Python 3.9.xPip 23.3.xGitSetuppython3.9-mvenvtenvsourcetenv/bin/activate
pip3.9install.TestInstall development requirements:pip3.9install-rrequirements-dev.txtRun automated testing in all environments:toxRunpy39testing:tox-epy39Runstyletesting:tox-estyleRuntypingtyping:tox-etypingPublishCreate the$HOME/.pypircfile:[pypi]
  username = __token__
  password = pypi-AgEIcHlw... (your PyPI token)Build distribution:python3.9-mbuildDeploy distribution to PyPI repository:twineuploaddist/*UsageUsing DockerCreate & start container in the background:dockercomposeup-dExecute commands in a running Docker container:dockerexec-ittelliotshConfigurationCreate the default configuration files:telliotconfiginitThe default configuration files are created in a folder calledtelliotin the user's home folder.View your current configuration:telliotconfigshowUsing TelliotAdd Reporting account:telliotaccountaddfantomacct15d18c4aabe8f0ee841e2e0ee504c7d9ec98d2aa9edb2e44d5e8825ec0670f8964002Check Reporting account:telliotaccountfindUsing StakingToken smart contract address provided below, mint 1000 TTRB tokens to thefantomacct1account:0x8e4E5eDab27Df5a93B25AC3a62b80beec1CfEBd0Report data with thefantomacct1account to the Tellor Oracle:telliotreport-afantomacct1-ncr-qttrb-usd-spotContributingBug reports and pull requests are welcome on GitHub at:https://github.com/BCTSAG/tellor
Package: bcts-telliotcore
BackgroundCommand line tool and SDK for interacting with the Tellor Protocol.The packagetelliot-coreversion0.3.0forked from:https://github.com/tellor-io/telliot-coreInitial SetupPrerequisitesThe following tools are expected to be installed on your system to run this project:Python 3.9.xPip 23.3.xGitSetuppython3.9-mvenvtenvsourcetenv/bin/activate
pip3.9install.NPM ganache dependency required:npminstallganache--globalTestInstall development requirements:pip3.9install-rrequirements-dev.txtRun automated testing in all environments:toxRunpy39testing:tox-epy39Runstyletesting:tox-estyleRuntypingtyping:tox-etypingPublishCreate the$HOME/.pypircfile:[pypi]
  username = __token__
  password = pypi-AgEIcHlw... (your PyPI token)Build distribution:python3.9-mbuildDeploy distribution to PyPI repository:twineuploaddist/*UsageConfigurationCreate the default configuration files:telliot-coreconfiginitThe default configuration files are created in the folder~/telliot/or$HOME/telliot/.View your current configuration:telliot-coreconfigshowContributingBug reports and pull requests are welcome on GitHub at:https://github.com/SELISEdigitalplatforms/l3-solidity-bcts-tellor
Package: bcts-telliotfeeds
BackgroundReporting tools and datafeeds for Tellor oracles.The packagetelliot-feedsversion0.1.14forked from:https://github.com/tellor-io/telliot-feedsInitial SetupPrerequisitesThe following tools are expected to be installed on your system to run this project:Python 3.9.xPip 23.3.xGitSetuppython3.9-mvenvtenvsourcetenv/bin/activate
pip3.9install.TestInstall development requirements:pip3.9install-rrequirements-dev.txtRun automated testing in all environments:toxRunpy39testing:tox-epy39Runstyletesting:tox-estyleRuntypingtyping:tox-etypingPublishCreate the$HOME/.pypircfile:[pypi]
  username = __token__
  password = pypi-AgEIcHlw... (your PyPI token)Build distribution:python3.9-mbuildDeploy distribution to PyPI repository:twineuploaddist/*UsageUsing DockerCreate & start container in the background:dockercomposeup-dExecute commands in a running Docker container:dockerexec-ittelliotshConfigurationCreate the default configuration files:telliotconfiginitThe default configuration files are created in a folder calledtelliotin the user's home folder.View your current configuration:telliotconfigshowUsing TelliotAdd Reporting account:telliotaccountaddfantomacct15d18c4aabe8f0ee841e2e0ee504c7d9ec98d2aa9edb2e44d5e8825ec0670f8964002Check Reporting account:telliotaccountfindUsing StakingToken smart contract address provided below, mint 1000 TTRB tokens to thefantomacct1account:0x8e4E5eDab27Df5a93B25AC3a62b80beec1CfEBd0Report data with thefantomacct1account to the Tellor Oracle:telliotreport-afantomacct1-ncr-qttrb-usd-spotContributingBug reports and pull requests are welcome on GitHub at:https://github.com/SELISEdigitalplatforms/l3-solidity-bcts-tellor
Package: bd09convertor
BD09ConvertorBD09 coordinates conversion tool for Python.From BD09LL to BD09MCFrom BD09MC to BD09LLPython版本的BD09坐标转换工具从BD09LL转为BD09MC从BD09MC转为BD09LLInstallpip install bd09convertorUsagefrom bd09convertor import convertMC2LL, convertLL2MC

convertMC2LL(12943772.884424742, 4832666.423350099) 
# return (116.27462499999993, 39.96162707324356)

convertLL2MC(116.274625, 39.961627) 
# return (12943772.884424742, 4832666.423350099)ReferenceBD09 is a CRS(Coordinate Reference System) encrypted by Baidu based on GCJ09 CRS.BD09LL represents longitude and latitude.
BD09MC represents coordinate with meter based unit like Web Mercator projection.BD09：为百度坐标系，在GCJ02坐标系基础上再次加密。其中bd09ll表示百度经纬度坐标，bd09mc表示百度墨卡托米制坐标。Ref:https://lbsyun.baidu.com/index.php?title=coordinate
Package: bd103
BD103 Python PackageThese are some utility modules and fun stuff that might make a good dependency. There are some subpackages that has special purposes.bd103.ext: Contains extensions and plugins to other Python packagesbd103.shared: Contains modules that are shared between different implementations of BD103's librarybd103.data: Contains data types for various formatsInstallationpython-mpipinstall-Ubd103Getting StartedA good idea is too look through thedocumentationto find modules that might pique your interest. A recommended one is thedecoratorsmodule.ContributingFeel free to submit a PR if you want to contribute to the package. For more information, seeCONTRIBUTING.md.
Package: bda
baseline_dependent_averagingThis is code for applying baseline-dependent averaging to a radio astronomy
interferometric dataset. It applies the principles and formulas presented inWijnholds et
al. (2018)to
average high-cadence data to a lower cadence while introducing a maximum amount
of decorrelation specified by the user. In brief, short baselines of an
interferometer do not decorrelate as rapidly as long baselines, and so data from
shorter baselines can be averaged together without losing as much coherent sky
information. The code and routines in this repo are designed to work on
already-recorded data (which is typically written at a common cadence for all
baselines) and averages together consecutive time samples until a specific
threshold. A forthcoming memo will describe the operation in more detail.InstallationThe code can be installed by invokingpipinstall.from the top level of the repo. This will install a module calledbdawhich
can be imported. The main user-facing function isbda.apply_bda, which is
designed to work on apyuvdataUVData
object. It also provides a script,apply_bda.py, which can be called from the
command line for applying BDA to an existing dataset on disk.DependenciesThe following packages are required:astropysetuptools_scmpyuvdatapyuvdatacan be installed fromconda(preferred), or frompip. It is
available on theconda-forgechannel. To install:condainstall-cconda-forgepyuvdataTestsThe testing requirements can be installed by invokingpipinstall.[testing]from the top level of the repo. This will install the package and all
dependencies for running tests. The test suite can be run by runningpytestafter installation.DependenciesIn addition to the main package dependencies above, the following packages are
required for running tests:pytest >= 6.0
Package: bda.basen
Module to represent integers as any other based representation string.There exist direct converter functions.>>> from bda.basen import str2int
>>> from bda.basen import int2str

>>> ref = 'abcde'

>>> int2str(12345, ref)
'deddea'

>>> str2int('abcde', ref)
194Or a direct representation call for a 62 ascii based base.>>> from bda.basen import base62
>>> term = base62(100)
>>> str(term)
'1C'

>>> term = base62('1D')
>>> int(term)
101This works also with uuids, which is with ~22 chars less than the default hex with 32 chars:>>> term = base62(uuid.uuid4())basejuses 91 chars ascii as base (no backslash, no single/double quotes, no control chars).
With this a chance to get 20 chars for a uuid is high:>>> term = basej(uuid.uuid4())Source Code and ContributionsIf you want to help with the development (improvement, update, bug-fixing, …) ofbda.basenthis is a great idea!
Submit issues or pull requests!The code is located atgithub.Maintainer is Jens Klein, Robert Niederreiter and the BlueDynamics Alliance developer team.
We appreciate any contribution and if a release is needed to be done on pypi,
please just contact one of usdev@bluedynamics dot comCode is licensed under GPL v2.
History
=======1.2 (2019-09-09)Support for Python 3
[jensens]1.1 (2016-05-25)Support for pythonuuidmodule
[jensens]more genericbasexbase class and specialbasejclass.
[jensens]1.0initial implementation [rnixx, jensens]
Package: bda.bfg.tile
A tile is a piece of web application, i.e. a form, a navigation, etc.Splitting your application in such small and logic application parts makes it
easy to re-use this application, simplifies application ajaxification and
the use of same application parts in different manners.Imports.:>>> from bda.bfg.tile import Tile
>>> from bda.bfg.tile import TileRenderer
>>> from bda.bfg.tile import registerTile
>>> from bda.bfg.tile import tileWe need some dummies as model and request.:>>> class Model(object): pass
>>> model = Model()
>>> from repoze.bfg.request import DEFAULT_REQUEST_FACTORIES
>>> request = DEFAULT_REQUEST_FACTORIES[None]['factory'](environ={})The pure Tile itself. Normally you do not create this directly, this is done
due registration, see below.:>>> mytile = Tile('testdata/tile1.pt', None)
>>> mytile(model,  request)
u'<span>Tile One</span>'Register a tile using the prior template testtemplate. When no object is given,
the default tile is instanciated as above.:>>> registerTile('tileone', 'testdata/tile1.pt')Render the already registered tile.:>>> TileRenderer(model, request)('tileone')
u'<span>Tile One</span>'Now the decorator - level=1 is needed for the doctest only to reduce the module
level.:>>> @tile('tiletwo', 'testdata/tile2.pt', level=1)
... class Tile2(Tile):
...     data = u'custom'
>>> TileRenderer(model, request)('tiletwo')
u'<span>Tile Two: <b><span>Tile One</span></b></span>'You can define an attribute which is responsible to render the tile instead of
defining a template. By defaultrenderis taken. With the keyword argumentattributeyou can point to a different attribute.:>>> @tile('attrtile')
... class Tile2(Tile):
...     def render(self):
...         return u'<h1>Rendered via attribute call</h1>'
>>> TileRenderer(model, request)('attrtile')
u'<h1>Rendered via attribute call</h1>'>>> @tile('attrtile', attribute='foobar')
... class Tile2(Tile):
...     def foobar(self):
...         return u'<h1>Rendered via attribute foobar call</h1>'
>>> TileRenderer(model, request)('attrtile')
u'<h1>Rendered via attribute foobar call</h1>'