Saravanan666/pypi-org huggingface dataset:
Package: mc-status-webhook
Minecraft server status webhook for DiscordPoll a server and send customizable webhook embed if the status changes.Requires Python 3.7+Table of contentsInstallationUsageCLI argumentsEnvironment variablesLicenseInstallationUsepythonon Windows instead ofpython3Update pip and dependencies$python3-mpipinstall--upgradepip
$python3-mpipinstall--upgradesetuptoolswheelInstall$python3-mpipinstall--upgrademc-status-webhookUsage$python3mc_status_webhook.py-u"<webhook url>"[options]or set the appropriate environment variables.SeeCLI argumentsandEnvironment variablesfor configuration.Example scripts are also provided in the 'examples' directory.
Getmsw-example.cmdfor Windows ormsw-example.shfor Linux and modify it to your needs.Note 1:It is strongly recommended to run mc-status-webhook on the same machine or local
network as the Minecraft server to avoid false notifications due to connection issues and wasting
bandwidth. In this case use the loopback address 127.0.0.1 or the local address of the machine.Note 2:When working with BungeeCord-based proxies, use-sor--full-statusto request
full status information, because BungeeCord does not respond to just ping. Note that this will
fetch more data such as MOTD, icon, version, sample players and will use more bandwidth if the
update time is set too low.CLI argumentsArgumentTypeDefaultRequiredDescription-H, --hoststr127.0.0.1NoServer address-p, --portint25565NoServer port-t, --update-timefloat10.0NoTime in seconds between checking the server status-i, --initial-statusboolNoSend a webhook with the server status on application start-s, --full-statusboolNoRequest full status info instead of just pinging (for BungeeCord-based proxies)-u, --webhook-urlstrYesDiscord webhook URL-d, --debugboolNoLog debug messages--online-colorstr30c030NoWebhook color hex when the server is online--offline-colorstrff4040NoWebhook color hex when the server is offline--online-thumb-urlstr<online icon>NoWebhook thumbnail URL when the server is online ('none' = disabled)--offline-thumb-urlstr<offline icon>NoWebhook thumbnail URL when the server is offline ('none' = disabled)--status-titlestrStatusNoWebhook status title--status-online-valuestrOnlineNoWebhook status value when the server is online--status-offline-valuestrOfflineNoWebhook status value when the server is offline--address-titlestrAddressNoWebhook address title--address-valuestrNoWebhook address value when the server is onlineEnvironment variablesEnvironment variableEquivalent argumentMSW_HOST-H, --hostMSW_PORT-p, --portMSW_WEBHOOK_URL-u, --webhook-urlMSW_ONLINE_COLOR--online-colorMSW_OFFLINE_COLOR--offline-colorMSW_ONLINE_THUMB_URL--online-thumbnail-urlMSW_OFFLINE_THUMB_URL--offline-thumbnail-urlMSW_STATUS_TITLE--status-titleMSW_STATUS_ONLINE_VALUE--status-online-valueMSW_STATUS_OFFLINE_VALUE--status-offline-valueMSW_ADDRESS_TITLE--address-titleMSW_ADDRESS_VALUE--address-valueLicenseMIT license. SeeLICENSEfor more information.
Package: mcstructure
mcstructureRead and write Minecraft.mcstructurefiles.🌍 This README is also available in the following
languages:🇨🇳 Chinese🇩🇪 GermanIn the entire project (and officially since
the "Better Together Update") the term
"Minecraft" refers to the edition of Minecraft
that is also known as "Bedrock Edition".Features that this library provide are only
useful for the above named edition of Minecraft.WarningThis project is currently in theBETAversion. Some
features may not work as expected.This library lets you programmatically create
and edit Minecraft structures. You are able to
save these as.mcstructurefiles and for
example use them in behavior packs.You may as well read them to identify blocks and
and entities that were saved with a Structure
Block in-game.InstallationpipinstallmcstructureBasic UsagefrommcstructureimportBlock,Structurestruct=Structure((7,7,7),Block("minecraft:wool",color="red"))(struct.set_block((1,1,1),Block("minecraft:grass")).set_block((2,2,2),Block("minecraft:grass")).set_block((3,3,3),Block("minecraft:grass")).set_block((4,4,4),Block("minecraft:grass")).set_block((5,5,5),Block("minecraft:grass")).set_block((6,6,6),Block("minecraft:grass")))withopen("house.mcstructure","wb")asf:struct.dump(f)withopen("house.mcstructure","rb")asf:struct=Structure.load(f)Useful Links👋 Introduction to Structure Blocks📖 Bedrock Wiki📖 Documentation📁 Source Code🐍 PyPINOT AN OFFICIAL MINECRAFT PRODUCT.
NOT APPROVED BY OR ASSOCIATED WITH MOJANG.
Package: mctc-hackathon-robot297
Python Examples for API InteractionsHere some basic python examples that you can leverage on how to interact with U.S. Bank's apis.To see the full list of APIs available, you can read the documentationhere.Set UpIf you're usingPyCharm, make sure that you have a virtual environment so that we can install
python packages specific to your project.If you're using another tool likeVisual Studio Code, make sure that you create a virtual
environment for your project.Once you have your project open, make sure you install the following libraries usingpip:requestspython-dotenvYou can also install both of these running the commandpip install -r requirements.txt(assuming you're using this project).Developer NotesTo see if your code is using industry standard naming conventions, you can runpylint *.py */*.pyto run the linter and have the code rated.Build Library LocallyTo build this library locally you can run the commandpython -m buildto generate a.whlfile in thedist/folder.If you want to use this library for your local python project, you can install it via pip by runningpip install --index-url https://test.pypi.org/simple/ --no-deps mctc-hackathon-robot297.
Package: mcthings
McThingsA Python programming framework for building a 3D World of Scenes in Minecraft (ProceduralCSG).
Scenes are compositions of Things (Python objects), created and transformed in memory and rendered
using theRaspberry PI Minecraftrendererimplemented using theAPI(which also works inMinetest).
This renderer is based
onmcpi library. More renderers are planned. It follows the pipeline: create and transform in memory (model in memory) and then render.This is the reference notebookwith a complete sample. And there is aintro video tutorialanda more complete one.A Thing is a built based on blocks (voxels based on cubes):Pyramid,River,House,Fenceand may others. All the Things share theThing API.
A Thing can bedecoratedusing existing decorators likeLightDecoratoror you can create your own one. Adecorated house.
Scenes can also be decoratedlike this samplewith a railway (BorderDecorator) around a Scene.And Things can also be rotated. For example, in this scenethe castle is rotated
180 degreesso the portal is accessible from the town ways.There is also a repository for experimental, incubating or with extra dependencies Things
atMcThings Extra.AWorldis a list of Scenes placed in concrete positions.
And aScene is a listof Things built in a specific position and order. Scenes can be shared
loading and saving them to files. Scenes can be also saved as Schematics
and converted withMinewaysto be used for3D rendering and printing.
Youcan share scenes adding them
to this repository.
And they can beinteractiveasin this app.This sceneincludes
a river, a house in each side of the river and a bridge for crossing the river.Things can be built usingMinecraftDrawing.SphereandCircleThings are used with Pyramids in the nextscene:And Things can also be built fromSchematics(there are thousands!).
There is asample notebook.And Things can also be created from MagicaVoxel models.MinecraftandMinetestforums pages.
Package: mctinctools
README.mdBase file using best practicespytest for testingprecommitblack formattingflake lintingincludes bandit for security scanningmypy - type testingmkdocs for automatic document preparation based on codenox for automatic testing and building (python version of tox)scriv for automatic changelog generationManual commandsBuild requirements.txt from poetrypoetryexport-frequirements.txt>requirements.txtBuild changelog data.sciv createEdit file created.edit changelog.d/AUTOGENERATEDFILENAME
add what you changedCollect the changelog to update CHANGELOG.rstscriv collectBuild package for upload to pypi.Get pypi authentication ready prior to publishUpdate version number in pyproject.toml prior to buildingadd env variables
put secret in env variables with
$POETRY_HTTP_BASIC_PYPI_TOKEN
export POETRY_HTTP_BASIC_PYPI_TOKEN=PYPIAPIGOESHERE
poetry config pypi-token.pypi $POETRY_HTTP_BASIC_PYPI_TOKEN
poetry build
poetry publish
Package: mc-tk
mc-tkA Monte-Carlo toolkit for educational purposes.pip install mc-tkpackage architectureexperiments
    - classical / typical experiments in probability

distributions 
    - inclulde MC experiments that produce common distributions

samplings
    - sampling distributions of statistic used in hypothesis testsmodules and classesModuleClassDescriptionmc.experimentsPiPerform Buffon’s needle experiment to estimate π .ParcelSimulate a bi-directional parcel passing game.DicesEstimate the probabilities of various dice combinations.Prisoners
                asymptotic_analysis()The famous locker puzzle(100-prisoner quiz). And the asymptotic_analysis() function will prove that the survival chance limit is 1−ln2 when n approaches +∞ .Galton_BoardUse the classic Galton board experiment to produce a binomial distribution.Paper_ClipsUse the paper clip experiment to produce a Zipf distribution.Sudden_DeathThis class simulates a sudden death game to produce the exponential distribution.mc.distributionsPoissonThis class will demonstrate that Poisson is a limit distribution of b(n,p) when n is large, and p is small.BenfordVerify Benford’s law using real-life datasets, including the stock market data, international trade data, and the Fibonacci series.mc.samplingsCltUsing various underlying distributions to verify the central limit  theorem. This class provides the following underlying distributions.’uniform’ - a uniform distribution U(-1,1).’expon’- an exponential distribution Expon(1).’poisson’ - poisson distribution π(1).’coin’- Bernoulli distribution with p = 0.5.’tampered_coin’ - PMF:{0:0.2,1:0.8}, i.e., head more likely than tail.’dice’- PMF:{1:1/6,2:1/6,3:1/6,4:1/6,5:1/6,6:1/6}.’tampereddice’ - PMF: {1:0.1,2:0.1,3:0.1,4:0.1,5:0.1,6:0.5},i.e., 6 is more likely.T_TestThis class constructs an r.v.  (random variable) following the t distribution.Chisq_Gof_TestVerify the statistic used in Pearson’s Chi-Square Goodness-of-Fit test follows the χ2  distribution.Fk_TestVerify the Fligner-Killeen Test statistic(FK) follows the χ2  distribution.Bartlett_TestVerify the Bartlett’s test statistic follows the χ2  distribution.AnovaVerify the statistic of ANOVA follows the F distribution.Kw_TestVerify the Kruskal-Wallis test statistic (H) is a χ2  r.v.Sign_TestFor the sign test (medium test), verify its N- and N+ statistics both follow b(n,1/2).Cochrane_Q_TestVerify the statistic T in Cochrane-Q test follows the χ2 distribution.Hotelling_T2_TestVerify the T2  statistic from two multivariate Gaussian populations follows the Hotelling’s T2  distribution.This version is major upgrade on the original version.All the functions were refactored by the OOP (Object Oriented Programming) pattern.McBaseacts as a common base class for all MC derivative classes.future plangui.py - add a Flask or tk-inter (ttkbootstrap) GUI
Package: mct-quantizers
Model Compression Toolkit (MCT) QuantizersThe MCT Quantizers library is an open-source library developed by researchers and engineers working at Sony Semiconductor Israel.It provides tools for easily representing a quantized neural network in both Keras and PyTorch. The library offers researchers, developers, and engineers a set of useful quantizers, along with a simple interface for implementing new custom quantizers.High level description:The library's quantizers interface consists of two main components:QuantizationWrapper: This object takes a layer with weights and a set of weight quantizers to infer a quantized layer.ActivationQuantizationHolder: An object that holds an activation quantizer to be used during inference.Users can set the quantizers and all the quantization information for each layer by initializing the weights_quantizer and activation_quantizer API.Please note that the quantization wrapper and the quantizers are framework-specific.Quantizers:The library provides the "Inferable Quantizer" interface for implementing new quantizers.
This interface is based on theBaseInferableQuantizerclass, which allows the definition of quantizers used for emulating inference-time quantization.On top ofBaseInferableQuantizerthe library defines a set of framework-specific quantizers for both weights and activations:Keras QuantizersPytorch QuantizersThe mark_quantizer DecoratorThe@mark_quantizerdecorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:QuantizationTarget: An Enum that indicates whether the quantizer is intended for weights or activations quantization.QuantizationMethod: A list of quantization methods (Uniform, Symmetric, etc.).identifier: A unique identifier for the quantizer class. This is a helper property that allows the creation of advanced quantizers for specific tasks.Getting StartedThis section provides a quick guide to getting started. We begin with the installation process, either via source code or the pip server. Then, we provide a short example of usage.InstallationFrom PyPi - mct-quantizers packageTo install the latest stable release of MCT Quantizer from PyPi, run the following command:pip install mct-quantizersIf you prefer to use the nightly package (unstable version), you can install it with the following command:pip install mct-quantizers-nightlyFrom SourceTo work with the MCT Quantizers source code, follow these steps:git clone https://github.com/sony/mct_quantizers.git
cd mct_quantizers
python setup.py installRequirementsTo use MCT Quantizers, you need to have one of the supported frameworks, Tensorflow or PyTorch, installed.For use with Tensorflow, please install the following package:tensorflow,For use with PyTorch, please install the following package:torchYou can also use therequirementsfile to set up your environment.LicenseApache License 2.0.
Package: mct-quantizers-nightly
Model Compression Toolkit (MCT) QuantizersThe MCT Quantizers library is an open-source library developed by researchers and engineers working at Sony Semiconductor Israel.It provides tools for easily representing a quantized neural network in both Keras and PyTorch. The library offers researchers, developers, and engineers a set of useful quantizers, along with a simple interface for implementing new custom quantizers.High level description:The library's quantizers interface consists of two main components:QuantizationWrapper: This object takes a layer with weights and a set of weight quantizers to infer a quantized layer.ActivationQuantizationHolder: An object that holds an activation quantizer to be used during inference.Users can set the quantizers and all the quantization information for each layer by initializing the weights_quantizer and activation_quantizer API.Please note that the quantization wrapper and the quantizers are framework-specific.Quantizers:The library provides the "Inferable Quantizer" interface for implementing new quantizers.
This interface is based on theBaseInferableQuantizerclass, which allows the definition of quantizers used for emulating inference-time quantization.On top ofBaseInferableQuantizerthe library defines a set of framework-specific quantizers for both weights and activations:Keras QuantizersPytorch QuantizersThe mark_quantizer DecoratorThe@mark_quantizerdecorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:QuantizationTarget: An Enum that indicates whether the quantizer is intended for weights or activations quantization.QuantizationMethod: A list of quantization methods (Uniform, Symmetric, etc.).identifier: A unique identifier for the quantizer class. This is a helper property that allows the creation of advanced quantizers for specific tasks.Getting StartedThis section provides a quick guide to getting started. We begin with the installation process, either via source code or the pip server. Then, we provide a short example of usage.InstallationFrom PyPi - mct-quantizers packageTo install the latest stable release of MCT Quantizer from PyPi, run the following command:pip install mct-quantizersIf you prefer to use the nightly package (unstable version), you can install it with the following command:pip install mct-quantizers-nightlyFrom SourceTo work with the MCT Quantizers source code, follow these steps:git clone https://github.com/sony/mct_quantizers.git
cd mct_quantizers
python setup.py installRequirementsTo use MCT Quantizers, you need to have one of the supported frameworks, Tensorflow or PyTorch, installed.For use with Tensorflow, please install the following package:tensorflow,For use with PyTorch, please install the following package:torchYou can also use therequirementsfile to set up your environment.LicenseApache License 2.0.
Package: mc-transformers
mc_transformersCode to run experiments over Multiple Choice QA with huggingface/transformers.
A big part of the code comes fromhuggingface/transformers, so its license may apply (Apache v2).Codemc_transformers.py: Code to train/eval/test models over any collection with transfomersutils_mc.py: Contains processors specific to each MC QA collection (RACE, SWAG, EntranceExams…)WhyAs I experiment with more MC collection and training modes (i.e.: tpu), support for more collections or more models is required. Instead of forking the whole transformers library I do it here.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-07-23)First release on PyPI.
Package: mc_trimmer
MC-TrimmerAllows for fast, UI-less trimming of minecraft worlds.Usagemctrimmer[-h][-b[BACKUP_DIR]]-iINPUT_DIR[-o[OUTPUT_DIR]][-p[THREADS]]-c{inhabited_time<15s,...}Trimaminecraftdimensionbasedonper-chunkcriteria.v0.1.0

options:-h,--helpShowthishelpmessageandexit.-b/--backup[BACKUP_DIR]Backupregionsaffectedbytrimmingtothisdirectory.Defaultsto'./backup'-i/--input-regionINPUT_DIRDirectorytosourcethedimensionfilesfrom.Ifnooutputdirectoryisspecified,in-placeeditingwillbeperformed.-o/--output-region[OUTPUT_DIR]Directorytostorethedimensionfilesto.Ifunspecified,in-placeeditingwillbeperformedbytakingtheinputdirectoryinstead.-p/--parallel[THREADS]Parallelizethetask.Ifnothreadcountisspecified,thenumberofcpucores-1istakeninstead.-c/--criteria{inhabited_time<15s,inhabited_time<30s,inhabited_time<1m,inhabited_time<2m,inhabited_time<3m,inhabited_time<5m,inhabited_time<10m}Pre-definedcriteriabywhichtodetermmineifachunkshouldbetrimmedornot.BenchmarkConditions:OS:                         Windows10 64bit
CPU:                        AMD 3700x
SSD:                        Corsair MP510 2TB
Total file size processed:  1.05 GB
Total output size:          436 MB
Total files:                120 region files, 120 entities filesCommand being run:Measure-Command {mctrimmer -i"./test_in"-o"%appdata%/.minecraft/saves/test"-b"./tests/test_backup"-c"inhabited_time<30s"-p}Results:TotalSeconds      : 3.3509565
TotalSeconds      : 4.4760565
TotalSeconds      : 3.3780054
TotalSeconds      : 3.4098966
TotalSeconds      : 4.0146584
TotalSeconds      : 3.7998296
TotalSeconds      : 3.8248743
TotalSeconds      : 3.9705653
Package: mcts
MCTSThis package provides a simple way of using Monte Carlo Tree Search in any perfect information domain.InstallationWith pip:pip install mctsWithout pip: Download the zip/tar.gz file of thelatest release, extract it, and runpython setup.py installQuick UsageIn order to run MCTS, you must implement aStateclass which can fully describe the state of the world.  It must also implement four methods:getPossibleActions(): Returns an iterable of all actions which can be taken from this statetakeAction(action): Returns the state which results from taking actionactionisTerminal(): Returns whether this state is a terminal stategetReward(): Returns the reward for this state.  Only needed for terminal states.You must also choose a hashable representation for an action as used ingetPossibleActionsandtakeAction.  Typically this would be a class with a custom__hash__method, but it could also simply be a tuple or a string.Once these have been implemented, running MCTS is as simple as initializing your starting state, then running:frommctsimportmctsmcts=mcts(timeLimit=1000)bestAction=mcts.search(initialState=initialState)Seenaughtsandcrosses.pyfor a simple example.Slow Usage//TODOCollaboratingFeel free to raise a new issue for any new feature or bug you've spotted. Pull requests are also welcomed if you're interested in directly improving the project.
Package: mcts-catherineannie13
Monopoly MCTS AIMonopoly MCTS AI is a Python package that implements the Monte Carlo Tree Search (MCTS) algorithm for decision-making in simulated Monopoly games. It utilizes neural networks for strategy optimization and state evaluation, providing a framework for researching AI-driven decision-making in board games.FeaturesImplementation of the Monte Carlo Tree Search (MCTS) algorithm.Use of neural networks (NN) for evaluating game states and making decisions.Simulation of Monopoly game dynamics including properties, stations, utilities, and player interactions.Customizable strategies for AI players.InstallationOption 1Clone this repository to your local machine:bashCopycode
gitclonehttps://github.com/catherineannie13/Capstone-Optimising-Monopoly-Gameplay-Strategies.gitcdmonopoly-mcts-aiEnsure you have Python 3.8 or later installed. It's recommended to use a virtual environment:bashCopycode
python-mvenvvenvsourcevenv/bin/activate# On Windows use `venv\Scripts\activate`Install the required dependencies:bashCopycode
pipinstall-rrequirements.txtOption 2Run pip install -ihttps://pypi.org/simple/mcts-catherineannie13==0.0.1UsageTo run a simulation of the Monopoly game with the MCTS AI, execute the following command in the root directory of the project:bashCopycode
python-mMonopolyBoardMCTSYou can customize the simulation parameters within theMonopolyBoardMCTS.pyscript or by modifying the command-line interface (if implemented) to adjust the number of games, AI strategies, and other settings.ContributingContributions to the Monopoly MCTS AI project are welcome. Please follow the standard fork-and-pull request workflow on GitHub. Ensure you write or update tests as necessary.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.AcknowledgmentsThis project is inspired by the classic board game Monopoly.Monte Carlo Tree Search algorithm for strategic decision-making.Neural network implementation for state evaluation.
Package: mctspy
mctspy : python implementation of Monte Carlo Tree Search algorithmBasic python implementation ofMonte Carlo Tree Search(MCTS) intended to run on small game trees.Installationpip3 install mctspyRunning tic-tac-toe exampleto run tic-tac-toe example:importnumpyasnpfrommctspy.tree.nodesimportTwoPlayersGameMonteCarloTreeSearchNodefrommctspy.tree.searchimportMonteCarloTreeSearchfrommctspy.games.examples.tictactoeimportTicTacToeGameStatestate=np.zeros((3,3))initial_board_state=TicTacToeGameState(state=state,next_to_move=1)root=TwoPlayersGameMonteCarloTreeSearchNode(state=initial_board_state)mcts=MonteCarloTreeSearch(root)best_node=mcts.best_action(10000)Running MCTS for your own 2 players zero-sum gameIf you want to apply MCTS for your own game, its state implementation should derive frommmctspy.games.common.TwoPlayersGameState(lookupmctspy.games.examples.tictactoe.TicTacToeGameStatefor inspiration)
Package: mcts-solver
mcts-solverMonte-Carlo tree search solver for chess-ant.This is a part ofChess-Antproject and based on pseudocode from the following paper:Winands, Mark & Björnsson, Yngvi & Saito, Jahn-Takeshi. (2008). Monte-Carlo Tree Search Solver. 25-36. 10.1007/978-3-540-87608-3_3.