Saravanan666/pypi-org huggingface dataset:
Package: mineer
minEER - A sensible sequence trimming algorithmFor a given quality annotated read, the minEER algorithm identifies the subsequence whichminimizes theexpectederrorrate (EER; the mean q-score) while maximizing the subsequence length according to user defined EER and minimum sequence length thresholds. This procedure mimics the manual exercise of choosing truncation positions by glancing at a quality profile distribution, but ensures consistent results and is fast (can run on 20,000 files in under 15s). The minEER algorithm offers an improvement to current heuristic methods (sliding windows, quality score drop offs, etc.) which can be sensitive to noise and miss the opportunity for a deterministic solution based on reasonable criteria. The algorithm itself can be seenhere.The minEER pipeline (see documentation withmineer -hafter installing) operates on an entire set of files from a single project. Assuming that all reads from a given direction (forward or reverse for paired reads) share a "similar" quality profile, the minEER pipeline runs the algorithm on a subsample of reads and to determine global truncation positions (where to start and end each read). All reads are then truncated according to these global positions and reads that fail to meet the user defined EER and minimum sequence lenght thresholds.InstallInstall withpip install mineerand then runmineer -hto view the input documentation and to test that installation worked properly.TutorialAfter installingmineer, run the following:# Download some fastq files to `sample_files/`
mineer-test-files
# Run the pipeline with default parameters (minimal acceptable error=.01)
mineer -i sample_files -f _1.fastq -r _2.fastq -o test_out -v sample_figsOnce you run the pipeline, a report of each step will appear as they execute. Files containing truncated reads will appear in the directory specified with-o. Providing the-vflag will produce visualizations like the following of quality profiles of untrimmed reads and the distribution of truncation positions identified by minEER:If you just want to compute truncation positions without writing out truncated files (which can take a while), then you can run mineer without writing files by not providing an output directory (no- oargument):mineer -i sample_files -f _1.fastq -r _2.fastqTo produce visualizations without writing out truncated files, you can provide a directory with-v(again, without-o):mineer -i sample_files -f _1.fastq -r _2.fastq -v sample_figsNote that these examples use default parameters, which can be inspected withmineer -h.PipelineMethod:Ingest files and recognize pairs based on file names (usingxopen)Run minEER on subset of readsDetermine global truncation positionsTruncate all reads to global positions and filter out read pairs that don't pass QC (currently requires longest length)Save truncated sequencesProduce visualizations, if visualization output directory providedContributingRun tests withpython -m unittestorpytestHere is a longer list of SRRs to test on:SRR9660346
SRR9660368
SRR9660375
SRR9660380
SRR9660372
SRR9660321
SRR9660322
SRR9660307
SRR9660387
SRR9660385
Package: minefield
What’s thisThis is a fork ofmeinheld, the
high performance python wsgi web server.Minefield removes asynchronous feature from meinheld and
have some experimental tuning.And minefield is a WSGI compliant web server. (PEP333 and PEP3333 supported)Requirementsminefield requiresPython 2.x >= 2.7orPython 3.x >= 3.3.minefield supports Linux, FreeBSD, Mac OS X.InstallationInstall from pypi:$ pip install -U minefieldInstall from source:$ python setup.py installminefield supports gunicorn.To install gunicorn:$ pip install -U gunicornBasic Usagesimple wsgi app:from minefield import server

def hello_world(environ, start_response):
    status = '200 OK'
    res = b"Hello world!"
    response_headers = [('Content-type', 'text/plain'), ('Content-Length', str(len(res)))]
    start_response(status, response_headers)
    return [res]

server.listen(("0.0.0.0", 8000))
server.run(hello_world)with gunicorn. user worker class “egg:minefield#gunicorn_worker” or “minefield.gminefield.MinefieldWorker”:$ gunicorn --workers=2 --worker-class="egg:minefield#gunicorn_worker" gunicorn_test:appPerformanceFor parsing HTTP requests, meinheld uses Ryan Dahl’s http-parser library.(seehttps://github.com/joyent/http-parser)It is built around the high performance event library picoev.(seehttp://developer.cybozu.co.jp/kazuho/2009/08/picoev-a-tiny-e.html)sendfileMeinheld uses sendfile(2), over wgsi.file_wrapper.
Package: mineflayer
Open Delivery Bot 📦🤖📦 Deliver anything, anywere! powered byMineFlayer.About•Key Features•How To Use•Install•Credits•LicenseAbout 📬Welcome to the Open Delivery Bot project! This open-source initiative empowers users to deploy a self-hosted delivery bot, offering an array of advanced functionalities. Our command-line interface (CLI) or remote web GUI allows effortless control and management, ensuring a seamless experience. 🤖Open Delivery Bot shines with its dynamic features, including pathfinding and optimized elytra flight, accelerating your delivery processes for utmost efficiency. Say goodbye to manual intervention and embrace automation! 📦Getting Started 🏁[!IMPORTANT]
Some parts of the code are still in development and do not work!All instalation instructons can be foundhere(6) Arguments & Options 📄[!IMPORTANT]
Not changing some of these settings will result in the bot not working. 🛑[!WARNING]
Do not share your Minecraft info to anyone. 🔒How to use the argumentsThe command line arguments available are:--email: The Minecraft Email the bot will log into (use a name and no password for a cracked account). 📧--password: The password for your account (if not using cracked). 🔑--host: The IP address or hostname of the Minecraft server. 🖥--port: The port number the Minecraft server is listening on. Default is 25565. ⚡--auth: The authentication method your account requires (mojang/microsoft/cracked). Default is "microsoft". 🔐--version: Minecraft version to use when connecting to a server. Default is auto. 🕹--check_timeout: How often in milliseconds the bot checks if it is still logged into the server. Default is 600000 (10 minutes). ⏱--viewer_port: The local port to run the viewer server on so you can visually see what the bot is doing. Default is 8000. 👀--quit_on_low_health: Disconect the bot if the bot is on low health. Default is "True". 👤--low_health_threashold: How low the health must be for the bot to quit. Default is "10". 👤--armor_equip: If the bot needs to equip all available armor. Default is "True". 👤🚀 GitHub Codespaces (run the bot in the cloud)[!IMPORTANT]
If the console gets spammed with random data, try changing the version from false to a version you know is supported by the server.Copy this code to you clipboard and run it once the terminal is availablepython-mopendeliverybotOpen GitHub CodeSpaces belowRoadmapAdd config.json fileAddclickChange to use as Pip package for better useAdd Discord integrationAdd Lava Caster (request by @givenbakerad on Discord)Add Custom code runner (a way for users to test their own bot code in a nice and simple way)MineFlayer 🚀Mineflayer is a complex library that allows you to control Minecraft accounts through a powerful, stable, and high-level JavaScript APIDiscordhttps://discord.gg/z8yRexNc3UCreditsMineFlayerScreenshots:DashboardSettings
Package: minefob
MinefobMinefob is a Python module that provides full control
over any Minehut server by utilizing the Minehut APIInstallationUse the package managerpipto install Minefob.pipinstallminefobUsagefromminefobimportMinefob# Instantiate a new instance of the Minefob class and provide it with# Minehut account email, password, and server namem=Minefob("foo_foo@example.domain","123456789","my_server")m.awaken()# starts serverm.say("Hello Word")# says "Hello World" in server chatm.slumber(10)# gives a ten second countdown before kicking players,# saving the world, and shutting down the serverContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: minegauler
A perfect clone of the classic Minesweeper game, with many added features.Read more about the project history onthe project homepage.Install and RunInstall withpip install minegauler, and then run withpython -m minegauler.FeaturesBasic functional gameAbility to specify custom board size/number of minesAbility to replay a gameMode to create boardsAbility to save and load boards (created or played)Get current game information, including predicted completion time for lost gameOption of whether first click will guarantee an openingOption to select cells by click-and-drag technique with the mouseOption to allow multiple mines per cellLocal highscores for standard difficulty modesHighscores uploaded to remote server when there is an internet connectionCustom cell stylesOption to change the cell sizeAbility to reduce the window size and use scrollContactEmail atminegauler@gmail.com, any questions/suggestions/requests welcome.Alternatively, feel free toopen an issueif you find a bug or have a feature request.
Package: mine-init
Main Documentationmine-initis a Python based startup routine forPackmakerbased modded Minecraft servers. It can be run on any Linux system and in any container at present. It can be configured via environment variables, flags, and soon config files. (ini,yamlandtomlformats are being considered)It works by usingPackmakerto download all mods based on aPackmakeryaml and lock file. It will download the latest mods, and sync the updated configuration and mods into the server directory in a stateful way that preserves runtime data, like the world.LikePackmaker,mine-initcan be given multiple pack files, which it will merge from first to last provided. This allows pack developers to release a server with pack related mods, and for server administrators to add their own maintenance packs, with mods for backup; sleep voting; and maps for example.Main Index
Package: minepkg
MinePKGThis is a package manager for minetest modsUsageFetching the newest mod list:$minepkgupdateInstalling mods:$minepkgaddmoreblocksbasic_materialsInstalling packages:- moreblocks- basic_materialsInstalling to /home/martijn/.minetest/modsContinue installing? [Y/n]moreblocks: 39.0KB [00:00, 326KB/s]basic_materials: 39.0KB [00:00, 389KB/s]Installing moreblocks...Installing basic_materials...
Package: minepy
minepy provides a library for the Maximal Information-based
Nonparametric Exploration (MICandMINE family). Key features:APPROX-MIC(the original algorithm, DOI: 10.1126/science.1205438) andMIC_e(DOI: arXiv:1505.02213 and DOI: arXiv:1505.02214) estimators;Total Information Coefficient(TIC, DOI: arXiv:1505.02213) and theGeneralized Mean Information Coefficient(GMIC, DOI: arXiv:1308.5712);anANSI C libraryaC++interface;an efficientPython API(Python 2 and 3 compatibility);an efficientMATLAB/OCTAVE API;minepy is an open-source, GPLv3-licensed software.TheminervaR interfaceis available atCRAN.MICtoolsThe`mine`command-line application is deprecated since version 1.2.2.
We suggest to use MICtools, a comprehensive and effective pipeline for TICe and MICe
analysis. TICe is used to perform efficiently a high throughput
screening of all the possible pairwise relationships assessing their
significance, while MICe is used to rank the subset of significant associations
on the bases of their strength.Paper,code and documentation.Docker imageThe minepy library is preinstalled in theMICtools Docker image.LinksHomepage and DocumentationDownloadGithub pageIssuesOld (version 1.0.0) documentationCiting minepyDavide Albanese, Michele Filosi, Roberto Visintainer, Samantha Riccadonna,
Giuseppe Jurman and Cesare Furlanello. minerva and minepy: a C engine for the
MINE suite and its R, Python and MATLAB wrappers.  Bioinformatics (2013)
29(3): 407-408 first published online December 14, 2012
doi:10.1093/bioinformatics/bts707.Financial ContributionsComputational Biology Unit - Research and Innnovation Center at Fondazione
Edmund MachPredictive Models for Biological and Environmental Data Analysis (MPBA)
Research Unit at Fondazione Bruno Kessler
Package: mi-ner
miNERA python library for NER (Named Entity Recognition) evaluationWe can evaluate the performance of NER by distinguishing between known entities and unknown entities using this library.SupportTagging SchemeIOB2BIOESBIOULmetricsprecisionrecallf1Requirementspython3cythonInstallationpipinstallcython# must execute before `pip install mi-ner`pipinstallmi-nerUsageSample>>>fromminerimportMiner>>>answers=['B-PSN O O B-LOC O O O O'.split(' '),'B-PSN I-PSN O O B-LOC I-LOC O O O O'.split(' '),'S-PSN O O S-PSN O O B-LOC I-LOC E-LOC O O O O'.split(' ')]>>>predicts=['B-PSN O O B-LOC O O O O'.split(' '),'B-PSN B-PSN O O B-LOC I-LOC O O O O'.split(' '),'S-PSN O O O O O B-LOC I-LOC E-LOC O O O O'.split(' ')]>>>sentences=['花子 さん は 東京 に 行き まし た'.split(' '),'山田 太郎 君 は 東京 駅 に 向かい まし た'.split(' '),'花子 さん と ボブ くん は 東京 スカイ ツリー に 行き まし た'.split(' '),]>>>knowns={'PSN':['花子'],'LOC':['東京']}# known words (words included in training data)>>>m=Miner(answers,predicts,sentences,knowns)>>>m.default_report(True)precisionrecallf1_scorenumLOC1.0001.0001.0003PSN0.5000.5000.5004overall0.7140.7140.7147{'LOC':{'precision':1.0,'recall':1.0,'f1_score':1.0,'num':3},'PSN':{'precision':0.5,'recall':0.5,'f1_score':0.5,'num':4},'overall':{'precision':0.7142857142857143,'recall':0.7142857142857143,'f1_score':0.7142857142857143,'num':7}}>>>m.unknown_only_report(True)precisionrecallf1_scorenumLOC1.0001.0001.0002PSN0.0000.0000.0002overall0.5000.5000.5004{'LOC':{'precision':1.0,'recall':1.0,'f1_score':1.0,'num':2},'PSN':{'precision':0.0,'recall':0.0,'f1_score':0,'num':2},'overall':{'precision':0.5,'recall':0.5,'f1_score':0.5,'num':4}}>>>m.return_predict_named_entities(){'known':{'LOC':['東京'],'PSN':['花子'],'overall':[]},'unknown':{'LOC':['東京スカイツリー','東京駅'],'PSN':['山田','太郎'],'overall':[]}}Methodsmethoddescriptiondefault_report(print_)return result of named entity recognition. if print_=True, showing resultknown_only_report(print_)return result of known named entity recognition.unknown_only_report(print_)return result of unknown named entity recognition.return_predict_named_entities()return named entities along predicted label(predicts).return_answer_named_entities()return named entities along answer label(answer).return_miss_labelings()return miss labeling sentences.segmentation_score(mode)show parcentages of matching answer and predict labels.  ifknownorunknownformode, return labeling accuracy for known or unknown NE.LicenseMIT
Package: mineralML
mineralMLWe present mineralML (mineral classification using Machine Learning) for classifying common igneous minerals based on oxide data collected by EPMA, with functions for calculating stoichiometries and crystallographic sites based on this classification. Utilizing this package allows for the identification of misclassified mineral phases and poor-quality data. We streamline data processing and cleaning to allow for the rapid transition to usable data, improving the utility of data curated in these databases and furthering computing and modeling capabilities.DocumentationRead thedocumentationfor a run-through of the mineralML code.Run on the CloudIf you do not have Python installed locally, run mineralML onGoogle Colab. The Cloud-based version runs rapidly, with test cases of >10,000 microanalyses classified within 4 seconds.Run and Install LocallyObtain a version of Python between 3.7 and 3.11 if you do not already have it installed. mineralML can be installed with one line. Open terminal and type the following:pip install mineralMLMake sure that you keep up with the latest version of mineralML. To upgrade to the latest version of mineralML, open terminal and type the following:pip install mineralML --upgradeMac/Linux installation will be straightforward. Windows installations will require the additional setup of WSL.
Package: minerl
TheMineRLPython PackagePython package providing easy to use gym environments and a simple data api for the MineRLv0 dataset.Toget started please read the docs here!InstallationWith JDK-8 installed run this commandpip3 install --upgrade minerlBasic UsageRunning an environment:importminerlimportgymenv=gym.make('MineRLNavigateDense-v0')obs=env.reset()done=Falsewhilenotdone:action=env.action_space.sample()# One can also take a no_op action with# action =env.action_space.noop()obs,reward,done,info=env.step(action)Sampling the dataset:importminerl# YOU ONLY NEED TO DO THIS ONCE!minerl.data.download('/your/local/path')data=minerl.data.make('MineRLObtainDiamond-v0',data_dir='/your/local/path')# Iterate through a single epoch gathering sequences of at most 32 stepsforcurrent_state,action,reward,next_state,done\indata.batch_iter(num_epochs=1,seq_len=32):# Print the POV @ the first step of the sequenceprint(current_state['pov'][0])# Print the final reward pf the sequence!print(reward[-1])# Check if final (next_state) is terminal.print(done[-1])# ... do something with the data.print("At the end of trajectories the length""can be < max_sequence_len",len(reward))Visualizing the dataset:# Make sure your MINERL_DATA_ROOT is set!exportMINERL_DATA_ROOT='/your/local/path'# Visualizes a random trajectory of MineRLObtainDiamondDense-v0python3-mminerl.viewerMineRLObtainDiamondDense-v0MineRL CompetitionIf you're here for the MineRL competition. Please checkthe main competition website here.
Package: minerl-navigate
MineRL Navigate Video DatasetA dataset for long-term video prediction. It shows human players traversing
worlds of the video game Minecraft, which include forests, mountains, oceans,
and islands. The dataset contains 961 train videos and 255 test videos. Each
video contains 500 images of size 64x64x3.CitationThe dataset was originally crowd-sourced by Guss et al. and preprocessed for
video prediction by Saxena et al. If you use this dataset, please reference:@article{saxena2021clockworkvae,
  title={Clockwork Variational Autoencoders},
  author={Saxena, Vaibhav and Ba, Jimmy and Hafner, Danijar},
  journal={arXiv preprint arXiv:2102.09532},
  year={2021},
}@article{guss2019minerl,
  title={Mine{RL}: A Large-Scale Dataset of {M}inecraft Demonstrations},
  author={William H. Guss and Houghton, Brandon and Topin, Nicholay and Wang, Phillip and Codel, Cayden and Veloso, Manuela and Salakhutdinov, Ruslan},
  journal={International Joint Conference on Artificial Intelligence},
  year={2019},
}InstructionsInstallation:pip3installminerl-navigateUsage example:importtensorflowastfimporttensorflow_datasetsastfdsimportminerl_navigatedataset=tfds.load('minerl_navigate',shuffle_files=True)test=dataset['test']train=dataset['train'].repeat()train=train.flat_map(lambdax:tf.data.Dataset.from_tensor_slices(tf.reshape(x['video'],(5,100,64,64,3))))train=train.shuffle(5000).batch(50).prefetch(1)forbatchintrain:assertbatch.shape==(50,100,64,64,3)assertbatch.dtype==tf.uint8breakCreationThe video dataset was created by combining theNavigateandNavigate
Extremetasks, splitting them into non-overlapping sequences of length 500,
splitting them into train and test sets, and storing the videos as MP4 files.
Optionally, the action sequences are available in JSON format. Instead of using
the data loader, the data can also bedownloadedmanually.
Package: minerl-patched
IGLU patchThis repo contains Minerl package with patched Malmo with custom blocks and behaviour added.TheMineRLPython PackagePython package providing easy to use gym environments and a simple data api for the MineRLv0 dataset.Toget started please read the docs here!InstallationWith JDK-8 installed run this commandpip3 install --upgrade minerlBasic UsageRunning an environment:importminerl_patchedimportgymenv=gym.make('MineRLNavigateDense-v0')obs=env.reset()done=Falsewhilenotdone:action=env.action_space.sample()# One can also take a no_op action with# action =env.action_space.noop()obs,reward,done,info=env.step(action)Sampling the dataset:importminerl_patched# YOU ONLY NEED TO DO THIS ONCE!minerl_patched.data.download('/your/local/path')data=minerl_patched.data.make('MineRLObtainDiamond-v0',data_dir='/your/local/path')# Iterate through a single epoch gathering sequences of at most 32 stepsforcurrent_state,action,reward,next_state,done\indata.sarsd_iter(num_epochs=1,max_sequence_len=32):# Print the POV @ the first step of the sequenceprint(current_state['pov'][0])# Print the final reward pf the sequence!print(reward[-1])# Check if final (next_state) is terminal.print(done[-1])# ... do something with the data.print("At the end of trajectories the length""can be < max_sequence_len",len(reward))Visualizing the dataset:# Make sure your MINERL_DATA_ROOT is set!exportMINERL_DATA_ROOT='/your/local/path'# Visualizes a random trajectory of MineRLObtainDiamondDense-v0python3-mminerl_patched.viewerMineRLObtainDiamondDense-v0MineRL CompetitionIf you're here for the MineRL competition. Please checkthe main competition website here.
Package: minerl-shwang
TheMineRLPython PackagePython package providing easy to use gym environments and a simple data api for the MineRLv0 dataset.Toget started please read the docs here!InstallationWith JDK-8 installed run this commandpip3 install --upgrade minerlBasic UsageRunning an environment:importminerlimportgymenv=gym.make('MineRLNavigateDense-v0')obs=env.reset()done=Falsewhilenotdone:action=env.action_space.sample()# One can also take a no_op action with# action =env.action_space.noop()obs,reward,done,info=env.step(action)Sampling the dataset:importminerl# YOU ONLY NEED TO DO THIS ONCE!minerl.data.download('/your/local/path')data=minerl.data.make('MineRLObtainDiamond-v0',data_dir='/your/local/path')# Iterate through a single epoch gathering sequences of at most 32 stepsforcurrent_state,action,reward,next_state,done\indata.sarsd_iter(num_epochs=1,max_sequence_len=32):# Print the POV @ the first step of the sequenceprint(current_state['pov'][0])# Print the final reward pf the sequence!print(reward[-1])# Check if final (next_state) is terminal.print(done[-1])# ... do something with the data.print("At the end of trajectories the length""can be < max_sequence_len",len(reward))Visualizing the dataset:# Make sure your MINERL_DATA_ROOT is set!exportMINERL_DATA_ROOT='/your/local/path'# Visualizes a random trajectory of MineRLObtainDiamondDense-v0python3-mminerl.viewerMineRLObtainDiamondDense-v0MineRL CompetitionIf you're here for the MineRL competition. Please checkthe main competition website here.