Saravanan666/pypi-org huggingface dataset:
Package: boadata
(B)rowser (O)f (A)rbitrary Data - a Python browser of data.
The goal is to create a simple tool for scientists / data miners
to browse and plot various data sources in one simple environment.It’s a library, as well as a set of tools.Most focus is placed on table-like pandas-based objects.Conceptsuri - boadata tries to support odo URIsdata object - wrapper around basic data types (a.k.a odo.resource but with wrapper)data tree - browseable that can contain nodes (some of the nodes can be data objects)data conversion - between different object typesview - visual representation of a data objectsExecutablesRuncommand –helpto see full list of optionsboadescribe <uri>- show basic info about a data object (in command-line)boatree <uri>- list nodes in a data treeboaplot <uri> <colx> <coly>- scatter plot of two columns (colx,y can be expressions like x**2, sqrt(y+2*exp(-x)) etc.)boahist <uri> <col>- histogram of a columnboadata [<uri>]- full gui with a treeboatable <uri>- show tabular representation of a dataobject (GUI)boacat <uri>- print tabular representation of a dataobject (command-line)boaconvert <from+> <to>- convert one data source into anotherboaview <uri>- show a tabbed window with all available views for the dataobjectStatusin (slow) developmentRequirementsPyQt4 (to become optional / replaceable with PySide/PyQt5)pandasnumpynumexprclicktabulateblinker (perhaps to be removed)h5py (optional)sqlalchemy (optional)pyqtgraph (to be removed)matplotlib (to become optional)bokeh (to become optional instead of matplotlib)seabornpandas_profiling (optional - dataframe summaries)datadotworld (optional - for their datasets)Python 3.6+Supported formats and sourcesFile system treeHDF5CSV (including web links)SQL based on SqlAlchemy (sqlite supported)pydataset datasetsseaborn datasetsMATLAB .fig filesdata.world datasets/tables (that can be imported pandas dataframes)Supported GUI viewsTableHistogramScatter / line plotText viewSummary (from pandas_profiling)
Package: boa-framework
Bayesian Optimization for AnythingBOA is a high-level Bayesian optimization framework and model wrapping tool for providing an easy-to-use interface
between models and the python librariesAxandBoTorchKey featuresModel agnosticCan be used for models in any language (not just python)Can be used for Wrappers in any language (You don't even need to write any python!) SeeScript Wrapperfor details on how to do that.Simple to implement for new models, with minimal coding requiredScalableCan be used for simple models or complex models that require a lot of computational resourcesScheduler to manage individual model runsSupports parallelizationModular & customizableCan take advantages of the many features of Ax/BoTorchCustomizable objective functions, multi-objective optimization, acquisition functions, etcChoice of built-in evaluation metrics, but it’s also easy to implement custom metricsInstall requirementsseeinstallation guideDocsDOIConda InstallPyPI InstallGithub Latest releaseGithub dev releaseBuild StatusCoverage
Package: boa-lib
BoaA library of advanced system tools for Python.Boa adds upgraded versions of system tools. For example, this includes advanced pipes, parallelism, etc.
It is designed to have a simple Pythonic interface.To list all of the available packages, simply Python's interactive prompt and explore:>>> from Boa import *
>>> help(parallel)
Help on package Boa.parallel in Boa:

NAME
    Boa.parallel - This package contains some useful tools when working with multithreading or multiprocessing.

PACKAGE CONTENTS
    abc (package)
    exceptions
    process (package)
    thread (package)
...Some practical examples of the content of Boa include:Futures to manage eventual objects to come:>>> from Boa.parallel.thread import Future
>>> from threading import Thread
>>> f = Future()
>>> def show_future():
...         while True:
...                 print(f"Future got set to {f.result()}.")
...                 f.clear()
... 
>>> Thread(target = show_future).start()
>>> f.set("Hello")
Future got set to Hello.
>>> f.set(True)
Future got set to True.
>>> f.set_exception(ValueError("Time to stop"))
Exception in thread Thread-1 (show_future):
Traceback (most recent call last):
  File "C:\Program Files\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
     self.run()
  File "C:\Program Files\Python311\Lib\threading.py", line 975, in run
    self._target(*self._args, **self._kwargs)
  File "<stdin>", line 3, in show_future
  File "C:\Users\Vince\OneDrive\Documents\Python\Boa\Boa\parallel\thread\future.py", line 248, in result
    raise self.__exception from None
ValueError: Time to stopFutures are particularly useful in multithreaded environement for synchronization, especially with their special methods to cancel Futures, link them together, etc.Many parallelism decorators:>>> from Boa.parallel.thread import exclusive
>>> from time import sleep
>>> @exclusive
... def run():
...         print("Entering...")
...         sleep(5)
...         print("Exiting.")
... 
>>> from threading import Thread
>>> for i in range(5):
...         Thread(target = run).start()
... 
Entering...
>>> Exiting.
Entering...
Exiting.
Entering...
Exiting.
Entering...
Exiting.
Entering...
Exiting.This decorator and others allow you to ensure exclusion with precise rules accros a multithreaded program.Better signal handling:>>> from Boa.signal import *
>>> Signals.
Signals.SIGABRT           Signals.SIGILL            Signals.SIGTERM           Signals.bit_length(       Signals.from_bytes(       Signals.numerator
Signals.SIGBREAK          Signals.SIGINT            Signals.as_integer_ratio( Signals.conjugate(        Signals.imag              Signals.real
Signals.SIGFPE            Signals.SIGSEGV           Signals.bit_count(        Signals.denominator       Signals.mro()             Signals.to_bytes(
>>> Signals.SIGINT.add_handler(lambda s : print(f"Received signal {s.name}"))
>>> Received signal SIGINT

KeyboardInterruptAnd many other useful random features!Note that this library is extensively documented. Use Python's help system (for example the help function while in an interactive interpreter) to learn how to use all the modules and classes.
Package: boar
ReadmeDirty tricks to run python notebooksPurposeTesting an idea is sometimes more easily done by calling a python notebook in a python code.
It is as dirty, filthy and ugly as a wild hairy swine with horns...
But if you can prove the value of your idea, why not try it out immediatly withboar.Read more at./POST.mdInstallationRequirements:Python 3pipThe package is available on pypi:https://pypi.org/project/boar/To install, execute:pipinstallboarPracticeI used this method in CI tasks to make sure tutorials associated to a project stay up-to-date.I would not recommend relying this trick on a long term product.UsageMore information at./USAGE.md.ReferenceDocumentationhttps://boar.readthedocs.io/en/latest/Sourcehttps://github.com/alexandreCameron/boar
Package: boardex-salesforce
Introductionboardex_salesforce package connects to salesforce bulk api. Downloads contact and accounts data.Uploads the downloaded data to BoardEx SFTP for concordance.Uploads BoardExID to salesforce from the concordance results.RequirementsBoardEx app on salesforcePython 3Installationpipinstallboardex-salesforceOverviewboardex_salesforce package has pre-build modules. The two main ones are:run_dailyrun_monthlyUsagefromboardex_salesforceimportconfigconfig.username='username@salesforce.com'config.password='salesforcePassword'config.security_token='salesforce_security_token'run_dailyfromboardex_salesforceimportrun_dailyrun_daily.run_daily()This script calls the methods:download_ContactDetails_from_sfdownload_UserDetails_from_sfThey download all the user relationship and contact details and place them in a download folder.run_dailyfromboardex_salesforceimportrun_monthlyrun_monthly.run_monthly()Please note if sftp is not being used, a folder must be created with the name 'upload' and
place all the output csv files from BoardEx inside the upload folderThis script calls the methods:upload_to_sfdownload_company_from_sfdownload_contact_from_sfdownload_ContactDetails_from_sfdownload_UserDetails_from_sfupload_to_sf uploads all the contact and conpany csv OUT file to salesforceParameters accessible from configBoardex sftp credentialsThe sftp credentials are provided by BoardEx, leave empty if not providedconfig.myHostnameconfig.myPasswordconfig.myUsernameUpload to sftpTo upload to sftp server, sftp credentials are required and set upload_to_sftp = TrueBy default the upload_to_sftp is set to false. (The csv files will not be uploaded to the sftp server)config.upload_to_sftp=Falselog levelPython has 6 log levels: CRITICAL, ERROR, WARNING, INFO, DEBUG and NOTSETThe default log level is set to CRITICAL if not selectedIf log_level is NOTSET, no logs will be providedconfig.log_level='DEBUG'query_emailquery_email is a boolean if set to True downloads email address from salesforceIf set to False, Email data will not be retrieved from the contact_queryconfig.query_email=Truepath_local_tempSpecifies where to place the logs, download and upload folder on local machineconfig.config.path_local_temp
Package: boardgame
boardgame aims to gather several simple boardgames like TicTacToe or Connect 4Home-page: UNKNOWNAuthor: Surya AmbroseAuthor-email: surya.ambrose@gmail.comLicense: MITDescription:boardgame aims to gather several simple boardgames like TicTacToe or Connect 4Each game is described by a State and a Model. A State represents the currentstate of the game (obviously) but can also provide a value for that state. Thisvalue is helpful for AI playing the game. A Model represents the rules of thegame and is used to describe which moves are possible from a given state, andwhat would be their resulting states.Aside of those two elements is a Viewer, whose purpose is to display thecurrent state of the game and to handle human actions.Above all this, a Controller handles the state, the model, the player actionsreturned by the viewer or the AI actions to run the game. This Controller isgeneric and does not need to be overwritten for each game.Platform: UNKNOWNClassifier: Development Status :: 2 - Pre-AlphaClassifier: Environment :: ConsoleClassifier: Intended Audience :: DevelopersClassifier: Intended Audience :: End Users/DesktopClassifier: License :: OSI Approved :: MIT LicenseClassifier: Programming Language :: Python :: 2.7Classifier: Topic :: Games/Entertainment :: Board Games
Package: boardgame2
boardgame2boardgame2is an extension of OpenAI Gym that implements multiple two-player zero-sum 2-dimension board games, such as TicTacToe, Gomuko, and Reversi.EnvironmentsReversi-v0KInARow-v0, as well asGomuku-v0andTicTacToe-v0Go-v0(Experimental, not fully implemented)Installpip install --upgrade boardgame2We support Windows, macOS, Linux, and other operating systems.UsageSeeAPI docsfor all classes and functions.Create a Gameimport gym
import boardgame2

env = gym.make('TicTacToe-v0') # 3x3, 3-in-a-row
env = gym.make('Gomuku-v0') # 15x15, 5-in-a-row
env = gym.make('KInARow-v0', board_shape=5, target_length=4) # 5x5, 4-in-a-row
env = gym.make('KInARow-v0', board_shape=(3, 5), target_length=4) # 3x5, 4-in-a-row
env = gym.make('Reversi-v0') # 8x8
env = gym.make('Reversi-v0', board_shape=6) # 6x6
env = gym.make('Go-v0') # 19x19
env = gym.make('Go-v0', board_shape=15) # 15x15Play a Gameimport gym
import boardgame2

env = gym.make('TicTacToe-v0')
print('observation space = {}'.format(env.observation_space))
print('action space = {}'.format(env.action_space))

observation, info = env.reset()
while True:
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        break
env.close()BibTeXThis package has been published in the following book:@book{xiao2019,
 title     = {Reinforcement Learning: Theory and {Python} Implementation},
 author    = {Zhiqing Xiao}
 year      = 2019,
 month     = 8,
 publisher = {China Machine Press},
}
Package: board-game-kurtispykes
Board GamesTic-tac-toe, noughts and crosses, or Xs and Os/“X’y O’sies”, is a paper-and-pencil game for
two players, X and O, who take turns marking the spaces in a 3×3 grid. The player who
succeeds in placing three of their marks in a diagonal, horizontal, or vertical row is
the winner [Source:Wikipedia].The main purpose of the project was to become accustomed with packaging projects and
distributing them on PyPi.Note: Source code was inspired byTic Tac Toe Game - Python Tkinter GUI Tutorial
Package: board-game-recommender
🎲 Board Game Recommender 👍Board game recommendation engine. View the recommendations live atRecommend.Games! Install viapipinstallboard-game-recommenderTraining new recommender modelsEnvironmentRequires Python 3. Make surePipenvis installed and create the virtual environment:python3-mpipinstall--upgradepipenv
pipenvinstall--dev
pipenvshellDatasetsIn order to train the models you will need appropriate game and rating data.
You can either scrape your own using theboard-game-scraperproject or take a look at theBoardGameGeek guildto obtain existing datasets.At the moment there is only onerecommender implementations:BoardGameGeek.ModelsWe use the recommender implementation byTuri Create.
Two recommender models are supported out of the box:RankingFactorizationRecommender(default): Learns latent factors for each user and game, generally yielding
very interesting recommendations.ItemSimilarityRecommender:
Ranks a game according to its similarity to other ratings by a user, often
resulting in less interesting recommendations. However, this model is also
able to find games similar to a given game.Run the trainingRun the training via themain script:python-mboard_game_recommender--helpE.g., train the default BGG mode like so:python-mboard_game_recommender\--train\--games-filebgg_GameItem.jl\--ratings-filebgg_RatingItem.jl\--modelmodel/output/dirLinksboard-game-recommender:
This repositoryRecommend.Games: board game recommender websiterecommend-games-server:
Server code forRecommend.Gamesboard-game-scraper:
Board game data scraper
Package: board-game-scraper
🎲 Board Game Scraper 🕸Scraping data about board games from the web. View the data live atRecommend.Games! Install viapipinstallboard-game-scraperSourcesBoardGameGeek(bgg)DBpedia(dbpedia)Luding.org(luding)Spielen.de(spielen)Wikidata(wikidata)Run scrapersRequires Python 3. Make surePipenvis installed and create the virtual
environment:python3-mpipinstall--upgradepipenv
pipenvinstall--dev
pipenvshellRun a spider like so:JOBDIR="jobs/${SPIDER}/$(date--utc+'%Y-%m-%dT%H-%M-%S')"scrapycrawl"${SPIDER}"\--output'feeds/%(name)s/%(time)s/%(class)s.csv'\--set"JOBDIR=${JOBDIR}"where$SPIDERis one of the IDs above.Run all the spiders with therun_scrapers.shscript. Get a
list of the running scrapers' PIDs with theprocesses.shscript. You can close all the running scrapers via./processes.shstopand resume them later.TestsYou can runscrapy checkto perform contract tests for all spiders, orscrapy check $SPIDERto test one particular spider. If tests fails,
there most likely has been some change on the website and the spider needs
updating.Board game datasetsIf you are interested in using any of the datasets produced by this scraper,
take a look at theBoardGameGeek guild.
A subset of the data can also be found onKaggle.Linksboard-game-scraper:
This repositoryRecommend.Games: board game recommender using the
scraped datarecommend-games-server:
Server code forRecommend.Gamesboard-game-recommender:
Recommender code forRecommend.Games
Package: boardify
boardifyA web-based dashboard to analyzing your data with PythonInstallationpip3installboardifyUsagefromboardifyimportBaseDashboard,BaseBlock,BarChart# Class for the dashboardclassFruitDashboard(BaseDashboard):defconfig(self):self.name="Fruit Company"# Add Blocks to the dashboardself.add_block(Products)self.add_block(NicestFruit)# Block contains HTML CodeclassProducts(BaseBlock):defconfig(self):self.name="Products"self.description="A list of our products"defgenerate(self):return"""<ul><li>Apple</li><li>Orange</li><li>Banana</li><li>Kiwifruit</li><li>Blueberry</li><li>Grapes</li></ul>"""# Block renders a bar chartclassNicestFruit(BarChart):defconfig(self):self.name="Nicest Fruit"self.description=("A survey of 145 people asked them ""\"Which is the nicest fruit?\"")defdata(self):return{"datasets":{"Survey One":[35,20,45,10,30,5],"Survey Two":[30,25,60,5,35,0],},"labels":["Apple","Orange","Banana","Kiwifruit","Blueberry","Grapes"],"background_color":["rgb(128, 18, 2)","rgb(244, 132, 0)","rgb(246, 221, 0)","rgba(83, 65, 25, 0.97)","rgb(35, 123, 214)","rgb(99, 74, 161)"]}# Run the serverdashboard=FruitDashboard()dashboard.run()Open the dashboard athttp://localhost:7000Made withFlask- web frameworkChart.js- charts made with JavaScriptMetaLucas Hild -https://lucas-hild.deThis project is licensed under the MIT License - see the LICENSE file for details
Package: boardinator
Chess BoardinatorThis project generates a chess board image from a given input image by replacing specific colors with new ones. The output image is saved to a file specified by the user. The project supports PNG and SVG formats.Usageboardinator --show --output_path cool_image.png --color_dict '{"(237,238,209)":"(0,0,255)","(119,153,82)":"(0,0,0)"}'Arguments--input_path: path to the input image file (default:./assets/chesscom.png)--output_path: path to the output image file (required)--color_dict: dictionary of color replacements in string format (default:{})--show: flag to show the modified imageInstallationThis project requires Python 3.11 or later and can be installed usingPoetry.Clone the repository:git clone https://github.com/example/chess-boardinator.git
cd chess-boardinatorInstall dependencies using Poetry:poetry installRun the project:poetry run boardinator --show --output_path cool_image.png --color_dict '{"(237,238,209)":"(0,0,255)","(119,153,82)":"(0,0,0)"}'LicenseThis project is licensed under the MIT License. See theLICENSEfile for details.
Package: boardlib
BoardLib 🧗‍♀️Utilities for interacting with (undocumented) climbing board APIs.Installation 🦺python3 -m pip install boardlibUsage ⌨️Databases 💾To download the climb database for a given board:boardlib database <board_name> <database_path>This will download asqlitedatabase file to the given path and synchronize it with the latest available data. The database contains all of the publicly available climb data. Only the synchronization will be attempted if there is already a database at the given path,NOTE: The Moonboard is not currently supported for the database command. Contributions are welcome.Supported Boards 🛹AllAurora Climbingbased boards (Kilter, Tension, etc.).Logbooks 📚To download your logbook entries for a given board:boardlib logbook <board_name> --username=<username> --output=<output_file_name>.csv --grade-type="hueco"This outputs a CSV file with the following fields:["board","angle","name","date","grade","tries","is_mirror"]For example, the commandboardlib moon2017 --username="Luke EF" --output="moon2017.csv" --grade-type="hueco"would output a file namedmoon2017.csvwith the following contents:board,angle,name,date,grade,tries, is_mirror
moon2017,40,C3PO,2021-07-13,V5,1, False
moon2017,40,LITTLE BLACK SUBMARINE,2021-07-13,V5,2, False
moon2017,40,MOUNTAIN GOAT HARD,2021-07-13,V5,1, False
...Seeboardlib --helpfor a full list of supported board names and feature flags.Supported Boards 🛹Currently allAurora Climbingbased boards (Kilter, Tension, etc.) and all variations of theMoonboardshould be supported.Bugs 🐞 and Feature Requests 🗒️Please create an issue in theissue trackerto report bugs or request additional features. Contributions are welcome and appreciated.
Package: boardman
boardmanMicroPython deployment toolDevelopment$poetryconfig--localvirtualenvs.createfalse$poetryinstall$poetryconfigrepositories.testpypihttps://test.pypi.org/legacy/
$poetryconfigpypi-token.testpypi<token>$pipinstall-U--pre-ihttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simple/-U"boardman>0.1.0"
Package: boardom
# BoardomHigh-ish level toolbox, less boilerplate, PyTorch, loops, code, metrics, blah blah, deep learning, blah.A continuation of [pydlt](https://github.com/dmarnerides/pydlt).# InstallationInstall Dependencies`bash pip install rawpy portalocker tqdm lmdb aiohttp wrapt zmq msgpack numpy matplotlib scipy pandas ordered_set pylatex `Install torch and torchvision ([follow instructions here](https://pytorch.org/get-started/locally/))3. Install OpenCV. Sometimes it’s tricky to get right. This should probably be fine:`bash pip installopencv-python`4. Finally install boardom`bash pip install boardom `## ContactDemetris Marnerides:dmarnerides@gmail.com## NOTE(A portmanteau of Tensorboard and Visdom.)Not affiliated with [boardom.io](https://boardom.io/), but that might be what you were looking for!
Package: board-to-fen
board_to_fenPython package that converts digital chessboard image into Forsyth-Edwards notation (FEN) notationInstallationboard_to_fen is available on PyPI:$ pip3 install board_to_fenQuick Startfromboard_to_fen.predictimportget_fen_from_imageprint(get_fen_from_image(PATH_TO_CHESSBOARD_IMAGE))Note:The package uses tensorflow+keras and python-opencv API.
They are pretty heavy.Customizationget_fen_from_image takes has 3 arguments:image_path [required]end_of_row '/' by defaultblack_view False by default -> set True if chessboard is provided from black player perspectiveWeb version (currently may not work)Available at:https://board2fen.bieda.itTrainingFor training You would probably want to download the source code by cloning the repository:$ git clone https://github.com/mcdominik/board_to_fen.gitDownload training data from:I will supply url for data in the futureIn the main repository dir, run$ python3 ./board_to_fen/train_model.pyVersion historyjanuary 2023versions 0.0.17-25added simple board validationbug fixesWarningsImage has to be provided in neutral angle (white or black player's perspective).Image has to be square (~3% tolerance depending on image resolution).Image can't contain paddings, board borders etc. other than 64 squares (with pieces) itself.References:https://www.kaggle.com/datasets/koryakinp/chess-positions