Saravanan666/pypi-org huggingface dataset:
Package: mc-players
Minecraft Server Player ListQuick script that generates a list of recent players on your server, sorted
in order of who last logged in. Works by looking at the last modified time of
theirplayer.datfiles in<server root>/world/playerdata, and resolving
those UUIDs to a playername using Mojang's API.CachingOptionally, you can write out the usernames to a cache file (to prevent API rate
limiting) using the--cache-fileoption. Usernames will only be looked up
again after 120s.Usage$ mc_players -h
usage: mc_players [-h] [--out OUT] [--cache-file CACHE_FILE] [--cache-expiry CACHE_EXPIRY] [--html] [-n N] [--servername SERVERNAME] worldpath

Generate a list of recent players on a minecraft server.

positional arguments:
  worldpath             Path to the world folder to scan (eg <server_root>/world)

options:
  -h, --help            show this help message and exit
  --out OUT             Path to output file, defaults to stdout
  --cache-file CACHE_FILE
                        Path to cache file (created if not exists) to prevent rate limiting
  --cache-expiry CACHE_EXPIRY
                        Look up usernames that haven't been looked up in n seconds
  --html
  -n N                  Only return the last n usernames, default all
  --servername SERVERNAME
                        Server name for use in outputExample OutputPlain Text$python3playerlist.py/opt/mc/worldPlayers last seen on server=================================================1                Bob    Mon Oct 05 2020, 11:01 PM2                Dan    Mon Oct 05 2020, 10:28 PM3             George    Mon Oct 05 2020, 03:06 PMHTML$ python3 playerlist.py  --html /opt/mc/world<html><head><!-- generated with github.com/jaydenmilne/minecraft-server-player-list --><title>server players</title><metaname='viewport'content='width=device-width, initial-scale=1.0'></head><body><h1>Players on server</h1><table><tr><th>Place</th><th>Player</th><th>Last Seen</th></tr><tr><td>1<td>Bob</td><td>Tue Oct 06 2020, 07:33 PM</td></tr><tr><td>2<td>Dan</td><td>Tue Oct 06 2020, 07:06 PM</td></tr><tr><td>3<td>George</td><td>Tue Oct 06 2020, 06:39 PM</td></tr></table></body></html>
Package: mc-plugin-helper
mc-plugin-helperMinecraft plugin helper, updates and checks versions of all plugins on a server!Project in developing! Please do not use it!At now implemented only output of plugins names.FeaturesNice and powerfuldocumentation!Easy management plugins in simple commands!Easy readable and supportable code!Support for Spigot.org plugins!Installationpipinstallmc-plugin-helperExampleCheck updates for all plugins:>mc-plugin-helpercheckall.

Plugin:AuthMe
Plugin:ClearLag
Plugin:CMI
Plugin:CMILib
Plugin:CoreProtect
Plugin:FastAsyncWorldEdit
Plugin:Geyser-Spigot
Plugin:LuckPermsThanksCreditsThis project was generated withautodonate-plugin-template.
Current template version is:cc64ede4f27ca8e272bff0a42d3950d26bcacb9a.
See what isupdatedsince then.
Package: mcp-persor
mcp_persormocopiで作成したBVHファイルを解析するパッケージです。インストールpipinstallgit+https://github.com/SatooRu65536/mcp_persor.git

or

pipinstallmcp-persor使い方インポートfrommcp_persorimportBVHparserファイルを読み込むbvhp=BVHparser('path/to/bvh/file')Dataframe として取得するmotion_df=bvhp.get_motion_df()LICENSEMIT
Package: mc-providers
Media Cloud Providers LibraryA package of search providers for Media Cloud, wrapping up interfaces for different social media platform.UNDER CONSTRUCTION- Probably won't get a huge amount of attention for a little bit, but I'm putting this
up now since I've done this extraction already.Install with pip (pip install .) and theinstall.shscript.Requires environment variables set for various interfaces to work correctly.BuildMake surepip install flit twineso you can build and deploy to PyPI.Bump the version number inpyproject.tomlAdd a note about changes to the version history belowCommit the changes and tag it with a semantic version numberRunflit buildto create an installation packageRunflit publishto upload it to PyPIVersion Historyv0.5.3- Temporary fix to onlinenews-mediacloud search handlingv0.5.3- Tweaks to onlinenews-mediacloud for compatibility with new database patternv0.5.2- Fix to allow override of chunk'ing in MC clientv0.5.1- Fix use of media cloud to respect domains clause on story list pagingv0.5.0- Integrate new mediacloud-news-client into onlinenews-mediacloudv0.4.0- Specify custom base URLs via new string param toprovider_by_nameandprovider_forv0.3.0- Add support for paging through stories directly, and including text in paged results for speedv0.2.6- Fixed querying by domain on new mediacloud systemv0.2.5- Alignment with new mediacloud system. Old onlinenews provider is now "onlinenews-mclegacy", "onlinenews-mediacloud" now queries the new index.v0.2.4- Added support for api keys via "provider_by_name"v0.2.3- removed support for API keys in environment variables- now expected as an argument inproviders.provider_forv0.2.2- transition to use the dedicated mediacloud-api-legacy package to avoid version conflictsgitv0.2.1- add in a date hack to resolve a lower-level bug in the Media Cloud legacy count-over-time resultsv0.2.0- add in support for Media Cloud legacy databasev0.1.7- corrected support for a "filters" kwarg in online_newsv0.1.6- Added support for a "filters" kwarg in online_newsv0.1.5- Added politeness wait to all chunked queries in twitter providerv0.1.4- Added Query Chunking for large collections in the Twitter providerv0.1.3- Added Query Chunking for large queries in the onlinenews providerv0.1.2- Test Completenessv0.1.1- Parity with web-search module, and language modelv0.1.0- Initial pypi upload
Package: mcpt
mcpt: Monte Carlo permutation tests for Pythonmcptis a Python 3 library for calculating p-values through Monte Carlo permutation tests, providing an intuitive, simple, and highly customisable interface to determining statistical significance.To get started, we recommend you read through Installation, Quickstart, and Functions sections of ourread the docs documentation. Also check out theFAQ, which we update regularly. If you have concerns about the software, or feel that there is something that should be more explicit, then we’d love to hear from you –please open an issue on Githuband we’ll get back in touch ASAP.If you usemcptin your research, please support us by citing the initial release:David J. Skelton. (2019, September 5). mcpt: Monte Carlo permutation tests for Python (Version 0). Zenodo.http://doi.org/10.5281/zenodo.3387528TLDR;InstallationThe simplest way to install this package is directly from PyPI using pippip install mcptUsagemcptcontains two main functions:mcpt.permutation_testandmcpt.correlation_permutation_test.Below is an example of themcpt.permutation_test- for more info, please see the documentationhere>> import mcpt
>> x = [10, 9, 11]
>> y = [12, 11, 13]
>> f = "mean"
>> n = 100_000
>> side = "lower"

>> result = mcpt.permutation_test(x, y, f, side, n=n)
>> print(result)
Result(lower=0.09815650454064283, upper=0.10305649415095638, confidence=0.99)Below is an example ofmcpt.correlation_permutation_test- for more information, please see the documentationhere>> import mcpt
>> x = [-2.31, 1.06, 0.76, 1.38, -0.26, 1.29, -1.31, 0.41, -0.67, -0.58]
>> y = [-1.08, 1.03, 0.90, 0.24, -0.24, 0.76, -0.57, -0.05, -1.28, 1.04]
>> side = "both"
>> f = "pearsonr"

>> result = mcpt.correlation_permutation_test(x, y, f=f, side=side)
>> print(result)
Result(lower=0.021282451892029475, upper=0.029347445354757373, confidence=0.99)
Package: mcptbr
mcptbrmcptbr é uma pequena biblioteca que permite o usuário a manipular blocos do Minecraft com python.Para quem serve?Material criado para os alunos do curso de programação em python no Minecraft.Observação: Versão em português de outras bibliotecas já existentes.InstalaçãoVá ao CMD e digitepip install mcptbr.Exemplo de usofrom mcptbr.model import *


print(pegarPosicaoJogador())
Package: mcpw
McStas Python Wrapper (mcpw)ContentWhy jet an other python wrapperInstallationKey FunctionalitesJupyter NotebookCommand UsageFunction overviewChange logQuick startRequirementsmcstasgccnumpymatplotlibInstalatlionInstall withpip install mcpwFirst setupEnter the directory with your mcstas instrumentrunmcpw_setup -I 'your_instrument.instr' -m 'path_to_mcstas_executable'Final steprunmcpw_manager -p 'your_instrument.py' fullFor more detailed instructions see the Install section and the rest of the Wiki at github.
Package: mc.py
mc.pymc.pyis a tiny and trivial Python package which provides you
a simple way to generate phrases using Markov chains.Docs can be foundhere.InstallationJust install it usingpipor any other package manager you use...
Should I seriously teach you this?pip install mc.pySimple usage exampleMore examples can be foundhere.importmcfrommc.builtinimportvalidatorsgenerator=mc.PhraseGenerator(samples=["hello world","world of cuties","bruh"])phrase=generator.generate_phrase(validators=[validators.words_count(minimal=4)])print(phrase)# >>> "hello world of cuties"LinksDocumentationExamplesPackage at PyPi
Package: mc-pyority-queue
Map colonies - priority queue management packageOverviewPyority-Queue - python priority queue package provides ability to manage queue by handle tasks statuses and report tasks liveness.Installation:From PyPi$ pip install mc-pyority-queueGetting StartedImport & Initializefrom mc-pyority-queue.task_handler import TaskHandler

task_handler = TaskHandler('job_type', 'task_type',
    'http://localhost:8081', 'http://localhost:8080/heartbeat', 1.0)above example uses TaskHanler class and initializing it follow the request params:task_handler = TaskHandler(job_type, task_type, job_mngr_url, heartbeat_url, interval_ms, logger)Dequeuefrom mc-pyority-queue.task_handler import TaskHandler

async def main():
    task_handler = TaskHandler('job_type', 'task_type',
    'http://localhost:8081', 'http://localhost:8080/heartbeat', 1.0, logger_instance)

    await task_hanlder.dequeue(interval_ms)


loop = asyncio.get_event_loop()
task = loop.run_until_complete(main())consume task from the job manager service and start send heartbeat to the heartbeat serviceRejectawait task_hanlder.reject(job_id, task_id, is_recoverable, reason)reject handle error by stop sending task's heartbeat and handle task's status - depends if task is recoverable or not.ackawait task_hanlder.ack(job_id, task_id)ack handle completed task - stops sending task's heartbeat and handle complete task statusupdate_progressawait task_hanlder.update_progress(job_id, task_id, percentage)handle task progress - sends job manager updated percentage on progress
Package: mcpy-pixel
PypixelA Simple Python Wrapper for the Minecraft Hypixel API!FeaturesGet Player Data and other Info from Hypixel's Servers.Get SkyBlock Game Data so you can interact with the SkyBlock API!More Coming Very Soon...InstallationPypixel requiresPythonv3.7+ to run.Install the package and Get Coding!pip3installpypixelapi
Package: mcqa
mcQA : Multiple Choice Questions AnsweringAnswering multiple choice questions with Language Models.InstallationWith pippipinstallmcqaFrom sourcegitclonehttps://github.com/mcqa-suite/mcqa.gitcdmcQA
pipinstall-e.Getting startedData preparationTo train amcQAmodel, you need to create a csv file with n+2 columns, n being the number of choices for each question. The first column should be the context sentence, the n following columns should be the choices for that question and the last column is the selected answer.Below is an example of a 3 choice question (taken from theCoS-E dataset) :Context sentenceChoice 1Choice 2Choice 3LabelPeople do what during their time off from work?take tripsbrow shorterbecome hystericaltake tripsIf you have a trainedmcQAmodel and want to infer on a dataset, it should have the same format as the train data, but thelabelcolumn.See example data preparation below:frommcqa.dataimportMCQADatamcqa_data=MCQAData(bert_model="bert-base-uncased",lower_case=True,max_seq_length=256)train_dataset=mcqa_data.read(data_file='swagaf/data/train.csv',is_training=True)test_dataset=mcqa_data.read(data_file='swagaf/data/test.csv',is_training=False)Model trainingfrommcqa.modelsimportModelmdl=Model(bert_model="bert-base-uncased",device="cuda")mdl.fit(train_dataset,train_batch_size=32,num_train_epochs=20)Predictionpreds=mdl.predict(test_dataset,eval_batch_size=32)Evaluationfromsklearn.metricsimportaccuracy_scorefrommcqa.dataimportget_labelsprint(accuracy_score(preds,get_labels(train_dataset)))ReferencesTypeTitleAuthorYear:newspaper: PaperExplain Yourself! Leveraging Language Models for Commonsense ReasoningNazneen Fatema Rajani, Bryan McCann, Caiming Xiong and Richard SocherACL 2019:newspaper: PaperSWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense InferenceRowan Zellers, Yonatan Bisk, Roy Schwartz and Yejin Choi2018
Package: mcqa-utils
mcqa_utilsUtilities to handle evaluation of Multiple Choice Question Answering systems and tasks.Free software: MIT licenseDocumentation:https://mcqa-utils.readthedocs.io.FeaturesEvaluate results from RACE-like predictionsCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-07-23)First release on PyPI.
Package: mcqgen
mcqgenGenerate MCQs with options and correct answers in JSON Format. Uses GPT.InstallationYou can install the package using pip:pipinstallmcqgenMake sure you have OpenAI key stored in environment variableOPENAI_API_KEYUsageSimply call functiongenerate_mcqsas below.
First argument (Topic) is mandatory.
Default Model (model) is GPT3.5 and default number of questions (no) is 5.from mcqgen import mcqgengen = mcqgen.MCQGenerator()data = gen.generate_mcqs('Cricket', model="gpt-3.5-turbo", no=3)print(data){'questions': [{'question': 'Who holds the record for the highest individual score in Test cricket?', 'options': ['a) Sachin Tendulkar', 'b) Brian Lara', 'c) Don Bradman', 'd) Ricky Ponting'], 'answer': 'b) Brian Lara'}, {'question': 'Which cricketer has scored the most centuries in One Day Internationals (ODIs)?', 'options': ['a) Sachin Tendulkar', 'b) Ricky 
Ponting', 'c) Virat Kohli', 'd) Kumar Sangakkara'], 'answer': 'a) Sachin Tendulkar'}, {'question': 'Who has the best bowling figures in a Test innings?', 'options': ['a) Jim Laker', 'b) Anil Kumble', 'c) Muttiah Muralitharan', 'd) Shaun Pollock'], 'answer': 'a) Jim Laker'}]}
Package: mcquery
mcqueryIntroductionmcquery (aka py-mcpe-query) is a Python software that uses the query protocol to ping a Minecraft: Bedrock edition
server for basic information.Note: If you get a timeout error, it might be that the target server do not support the query protocol. If that happens,
you can usemcstatsinstead, where you can ping a server without the query
protocol.InstallInstall via pipRunpip install mcqueryin your terminal, and it will install the latest version of this project in your Python
environment.Install from sourceClone this repository and run the following in the root folder of this project:python setup.py build installUsageQuery with CLIAfter installation, themcquerycommand will be available in your Python environment. You can perform a query with
the following command:mcquery localhost.Example:$ mcquery play.lbsg.net
Motd: None
Hostname: {-name-:-Lifeboat Proxy-,-node-:{-type-:-PROXY-,-ip-:-proxy14.lbsg.net-,-players-:91,-maxplayers-:400,-tps-:39.950001}}
Game type: None
Game id: None
Version: None
Server engine: None
Plugins: ['']
Map: None
Num players: 91
Max players: 400
Whitelist: None
Host ip: **.***.**.**
Host port: 19132
Players: []You can also specify the port with the-poption (e.g.:mcquery localhost -p 19133). For more options, please runmcquery --help.Use as a moduleYou can also use the API by writing the following code in your project:frommcqueryimportmcqueryhost="localhost"port=19132withmcquery(host,port=port,timeout=10)asdata:# data is a QueryServerData objectprint(data)LicenseMIT © 2017-2021 w-gao
Package: mcr2
Maximal Coding Rate ReductionThis repository is an unofficial implementation of the following papers:Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction(2020)ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction(2021)More on the way.This also serves as the host repository forthe Pip package.What is Maximal Coding Rate Reduction?Our goal is to learn a mapping that maps the high-dimensional data that lies in a low-dimensional manifold to low-dimensional subspaces with the following three properties:Between-Class Discriminative: Features of samples from different classes/clusters should be highly uncorrelatedand belong to different low-dimensional linear subspacesWithin-Class Compressible: Features of samples from the same class/cluster should be relatively correlated in a sense that they belong to a low-dimensional linear subspaceMaximally Diverse Representation: Dimension (or variance) of features for each class/cluster should beas large as possibleas long as they stay uncorrelated from the other classesTo achieve this, we propose an objective function calledMaximal Coding Rate Reduction(MCR2). In our paper, we provide not only theoretical guarantees to the desired properties upon convergence, but also practical properties such as robustness to label corruption and empirical results such as state-of-the-art unsupervised clustering performance. For more details on algorithm design, please refer to our paper.What is ReduNet?Our goal is to build a neural network for representation learning with the following properties:Interpretable: We should be able to interpret each network operator and assign precise meaning to each layer and parameter.Forward-Propagation Only: The network should be trained using much-more interpretable forward-propagation methods, as opposed to back-propagation which tends to create black-boxes.Use MCR2: The network should seek to optimize MCR2loss function, as the purpose is distribution learning.To achieve this, we propose a neural network architecture and algorithms calledReduNet. In our paper, we provide not only theoretical interpretations and a precise derivation of each operator in the network, but also connections to other architectures that form naturally as components of ReduNet. We also provide empirical justification for the power of ReduNet. For more details on algorithm design, please refer to our paper.
Package: mcrcon
This library provides a straightforward implementation of Minecraft’sRcon protocolin Python to provide a client for handling Remote Commands
(RCON) to a Minecraft server.InstallationThe library is availble on PYPI and can be installed with pip:pip install mcrconUsageThe recommend way to run this client is using the python ‘with’ statement.
This ensures that the socket is correctly closed when you are done with it
rather than being left open.Example:In [1]: from mcrcon import MCRcon
In [2]: with MCRcon("10.1.1.1", "sekret") as mcr:
   ...:     resp = mcr.command("/whitelist add bob")
   ...:     print(resp)While you can use it without the ‘with’ statement, you have to connect
manually, and ideally disconnect:In [3]: mcr = MCRcon("10.1.1.1", "sekret")
In [4]: mcr.connect()
In [5]: resp = mcr.command("/whitelist add bob")
In [6]: print(resp)
In [7]: mcr.disconnect()Command Line UsageAfter installing the package a cli will be included that can be used.Help can be gotten by doing:mcrcon --helpYou can connect from the console with commands like the following:mcrcon 10.1.1.1You will either be prompted for your password, or you can set it as follows
as an environment variable:export RCON_PASSWORD=sekretAnd then you can run commands on the server interactively:> /whitelist add bob
Package: mcreate
mcreateTool to create projectsFree software: MIT licenseDocumentation:https://create.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.0 (2019-06-02)Updated docsClean-up0.1.0 (2019-06-02)First release on PyPI.
Package: mcreep
MCREEPMCREEP package fits various pre-defined models to creep data.The package can process both tensile and indentation creep measuremenentsin such a way that the results from the two methods are comparable.The creep data:A text file with two columns.The 1st column = time, the 2nd column = deformation.Deformation = strain (for tensile) or penetration depth (for indentation).The pre-defined models within the pagkage are:Simple empirical models = Power law, Nutting's law.Phenomenological models = elasto-visco-plastic models = EVP models.The theory and models are described in the open-acces publication:Materials 16 (2023) 834.https://doi.org/10.3390/ma16020834If you use MCREEP in your research,please citethe publication above.Quick startInstall MCREEP:pip install mcreepDownload thedemo package,
unzip it, and follow the instructions inreadmefile.Basically, you just run the unzipped PY-script and see the results.The PY-script should run anywhere, but the recommended environment isSpyder.The output (in Spyder) should look like in thisPrintScreen.Installation, documentation and examplesMCREEP is available atPyPI⇒ installation:pip install mcreepHome page of the project is atGitHubandGitHub pages.Docs and examplesare summarized at GitHub pages.All modules, classes and functions are documented by docstrings.Full documentationwas auto-generated from docstrings byPDoc.Versions of MCREEPVersion 1.0 = finalized, working for both tensile and indentation creepVersion 1.1 = a few important improvements, better documentation
Package: mcresources
Minecraft Resource GeneratorThis is a python module aimed to enable simple generation of the many json files that are required for Minecraft modding.Data FormatUpdates of this tool will track the format of the latest version of Minecraft. For each specific Minecraft version, the latest version ofmcresources, targeting that version, can be found below:Minecraft VersionLatest mcresources Version1.20Latest1.181.5.111.171.4.61.16.21.4.61.151.3.31.130.0.2DocumentationThe wiki (link above) is generated from the methods and docstring comments of the library itself using./docs.py, and will always be up-to-date with the latest version of the library.
Package: mcroute
MCRouteMarkov chain modelling of transportation networks.This package enables the modelling of networks (transportation or otherwise) in
which movement through the network is represented by discrete state Markov
chains.MCRoute extends theNetworkXmodule, requiring each node and edge to have an accompanying transition
probability matrix that can be used during network traversal calculations. The
package also incldues utility functions for constructing transition matrices
from data and theoretical distributions.Get StartedYou can take a look at the example workbooks available onGitHub.DocumentationDocumentation can be found for the latest release onRead the DocsCiting This PackagePlease cite the following paper (currently under preparation):W. KlumpenhouwerModelling Stochastic Transportation Networks with Markov
Chains. (Under Preparation)
Package: mcrpc
mcrpc - Multichain RPC clientContent Blockchain RPC ClientA Python 3 MultiChain RPC client built for theContent Blockchain ProjectThe versioning scheme followsMultiChainand includes code generated function annotations and api documentation to support code
completion and get you up to speed fast.Code CompletionCode DocumentationInstallingThe RPC client is published with the package namemcrpcon Python Package Index. Install it with:$pip3installmcrpcUsageIf you have a local blockchain node with default data-dir you can do:importmcrpcclient=mcrpc.autoconnect()client.getinfo()Change Log2.0.6.0 (2020-05-03)Add autoconnect convenience functionSwitched package versioning to match multichainSwitched to poetry based packagingUpdated to multichain node 2.0.61.0.2 (2019-02-13)Fix signature of appendrawexchange and completerawexchangeBuild rpc client against Multichain 2 Beta 2Add pretty-print support for response objectsSort response class properties for cleaner diffs on updates