Saravanan666/pypi-org huggingface dataset:
Package: mc-enchanter
MC-EnchanterDescriptionMC-Enchanteris a simple python program that simulates the enchantment mechanics of the anvil in the popular game:Minecraft.
It's based on the Official Minecraft Wiki page:Anvil/Mechanics.MotivationLet's say that you wanted a powerfull axe with all of the following enchantements: Sharpness V, Efficiency V, Silk Touch and Unbreaking III. Suppose that you got all corresponding enchanted books and you applied them one after the other to the axe using an anvil. You'll end up with a total cost of28 EXP.
But if you combined Silk Touch I and Unbreaking III first, and then you applied the rest of books then it will cost you25 EXP.
This motivated me to write this program that by giving it the enchanted books you have, and the desired enchanted item, it gives you the cheapest (possible) books combination.FeaturesCalculate the exact total experience cost of any enchantment.Find the minimum experience cost for a specific enchantements combination using enchanted books only.Find the minimum experience cost for a specific enchantements combination using enchanted books and items.Support items durability.Build a nice gui.InstalationAfter downloading Python 3.6+, use the following pip command:pip install --upgrade mc-enchanterExampleLet's take the example used inMotivationfrommc_enchanterimport*axe=EnchantableItem([],"axe")unbreaking3=EnchantedBook([Enchantment('Unbreaking',3)])sharpness5=EnchantedBook([Enchantment('Sharpness',5)])efficiency5=EnchantedBook([Enchantment('Efficiency',5)])silk_touch=EnchantedBook([Enchantment('Silk Touch',1)])enchantedBooks=[unbreaking3,sharpness5,efficiency5,silk_touch]combination=BestBooksEnchantmentCombination(axe,enchantedBooks)print(combination)Output[25, ((axe, Book(Sharpness V), 5), (axe, Book(Efficiency V), 6), (axe, Book(Book(Silk Touch I)+Book(Unbreaking III),3), 11))]
 ^ The    | ^_> Combine the axe    |  ^_> Combine the axe       |    ^_> 1- Combine Book(Silk Touch I) with Book(Unbreaking III) costs 3 EXP
 cheapest | with Book(Sharpness V) |  with Book(Efficiency V)   |     2- Combine axe with the resulting book: Book(Book(Silk Touch I)+Book(Unbreaking III),3)
   cost   |     costs 5 EXP        |      costs 6 EXP           |                            costs 11 EXP
Package: mcerp
Code:https://github.com/tisimst/mcerpDocumentation: (not online yet, for now, see the doc folder on Github)License: BSD-3-ClauseOverviewmcerpis a stochastic calculator forMonte Carlo methodsthat useslatin-hypercube samplingto perform non-order specificerror propagation(or uncertainty analysis).With this package you caneasilyandtransparentlytrack the effects
of uncertainty through mathematical calculations. Advanced mathematical
functions, similar to those in the standardmathmodule, and statistical
functions like those in thescipy.statsmodule, can also be evaluated
directly.If you are familiar with Excel-based risk analysis programs like@Risk,Crystal Ball,ModelRisk, etc., this packagewill work wondersfor you
(and probably even be faster!) and give you more modelling flexibility with
the powerful Python language. This package alsodoesn’t cost a penny,
compared to those commercial packages which costthousands of dollarsfor a
single-seat license. Feel free to copy and redistribute this package as much
as you desire!Main FeaturesTransparent calculations.No or little modificationto existing
code required.BasicNumPysupport without modification. (I haven’t done extensive
testing, so please let me know if you encounter bugs.)Advanced mathematical functions supported through themcerp.umathsub-module. If you think a function is in there, it probably is. If it
isn’t, please request it!Easy statistical distribution constructors. The location, scale,
and shape parameters follow the notation in the respective Wikipedia
articles and other relevant web pages.Correlation enforcementand variable sample visualization capabilities.Probability calculationsusing conventional comparison operators.Advanced Scipystatistical function compatibilitywith package
functions. Depending on your version of Scipy, some functions might not
work.Installationmcerpworks on Linux, MacOS and Windows, with Python 2.7 or with Python 3.5 or later.To install it, usepip:pip install mcerpThemcerpdependencies should be installed automatically if usingpip,
otherwise they will need to be installed manually:NumPy: Numeric PythonSciPy: Scientific PythonMatplotlib: Python plotting librarySee alsouncertainties: First-order error propagationsoerp: Second-order error propagationContactPlease sendfeature requests, bug reports, or feedbacktoAbraham Lee.
Package: mcerp3
Overviewmcerp3is a stochastic calculator forMonte Carlo methodsthat useslatin-hypercube samplingto perform non-order specificerror propagation(or uncertainty analysis).With this package you caneasilyandtransparentlytrack the effects
of uncertainty through mathematical calculations. Advanced mathematical
functions, similar to those in the standardmathmodule, and statistical
functions like those in thescipy.statsmodule, can also be evaluated
directly.If you are familiar with Excel-based risk analysis programs like@Risk,Crystal Ball,ModelRisk, etc., this packagewill work wondersfor you
(and probably even be faster!) and give you more modelling flexibility with
the powerful Python language. This package alsodoesn’t cost a penny,
compared to those commercial packages which costthousands of dollarsfor a
single-seat license. Feel free to copy and redistribute this package as much
as you desire!What’s New In This Releasethis is a Python 3 release of the mcerp package by Abraham Leeavailable viacondaorpipofficially adds the 3-clause BSD licesnse text to the software
(this license has been specified in the mcerp PyPI package for years)supports SciPy >= 1.0 by removing the scipy.stats.signaltonoise functionMain FeaturesTransparent calculations.No or little modificationto existing
code required.BasicNumPysupport without modification. (I haven’t done extensive
testing, so please let me know if you encounter bugs.)Advanced mathematical functions supported through themcerp.umathsub-module. If you think a function is in there, it probably is. If it
isn’t, please request it!Easy statistical distribution constructors. The location, scale,
and shape parameters follow the notation in the respective Wikipedia
articles and other relevant web pages.Correlation enforcementand variable sample visualization capabilities.Probability calculationsusing conventional comparison operators.Advanced Scipystatistical function compatibilitywith package
functions. Depending on your version of Scipy, some functions might not
work.Python 3 supportInstallationHow to installEffort has been made to ensuremcerp3is easy to install.From the command-line, do one of the following:Install theconda package:$ conda install mcerp3 -c freemapaInstall thePyPI package:$ pip install mcerp3Thesource codeis also freely available, in case you would like to
incorporate it directly into your project. However, when possible, it is
usually easier to let your package manager handle things for you.Required PackagesThe following packages are required, but should be installed automatically
(if usingcondaorpip). Otherwise, they may need to be installed
manually:NumPy: Numeric PythonSciPy: Scientific PythonMatplotlib: Python plotting librarySee alsouncertainties: First-order error propagationsoerp: Second-order error propagationContactBugs should be reported on theGitHub issuespage. Python 3 related
requests can be sent toPaul Freeman. Other issues should be referred to
the original author,Abraham Lee.
Package: mcextract
mcextractExtract assets and data from the Minecraft jar.InstallationInstall the module with pip:pip3 install mcextractUpdate existing installation:pip3 install mcextract --upgradeFeaturesIncludes a UI and a command line extractor.Saves configuration for the next time you use it.Choose to extract the assets or data folders from the jarCompile the objects to get access to all sounds, langs, and other hidden assets that aren't in the jar.Choose which version to extract using a simple dropdown menu. (may experience some issues if your mc is located in diff folder)Data Generator for generating reports, and vanilla world generation files.ExamplesRun with UIimportmcextractapp=mcextract.CTkClient()app.mainloop()Run with the command lineimportmcextractsvr=mcextract.Server.from_args()svr.run()Now run the Python script using the commands listed belowCommand line commandsextractArgumentsNameRequiredDescription<fp>YesThe jar file to extract. Located:%appdata%\.minecraft\versions\VERSION\VERSION.jar--assetsExtract all files in assets folder.--dataExtract all files in data folder.--output <directory>The output directory.--eulaYesExcept the EULA.Examplepython -m mcextract extract "%appdata%\.minecraft\versions\1.20.1\1.20.1.jar" --assets --data --eula --output "%userprofile%\Downloads\Output"mapArgumentsNameRequiredDescription<index>YesThe index JSON to map objects with. Located:%appdata%\.minecraft\assets\indexes\INDEX.json<objects>YesThe directory that contains all objects. Located:%appdata%\.minecraft\assets\objects--output <directory>The output directory.--eulaYesExcept the EULA.Examplepython -m mcextract map "%appdata%\.minecraft\assets\indexes\3.json" "%appdata%\.minecraft\assets\objects" --eula --output "%userprofile%\Downloads\Output"generateArgumentsNameRequiredDescriptionExample<version>YesThe server jar version to download and use.1.20.1--clientGenerate client data.--serverGenerate server data.--reportsGenerate reports.--output <directory>The output directory.--eulaYesExcept the EULA.Examplepython -m mcextract generate 1.20.1 --client --server --reports --eula --output "%userprofile%\Downloads\Output"Planned FeaturesAdd minimize or maximize JSON's.
Package: mcfa
Thispythonpackage implements the Mixtures of Common Factor Analyzers model
introduced byBaek+ 2010. It
usestensorflowto implement a stochastic
gradient descent, which allows for model training without prior imputation of
missing data. The interface resembles thesklearnmodel API.DocumentationRefer to thedocs/documentation.ipynbfor the documentation anddocs/4d_gaussian.ipynbfor an example application.InstallInstall from PyPi usingpip:$ pip install mcfaThe minimum requiredpythonversion is 3.8.AlternativesEMMIXmfainRCasey+ 2019inpythonCompared to this implementation, Casey+ 2019 use an EM-algorithm instead of a
stochastic gradient descent. This requires the imputation of the missing valuesbeforethe model training. On the other hand, there are more initialization
routines the lower space loadings and factors available in the Casey+ 2019 implementation.
Package: mcfetch
MCFETCHFetches Minecraft player information from the Mojang APIInstallationRun the following:pipinstallmcfetchHow to useNon asynchronousFetch a player using their username:>>>frommcfetchimportFetchPlayer>>>player=FetchPlayer(name="gronkh")>>>player.name'Gronkh'>>>player.uuid'a2080281c2784181b961d99ed2f3347c'Fetch a player using their uuid:>>>frommcfetchimportFetchPlayer>>>player=FetchPlayer(uuid="a2080281c2784181b961d99ed2f3347c")>>>player.name'Gronkh'If a player doesn't exist:>>>frommcfetchimportFetchPlayer>>>player=FetchPlayer(name="ThisUsernameIsNotValid")>>>player.nameNone>>>player.uuidNoneIt is also possible to use a custom requests object:>>>frommcfetchimportFetchPlayer>>>fromrequests_cacheimportCachedSession>>>my_cache=CachedSession(cache_name='./my_cache',expire_after=60)>>>player=FetchPlayer(name="gronkh",requests_obj=my_cache)You can fetch a player's skin URL and skin texture>>>frommcfetchimportFetchPlayer>>>player=FetchPlayer(name="Notch")>>>player.skin_url'http://textures.minecraft.net/texture/292009a4925b58f02c77dadc3ecef07ea4c7472f64e0fdc32ce5522489362680'>>>player.skin_textureb'\x89PNG\r\n\x1a\n\x00\x00\x00\...'Fetch a player without specifying whether you are using a Username or UUID>>>frommcfetchimportFetchPlayer2>>>player=FetchPlayer2("a2080281c2784181b961d99ed2f3347c")>>>player.name'Gronkh'>>>player=FetchPlayer2("Gronkh")>>>player.uuid'a2080281c2784181b961d99ed2f3347c'AsynchronousFetching a player (same functionality as the above examples)>>>importasyncio>>>frommcfetchimportAsyncFetchPlayer2>>>asyncdefmain():...player=AsyncFetchPlayer2("Gronkh")...print(awaitplayer.name)...print(awaitplayer.uuid)>>>asyncio.run(main())'Gronkh''a2080281c2784181b961d99ed2f3347c'ToolsCheck syntax of a username:>>>frommcfetchimportis_valid_username>>>is_valid_username('gronkh')True>>>is_valid_username('gronkh-is cool')FalseCheck syntax of a UUID (undashed):>>>frommcfetchimportis_valid_uuid>>>is_valid_uuid('a2080281c2784181b961d99ed2f3347c')True>>>is_valid_uuid('bcc28a5f6')FalseRemove dashes from a UUID:>>>frommcfetchimportundash_uuid>>>undash_uuid('a2080281-c278-4181-b961-d99ed2f3347c')a2080281c2784181b961d99ed2f3347cLicenseThis software is licensed under the MIT license. Feel free to use it however you like. For more infomation seeLICENSE.
Package: mcfinance
mcfinanceDeveloped by ragha1992 and 05kashyapDescription:Extract financial data from the money control website using the company name, BSE or NSE number.
Export any selected data into either pandas dataframe or excel sheet with ease!.Disclaimer: We are in no way affiliated with moneycontrol.comUsage:Initialise company details:Create an Extractor instance with company name/ BSE/ NSE ID and/or number of years(default), required documents(default), and filepath to write documents(default current directory).frommcfinanceimportExtractorCompany=Extractor(user_input="Company_name")or input the BSE listing number(int) (number can be used as input directly)Company=Extractor(user_input=BSEnumber)or input the NSE listing code (code can be used as input directly)Company=Extractor(user_input="NSEcode")#years and docs are defaultCompany=Extractor(user_input="Company_name",years=10,docs=["balance sheet","profit loss"],filepath="/files")any of the inputs can be changed later on as per user convenienceCompany.set_inputs(years=6)We can view the initialised details using:print(Company)output:Extractor object, name is Company_name, number of years is 10, documents are ["balancesheet", "profit loss"]Export company details as excel file (default)The get_info() function can be used to extract and store company data in an excel file. The file will be stored in the current filepath or the user defined filepath as per object initialisationCompany.get_info()orCompany.get_info(option=1)Export company details into pandas data frameDataFrame1,DataFrame2,DataFrame3=Company.get_info(option=0)Plot certain attribute over selected years using matplotlibThe plotter() function can be used to show the companies attribute from a certain document over the selected period of time using a line graph from the matplotlib library. The function accepts a single required arguement for the attribute selection.company.plotter("certain file attribute of the document")Usage example:cmp=Extractor("TCS",years=10,docs=["ratios"])cmp.plotter("EV/EBITDA (X)")output:We can also plot the data of multiple companies on the same graph for comparison purposescompany1=Extractor("TCS",years=10,docs=["ratios"])company2=Extractor("Infosys",years=10,docs=["ratios"])Extractor.cmp_plot(comp=[company1,company2],attributes="EV/EBITDA (X)")output:
Package: mcflow
An example package. Generated with cookiecutter-pylibrary.Free software: BSD 2-Clause LicenseInstallationpip install mcflowDocumentationhttps://python-mcflow.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2019-02-14)First release on PyPI.
Package: mcfly
The goal of mcfly is to ease the use of deep learning technology for time series classification and regression. The advantage of deep learning is that it can handle raw data directly, without the need to compute signal features. Deep learning does not require  expert domain knowledge about the data, and has been shown to be competitive with conventional machine learning techniques. As an example, you can apply mcfly on accelerometer data for activity classification, as shown inthe tutorial.If you use mcfly in your research, please cite the following software paper:D. van Kuppevelt, C. Meijer, F. Huber, A. van der Ploeg, S. Georgievska, V.T. van Hees.Mcfly: Automated deep learning on time series.SoftwareX,
Volume 12,
2020.doi: 10.1016/j.softx.2020.100548InstallationPrerequisites:Python 3.7, 3.8, 3.9 or 3.10pipTensorflow 2Installing all dependencies in separate conda environment:condaenvcreate-fenvironment.yml# activate this new environmentsourceactivatemcflyTo install the package, run in the project directory:pip install mcflyVisualizationWe build a tool to visualize the configuration and performance of the models. The tool can be found onhttp://nlesc.github.io/mcfly/. To run the  model visualization on your own computer, cd to thehtmldirectory and start up a python web server:python -m http.server 8888 &Navigate tohttp://localhost:8888/in your browser to open the visualization. For a more elaborate description of the visualization seeuser manual.User documentationUser and code documentation.ContributingYou are welcome to contribute to the code via pull requests. Please have a look at theNLeSC guidefor guidelines about software development.We use numpy-style docstrings for code documentation.LicensingSource code and data of mcfly are licensed under the Apache License, version 2.0.
Package: mcfly_test
McFly Test.🚀 UsingPrerequisites1. Install DockerGo toDocker, download and install docker.Configure Docker to use the BuildKit build system. On macOS and Windows, BuildKit is enabled by default in Docker Desktop.To install this package, run:pipinstallmcfly_test✨ Setup VSCodeOpen this repo with VSCode,build DevContainerandDev Containers: Reopen in Container.✨ Setup PyCharmOpen this repo with PyCharm, andconfigure Docker Compose as a remote interpreterwith thedevservice.🧑‍💻 ContributingThe following commands can be used inside a DevContainer.Run linterspoelintRun testspoetestUpdate poetry lock filepoetrylock--no-updateOutside a DevContainerRun lintersdockercomposerundevcontainerpoelintRun testsdockercomposerundevcontainerpoetestUpdate poetry lock filedockercomposerundevcontainerpoetrylock--no-update# Update the docker image with the new lock filedockercomposebuildOpen a shell in dockerdockercomposerundevcontainerShortcuts outside a DevContainermake lintmake testmake lockmake shell️⚡️ Scaffolded withPoetry Copier🛠️Open an issueif you have any questions or suggestions.
Package: mcgen
mcgenPython utilities for downloading and processing Minecraft's generated data.RequirementsPython 3.8+Java 11+ (for invoking the Minecraft server's data generator)InstallationpipinstallmcgenUsagepython-mmcgen--helpmcgen [-h] [--jarpath JARPATH] [--rawpath RAWPATH] [--outpath OUTPATH] [--version VERSION] [--manifest MANIFEST] [--processors [PROCESSORS [PROCESSORS ...]]] [--log LOG]

Download the Minecraft server jar for the specified version, invoke the data generator, and process the output.

optional arguments:
  -h, --help            show this help message and exit
  --jarpath JARPATH     Where to download and store the server jar. Default: temp/jars/minecraft_server.{version}.jar
  --rawpath RAWPATH     Where to store the raw server-generated files. Default: temp/raw/{version}
  --outpath OUTPATH     Where to write the final processed output. Default: temp/out/{version}
  --version VERSION     The server version to download and process. Defaults to latest snapshot.
  --manifest MANIFEST   Where to fetch the version manifest from. Defaults to Mojang's online copy.
  --processors [PROCESSORS [PROCESSORS ...]]
                        Which processors to use in processing the raw server-generated files. Defaults to a set of built-in processors.
  --log LOG             The level of verbosity at which to print log messages.ProcessorsProcessors are used to process the raw server-generated data and produce output. They are invoked one after the other, in the order they are defined.To provide a custom set of processors, use the--processorsoption like so:python-mmcgen--processorsmcgen.processors.split_registriesmcgen.processors.summarize_dataBuilt-in processorsSeveral built-in processors are provided inmcgen.processors:write_version_file- Write the game version to a file.convert_json_files- Convert json files into another form.simplify_blocks- Create an optimized summary of blocks.split_registries- Splitregistries.jsoninto separate files.summarize_data- Create a summary of each vanilla registry.summarize_worldgen- Create a summary of worldgen reports.create_all_tags_data_pack- Generate a data pack with "all" tags.Custom processorsProcessors are Python modules containing a function with the following signature:defprocess(ctx:Context,**options):...ctxcontains information about the processing contextoptionsis a key-value mapping of arbitrary data21w39aNote that in snapshot 21w39a thejavacommand used to invoke the server generator changed:# Prior to 21w39a:java-cp{jar_path}net.minecraft.data.Main--server--reports# From 21w39a onward:java-DbundlerMainClass=net.minecraft.data.Main-jar{jar_path}--server--reportsIf you need to generate data for versions prior to 21w39a, use the--cmdoption with the olderjavacommand.
Package: mcget
"mcget" is a simple tool to download and update the Minecraft server or/and
launcher.FeaturesInstall or update the Minecraft server (server.jar) in any directory.Install or update the Minecraft Java launcher (launcher.jar) in any
directory (This launcher variant was chosen because it works on every OS and
only requires Java installed).Downloads files only if outdated or not installed.Uses the SHA1 hash provided by Mojang API to check download integrity and
check if a file needs to be updated.Performs the operation on server and launcher in parallel to improve update
performance.Installation"mcget" can be installed using pip and require Python >= 3.6:pip3installmcgetUsage"mcget" is a simple command line utility that support following arguments:--launcheror-l: If specified, install or update the launcher.--serveror-s: If specified, Install or update the server.--out_diror-o: Output directory. Default to current directory if not
specified.--quietor-q: If specified, hide outputs.--version: If specified, return "mcget" version and exit.ExamplesInstalling (or updating) the launcher and the server in a the/opt/minecraftdirectory:mcget--launcher--server--out_dir=/opt/minecraft# Or with a shorter syntax:mcget-lso/opt/minecraftInstalling (or updating) the server only in the current directory:mcget--server# Or with a shorter syntax:mcget-s