Saravanan666/pypi-org huggingface dataset:
Package: balepy
balepybalepy a Library Python for create bot API in bale applicationInstall and Update:pipinstall-UbalepyFor See Docs:WebSiteTELEGRAMSTART:frombalepyimportClientfromasyncioimportrun__token='your-token-here'app=Client(__token,timeout=10)asyncdefmain():forminapp.on_message():awaitapp.send_message(chat_id=m.chat_id,text='Hello __from__ *balepy*',reply_to_message_id=m.message_id)if__name__=='__main__':run(main())Thanks To:AmirAliSocial Media:TELEGRAMRUBIKA
Package: baler-compressor
IntroductionBaler is a tool used to test the feasibility of compressing different types of scientific data using machine learning-based autoencoders. Baler provides you with an easy way to:Train a machine learning model on your dataCompress your data with that model, and save the modelDecompress the file using the model at a later timeGetting StartedBaler now exists as apip packagewhich you can install through:pip install baler-compressorFor examples on how to use Baler we recommend following the tutorial from our baler-demo repository:https://github.com/baler-collaboration/baler-demoContributingIf you wish to contribute, please see thecontribution guidelines.
Package: balic
Balič(readbalitch) is a command-line toolset for working with LXC containers.InstallationInstallbalicviapip:pip install balicBalicrequires the following packages installed:appdirs
cliffUsageCreate a new container:# create a new lxc container called test
balic create -n testBuild setup:# create test build directory
mkdir test

# create build.sh that echos hello world inside the container
echo 'echo "hello world"' > test/build.sh

# copy test directory into the container and run build.sh
balic build -n test -d testIn case you need to prepare anything in the build directory:# create prepare.sh which will be executed locally to prepare build dir
echo 'echo "prepare something inside build dir: `dirname "$0"`"' > test/prepare.sh

# run prepare.sh in given build dir
balic prepare -n test -d testMulti-step builds can be done as well:# create test_step2 build directory
mkdir test_step2

# create build.sh that echos "another step" inside the container
echo 'echo "another step"' > test_step2/build.sh

# copy test directory into container and run build.sh
balic build -n test -d test_step2Pack the built container:# pack test lxc container into rootfs.tar.gz
balic pack -n test -o rootfs.tar.gzDestroy the container when no longer needed:# destroy test lxc container
balic destroy -n testFull workflow:balic create -n test                 # creates lxc container names test
balic prepare -n test -d build_dir   # run prepare.sh inside the build_dir
balic build -n test -d build_dir     # builds test lxc container using content of build_dir
balic build -n test -d build_dir2    # builds test lxc container using content of build_dir2
balic build -n test -d build_dir3    # builds test lxc container using content of build_dir3
...
balic pack -n test -o rootfs.tar.gz  # packs test lxc container into rootfs.tar.gz
balic destroy -n test                # destroy test lxc containerUseenvironmentinput parameter forbuildandpreparecommands:# to run `build.sh environment` or `prepare.sh environment` use -e input parameter
balic prepare -n test -d test -e production
balic build -n test -d test -e productionIe. whatever you specify as environment ends up as$1inbuild.sh/prepare.sh.DocumentationSource of the documentaton is available in theBalicrepositoryhttps://gitlab.com/markuz/balic/tree/master/docs/sourceDevelopmentPull requests welcomed.Balicgit repository is available athttps://gitlab.com/markuz/balicFor more information, seehttps://gitlab.com/markuz/balic/-/blob/master/docs/source/development.rstLicenseBSD 3-clause Clear License
Package: bali-cli
bali-cliis 
    CLI tools to simplify gRPC services and clientsbali-cliCLI tools to simplify gRPC services and clientsCli commandsbali addAdd service to clients folder, syntax isbali add {service}# Example: added a service named `user`baliadduserbali buildBuild current development service protobufLegacy protobuf path:/services/rpc/.protoNew protobuf path:/protos/.proto# Example: build in a service root directorybalibuildbali runNew in2.5.0Start Bali App service, includehttp/rpc/event# Example: start httpbalirunhttp# Example: start rpcbalirunrpc# Example: start eventbaliruneventbali shellNew in2.5.0# Enter Bali App shellbalishellRelated ProjectsCONTRIBUTEDeveloper Environmentmakeinit# setup examples environments
git submodule update --init --recursive

# execute bali commands 
# cd examples/gateway_service && bali add greeterTag a new releasetag a version:gittag-av0.1.0push tags to remote:gitpush--tagsGet a test reportmaketest
Package: ballad
BalladFor when Poetry just doesn't work.Have you tried setting upPoetry, but something doesn't work? Maybe you're...Trying to implement Github Actions but the commands don't register.Testing on a computer that doesn't have Poetry installed.Just wanting to test out a package without any of these crazy dependency managers.If so, then you're in the right place!Install and UseFirst install:$pipinstallballadWant to install dependencies? Run:$python-mballadinstallThere are various options that you can also run. Check them out!ContributingGet the source code with:$gitclonehttps://github.com/BD103/Ballad.git$cdBallad$pipinstall-rrequirements.txtCheck over your code with the following commands:$black.$isort.$flake8Create a PR, thanks for helping!
Package: balladeer
Balladeeris a Python package for creating web-enabled interactive screenplay.For tutorials and updates, please visit thedevelopment blogsite.An API manual is included in thecode repositoryand is available toread online.InstallationHere are the install instructions for Linux. You need Python version 3.11 or higher.Virtual EnvironmentFirst make a fresh Python virtual environment:python3 -m venv ~/balladPackagesUpdate the package manager within it:~/ballad/bin/python -m pip install -U pip wheelInstall (or update) Balladeer:~/ballad/bin/python -m pip install -U balladeerExamplesDownload therepository as a zip file.
Unzip it to a local directory.cdto one of the directories underexamples:cd balladeer/examples/ex_00_hello_worldRun the example like this:~/ballad/bin/python -m main

Hello, World!
Package: ballast
Framework for client-side load-balancing for inter-process
communication between cloud services.It is inspired in large part by Netflix’sRibbonfor java.How to UseIn its most basic form, you can create aballast.Servicewith a static list of servers:>>>importballast>>>my_service=ballast.Service(['127.0.0.1','127.0.0.2'])Now, just use it as you would use therequestspackage:>>>response=my_service.get('/v1/path/to/resource')<Response[200]>NOTE:at this point in time, only the basic api features from therequestspackage are supported.For advanced usage, checkout thedocs
Package: ballchaser
ballchaser ⚽️🚗Unofficial Python API client for the ballchasing.com API.Usagepip install ballchaserAll API requests are exposed via theBallChaserclass which is initialised with aballchasing.com API token.importosfromballchaser.clientimportBallChaserball_chaser=BallChaser(os.getenv("BALLCHASING_API_TOKEN"))# search and retrieve replay metadatareplays=[replayforreplayinball_chaser.list_replays(player_name="GarrettG",replay_count=10)]# retrieve replay statisticsreplay_stats=[ball_chaser.get_replay(replay["id"])forreplayinreplays]API requests can automatically be retried if they return a rate limit response by specifyingbackoff=True. Requests
will be tried up tomax_triestimes with exponential backoff between subsequent retries, e.g.importosfromballchaser.clientimportBallChaserball_chaser=BallChaser(os.getenv("BALLCHASING_API_TOKEN"),backoff=True,max_tries=5)Contributing & FeedbackIf there are any new features you'd like, or you encounter a bug, you can contribute by opening an issue or submitting a pull request.
Package: ballcosmos
Python support for Ball Aerospace COSMOS v4This project allows accessing the COSMOS API from the python programming language.
Additional functionality and support will be added over time.Installation:pip install ballcosmosSee the docs and examples for usageRunning the test$ docker-compose -f .\compose.yaml build
$ docker-compose -f .\compose.yaml up -d
Creating network "python-ballcosmos_default" with the default driver
Creating python-ballcosmos_ballcosmos_1 ... done
$ docker exec -it python-ballcosmos_ballcosmos_1 /bin/ash
/app # coverage run -m pytest ./tests/
/app # coverage report
Package: ballistics
The Python ballistics package is a collection of functions to help analyze and understand the motion of an un-powered
(i.e. not a rocket, missile, etc) projectile from the muzzle to the target. Theballistics.extrasmodule contains
related functions that aren’t strictly related to the study of ballistics but have proven interesting or useful.Free software: MIT licenseInstallationpip install ballisticsDocumentationhttps://python-ballistics.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.4.0Recoil Impulse solver added to extras module.Firearm Code made optional for all Recoil solvers.0.3.0 (2018-02-12)Recoil Velocity solver added to extras module.0.2.0 (2018-02-10)Free Recoil Energy solver added to extras module.0.1.1 (2018-02-07)Implementation no longer supports PyPy (for now).0.1.0 (2018-02-06)First release on PyPI.
Package: balloon-learning-environment
Balloon Learning EnvironmentDocsThe Balloon Learning Environment (BLE) is a simulator for stratospheric
balloons. It is designed as a benchmark environment for deep reinforcement
learning algorithms, and is a followup to the Nature paper"Autonomous navigation of stratospheric balloons using reinforcement learning".Getting StartedNote: The BLE requires python >= 3.7The BLE can easily be installed with pip:$ pip install --upgrade pip
$ pip install balloon_learning_environmentTo install with theacmepackage:$ pip install --upgrade pip
$ pip install balloon_learning_environment[acme]Once the package has been installed, you can test it runs correctly by
evaluating one of the benchmark agents:python -m balloon_learning_environment.eval.eval \
  --agent=station_seeker \
  --renderer=matplotlib \
  --suite=micro_eval \
  --output_dir=/tmp/ble/evalTo install from GitHub directly, run the following commands from the root
directory where you cloned the repository:$ pip install --upgrade pip
$ pip install .[acme]Ensure the BLE is Using Your GPU/TPUThe BLE contains a VAE for generating winds, which you will probably want
to run on your accelerator. See the jax documentation for installing withGPUorTPU.As a sanity check, you can open interactive python and run:from balloon_learning_environment.env import balloon_env
env = balloon_env.BalloonEnv()If you are not running with GPU/TPU, you should see a log like:WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)If you don't see this log, you should be good to go!Next StepsFor more information, see thedocs.Giving creditIf you use the Balloon Learning Environment in your work, we ask that you use
the following BibTeX entry:@software{Greaves_Balloon_Learning_Environment_2021,
  author = {Greaves, Joshua and Candido, Salvatore and Dumoulin, Vincent and Goroshin, Ross and Ponda, Sameera S. and Bellemare, Marc G. and Castro, Pablo Samuel},
  month = {12},
  title = {{Balloon Learning Environment}},
  url = {https://github.com/google/balloon-learning-environment},
  version = {1.0.0},
  year = {2021}
}If you use theble_wind_fielddataset, you should also citeHersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A.,
Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G.,
Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M.,
Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L.,
Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P.,
Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F.,
Villaume, S., Thépaut, J-N. (2017): Complete ERA5: Fifth generation of ECMWF
atmospheric reanalyses of the global climate. Copernicus Climate Change Service
(C3S) Data Store (CDS). (Accessed on 01-04-2021)
Package: ballotapi
BallotAPI ServerWARNING: THE CODE FOR THIS README HASN'T BEEN WRITTEN YETThis folder has the code for the BallotAPI server itself. If you
want to run your own mirror of our ballot API, this is the code
you are looking for.Installation1. Install the BallotAPI serverThe easiest way to install is viaPyPI:pipinstallballotapiTo update, just add the-Uflag.pipinstall-UballotapiAlternatively, you can install directly from source:gitclonehttps://github.com/open-austin/ballotapi.git
pipinstall-eballotapi/Alternatively, you can run from source directly:gitclonehttps://github.com/open-austin/ballotapi.gitcdballotapi
pipinstall-rrequirements.txt
python3-mballotapi.cli--help2. Setup your databaseThe BallotAPI server requires a database of ballot data be loaded.
You can download a copy of varioustest databases,
thefull production database,
orbuild your own.You can skip this step if you already have your database loaded.# Install PostgreSQL and PostGIS pluginsudoapt-getinstallpostgresqlpostgis# Create a BallotAPI database and usersudo-upostgrespsql-c"CREATE DATABASE ballotapi;"sudo-upostgrespsql-c"CREATE USER ballotapiuser WITH ENCRYPTED PASSWORD 'yourpasswordhere';"sudo-upostgrespsql-c"GRANT ALL PRIVILEGES ON DATABASE ballotapi TO ballotapiuser;"# Set database connection uri environmental variableexportBALLOTAPI_DB_URI="postgresql://ballotapiuser:yourpasswordhere@localhost:5432/ballotapi"# Load a dataset into your databaseballotapiload"testdata-default"UsageNow that the BallotAPI server is installed and a database is loaded,
you are ready to run the server!ballotapirunserverTo customize the settings of your server, check out which options
there are usingballotapi runserver --help.usage:ballotapirunserver[-h][--db-uriURI][--cache-uriURI][--hostHOST][--portPORT][--uwsgi-iniFILE][--daemon]optionalarguments:-h,--helpshowthishelpmessageandexit--db-uriURIconnectionuritothepostgresdatabase(defaultisBALLOTAPI_DB_URIenvvariable)--cache-uriURIconnectionuritoacacheserver(defaultisNone)--hostHOSTlistenforthishost(defaultlocalhost)--portPORTlistenonthisport(default1776)--uwsgi-iniFILEsettingsforuwsgi(defaultisasimplehttpserver)--daemondetachservertoruninbackgroundasadaemon(optional)To stop the server when you're running it directly from the command line,
simply useCtrl+C. To stop/reload a server that's running in the background
as a daemon, send signals to the process.# gracefully reloadkillall-sSIGHUPballotapi# gracefully stopkillall-sSIGTERMballotapi# brutally reloadkillall-sSIGINTballotapi# brutally stopkillall-sSIGKILLballotapiCommandsusage: ballotapi [-h] <subcommand> ...

Documentation: https://ballotapi.org/docs

optional arguments:
  -h, --help    show this help message and exit

available subcommands:
  <subcommand>
    runserver   Run the web server
    load        Load in a database from a source location
    export      Dump the database as a sql fileContributingHelp us out! If you find a bug or want to improving this codebase,
feel free to submit anissueorpull request.
Check out ourCONTRIBUTINGdocs for more details.
Package: ballpark
When people think of human-readable numbers, they think of rounding to
two decimal places and adding a thousands separator. 12,214.17 is
already quite an improvement over 12214.16666667. But standard formats
for human-readable numbers still have various flaws:even with a thousands separator, at a glance you might easily mistake
a billion for a trillioneven when rounding, an amount like 12,214.17 dollars is a lot of
number noise for communicating 12.2Kscientific notation leads to exponents like1.22e4which are hard
to interpret because we’re used to working with thousands, millions
and billions – orders of magnitudes that are multiples of threewhen comparing multiple measurements of the same underlying variable,
like the yearly sales numbers for 2010-2015, it’s annoying to have
some numbers in thousands and other numbers in millions – you want
consistency so that digits in the same position are of the same
magnitudepython-ballparkintroducesbusiness notation, an offshoot ofengineering
notation, for
producing better human-readable numbers.Install withpip install ballparkorpip3 install ballpark.What it looks likenumbersroundedengineering
notationbusiness
notation11234.22,
233000.55,
1175125.211,234.22,
233,000.55,
1,175,125.211.2E+3,
233E+3, 1.18E+611K, 233K,
1,180K111, 1111.23,
1175125.234111, 1,111.23,
1,175,125.23111, 1.11E+3,
1.18E+60.11K, 1.11K,
1,180.00KHow to use it>>>fromballparkimporthuman,scientific,engineering,business,ballpark>>>business([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># business notation is also aliased as `ballpark`>>>ballpark([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># or use the shortcut functions>>>fromballparkimportH,S,E,B>>>B([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># all notations accept single numbers too, but then we can't guarantee>>># that all numbers will have the same prefix (kilo, mega etc.)>>>[B(value)forvaluein[11234.22,233000.55,1175125.2]]['11.2K','233K','1.18M']How it worksbusiness(values,precision=3,prefix=True,prefixes=SI,statistic=median)precision:the amount of significant digits. When necessary,businesswill round beyond the decimal sign as well: in the
example above,1175125.2was turned into1,180Krather than1,175Kto retain only 3 significant digits.prefix:whether to use SI prefixes like m (milli), K (kilo) and
so on instead of scientific exponents like E+03.prefixes:a mapping of orders of magnitude to prefixes, e.g.{-3:'m', 3: 'K'}, allowing you to customize the prefixes, for
example using B for billion instead of T for tera.statistic:a function to produce the reference number. The
reference number determines the order of magnitude and precision for
the entire group of numbers, so that for example when the reference
number is 23.3K, smaller numbers like 1.1K won’t gain a decimal place
and larger numbers like 1,180K won’t jump an order of magnitude to
1.18M. The median often works well, but if you want more precision
for small outliers, tryballpark.statistics.Q1or even Python’s
builtinmin.
Package: ballparker
BallparkerA Python package for creating, maintaining, and manipulating task lists/trees
associated with "ballpark" estimation.Table of ContentsInstallUpgradingUsageMaintainersThanksCode of ConductContributingLicenseInstallPython 3.6 or newer is required.Ballparker is in PyPI, so you can install the latest released version as follows:python3-mpipinstall--userballparkerIf you want the latest source tree, instead of latest release, you can install
directly from the main Git repo as follows:python3-mpipinstall--usergit+https://gitlab.com/ryanpavlik/ballparker.gitAlternately, especially if you would like to participate in development, you can
install from a clone of this repository. It's recommended to install this in a
virtualenvespeciallyif you choose this install option. Something like this
will work on *nix-like systems, adjusted for your shell usage.# Create a virtualenv in `venv`python3-mvenvvenv# Activate it.venv/bin/activate# Install a link to this package in the venv.python3-mpipinstall--editable.If you follow those instructions, the virtualenv will refer to the source
instead of copying the source, so updating your local clone of this repo will
also update the virtualenv. Omit the--editableif you want other behavior.UpgradingIf you installed using pip and want to upgrade, you'll want to do one of the
following, depending on where you want to update from. You can even upgrade
between a packaged release and the latest Git, if you like.To upgrade to the latest tagged release:python3-mpipinstall--user--upgradeballparkerTo upgrade to the very latest code in Git:python3-mpipinstall--user--upgradegit+https://gitlab.com/ryanpavlik/ballparker.gitUsageYou will typically create a new Python file for each estimate you produce. You
can start it with the template inballpark_template.py.See the documentation forballparker.dslfor instructions on the ballpark
process this package was designed around.When preparing for a customer, do not forget to add project-managment overhead
to the estimated days!Maintainers@ryanpavlikThanksThis tool was initially developed by Ryan Pavlik in the course of his work at
the open source software consultancyCollabora. Thanks
to Collabora and their "Open First" mantra for supporting the development of
this software.Code of ConductPlease note that this project is released with a Contributor Code of Conduct. By
participating in this project you agree to abide by its terms.ContributingIf you have questions or found a bug, feel free to file an issue on the GitLab
project for Ballparker.LicenseThetl;dris: The software written for this package isApache-2.0licensed, while a few other files, including templates, are CC0-1.0 public
domain dedications. SeeCOPYING.mdfor details, and see individual
files for specifics.
Package: ballpark-fixed
When people think of human-readable numbers, they think of rounding to
two decimal places and adding a thousands separator. 12,214.17 is
already quite an improvement over 12214.16666667. But standard formats
for human-readable numbers still have various flaws:even with a thousands separator, at a glance you might easily mistake
a billion for a trillioneven when rounding, an amount like 12,214.17 dollars is a lot of
number noise for communicating 12.2Kscientific notation leads to exponents like1.22e4which are hard
to interpret because we’re used to working with thousands, millions
and billions â€“ orders of magnitudes that are multiples of threewhen comparing multiple measurements of the same underlying variable,
like the yearly sales numbers for 2010-2015, it’s annoying to have
some numbers in thousands and other numbers in millions â€“ you want
consistency so that digits in the same position are of the same
magnitudepython-ballparkintroducesbusiness notation, an offshoot ofengineering
notation, for
producing better human-readable numbers.Install withpip install ballparkorpip3 install ballpark.What it looks likenumbersroundedengineering
notationbusiness
notation11234.22,
233000.55,
1175125.211,234.22,
233,000.55,
1,175,125.211.2E+3,
233E+3, 1.18E+611K, 233K,
1,180K111, 1111.23,
1175125.234111, 1,111.23,
1,175,125.23111, 1.11E+3,
1.18E+60.11K, 1.11K,
1,180.00KHow to use it>>>fromballparkimporthuman,scientific,engineering,business,ballpark>>>business([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># business notation is also aliased as `ballpark`>>>ballpark([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># or use the shortcut functions>>>fromballparkimportH,S,E,B>>>B([11234.22,233000.55,1175125.2])['11K','233K','1,180K']>>>>>># all notations accept single numbers too, but then we can't guarantee>>># that all numbers will have the same prefix (kilo, mega etc.)>>>[B(value)forvaluein[11234.22,233000.55,1175125.2]]['11.2K','233K','1.18M']How it worksbusiness(values,precision=3,prefix=True,prefixes=SI,statistic=median)precision:the amount of significant digits. When necessary,businesswill round beyond the decimal sign as well: in the
example above,1175125.2was turned into1,180Krather than1,175Kto retain only 3 significant digits.prefix:whether to use SI prefixes like m (milli), K (kilo) and
so on instead of scientific exponents like E+03.prefixes:a mapping of orders of magnitude to prefixes, e.g.{-3:'m', 3: 'K'}, allowing you to customize the prefixes, for
example using B for billion instead of T for tera.statistic:a function to produce the reference number. The
reference number determines the order of magnitude and precision for
the entire group of numbers, so that for example when the reference
number is 23.3K, smaller numbers like 1.1K won’t gain a decimal place
and larger numbers like 1,180K won’t jump an order of magnitude to
1.18M. The median often works well, but if you want more precision
for small outliers, tryballpark.statistics.Q1or even Python’s
builtinmin.