Saravanan666/pypi-org huggingface dataset:
Package: metadata-viewer
Metadata-viewer is a Python  package designed to help you easily visualize
metadata from various file formats mainly for visualizing metadata associated
with cryo-electron microscopy (cryo-EM) data. Its primary functionality includes
displaying data in both table and image gallery modes. This package is capable
of handling large datasets with millions of rows and performing operations such
as selecting and sorting quickly. While its core purpose is to serve as a viewer within the
Scipion framework, it can also be used independently outside of Scipion.
The package facilitates the exploration of cryo-EM-related information and other
types of metadata through efficient data manipulation and visualization, making
it a valuable tool for researchers in the field.
It is available as a small self-containedPython module.InstallationYou can install MetadataViewer using pip:pip install metadata-viewerUsageTODOSupported metadata file formatsMetadataViewer supports a wide range of file formats(through Scipion), including but not limited to:star filesxmd filessqlite filesContributingMetadataViewer is an open-source project, and contributions from the community
are welcome. If you’d like to contribute or report issues, please visit our
GitHub repository.GitHub repository.AuthorsYunior C. Fonseca Reyna, Biocomputing Unit, National Center of Biotechnology, Madrid, SpainPablo Conesa Mingo, Biocomputing Unit, National Center of Biotechnology, Madrid, SpainJorge Jiménez, Biocomputing Unit, National Center of Biotechnology, Madrid, SpainTestingpython3-munittest discover metadataviewer/tests
Package: metadir
IntroductionTo autogenerate SCANME.md, you can globallypip install metadir, and
typemetadir ..UsageRunning commandmetadir .to generateSCANME.mdcontent.-'*':''README.md:{'*':''}src:-'*':''main.py:{'*':''}main.js:{'*':''}core:-'*':''app.py:{'*':''}docs:-'*':''index.md:{'*':''}The values at each level provide ability to specifymetaformatfor each file and directory in the project by providing URLs to schema
specifications.TODOImplement ignoring of files based on.gitignore, and using regex
(look for stringsSKIPin the project).
Package: metadisassembler
The Metabolic Disassembler is a Python package to automatically predict
a combination of biosynthetic units in a natural product. This software
would help to reveal the starting materials of the target natural
product.InstallationInstall The Metabolic Disassembler withpip.$pipinstallmetadisassemblerRequirementsPython (3.6)RDKit(version 2019.09.2.0)NetworkX(version 2.2)CairoSVG(version 2.4.2)Pillow (PIL)(version 6.2.1)Pandas(version 0.25.3)Matplotlib(version 3.1.2)Command Line Usage%metadisassembler-husage:metadisassembler[-h][-tTIME][--hide][-c]querypositionalarguments:queryMDL_Molfile,SMILES,InChI,KEGG_COMPOUND_ID,KNApSAcK_COMPOUND_IDoptionalarguments:-h,--helpshowthishelpmessageandexit-tTIME,--timeTIMEsetatimelimit[s][default:300]--hidehidestereochemistry[default:False]-c,--coloroutputcolorallocationinformation[default:False]Basic UsageA usage example by Jupyter Notebook can be seenhere.Google ColabYou can try the process from installation to the basic usage in Google
Colab.CitationAmano K, Matsumoto T, Tanaka K, Funatsu K, Kotera M. Metabolic
disassembler for understanding and predicting the biosynthetic units of
natural products.BMC Bioinformatics20, 728 (2019)doi:10.1186/s12859-019-3183-9
Package: metadmg
A fast and accurate ancient DNA damage toolkit for metagenomic datametaDMGis a novel framework and suite of programs for analysing large-scale genomic data especially in the context of environmental DNA. This includes state-of-the-art statistical methods for computing nucleotide misincorporation and fragmentation patterns of even highly complex samples.For more information, see thedocumentation.
For a quick preview of the interactivity, see thelive dashboard. Do note that the speed of the live dashboard is severely limited since it's running on a free server and thus indicative of the actual speed of the dashboard.Table of contentsGetting startedCiting metaDMGGetting StartedFor information about how to get started runningmetaDMG, see the section in thedocumentation.CitingmetaDMGHere will be more information once our paper is released.
Package: metadocuments
MetaDocumentsInstallationpip install metadocumentsUsageDecorate the document with@Metadocumentto specify that it (and all its children) should be treated
as a metadocument. This adds functionsto_dict,to_jsonandto_yamlto the class instance which
then can be called to create a corresponding structure. Classes can contain methods, but those are ignored
unless they are decorated with@property. Inheritance and multiple inheritance do work, the@Metadocumentdecorator is only required for the parent class.Example@MetaDocumentclassSubObject:a=2b="c"@MetadocumentclassMyClass:a=1b=SubObject()print(MyClass().to_json(indent=4))# {#     "a": 1,#     "b": {#         "a": 2,#         "b": "c"#     }# }Example 2@MetadocumentclassMyClass:a=1classChild(MyClass):b=2c="c"print(Child().to_json(indent=4))# {#     "a": 1,#     "b": 2,#     "c": "c"# }Example 3UsingFromKeywordshelper class. This can be helpful when there are structures that one does not want
to define as class. All the keywords are mapped 1:1 to a attribute / key.if__name__=="__main__":@MetadocumentclassFoobar():a=1b=Field(key="v-1",value="somevalue")c=FromKeywords(d="a",e="b",f=FromKeywords(g=1,asd=FromKeywords(a="b")))print(Foobar().to_json(indent=4))LimitationsBecause of Python syntax abuse, there is no support for numeric keys unfortunately. If those are necessity, then
old-school dicts are best option (for now).FromKeywordsdoes inheritdictso it is possible to use it inside a dictionary which then can be serialized to JSON.if__name__=="__main__":@MetadocumentclassFoobar:a=1b=2c=FromKeywords(d="a",e="b",f=FromKeywords(g=1,asd=FromKeywords(a="b")))some_dict={"foo":"bar",1:"asd","afgg":FromKeywords(a=1,b=2),"ccc":Foobar().to_dict()}print(Foobar().to_json(indent=4))print(json.dumps(some_dict))
Package: metador-core
metador-coreThe core library of the Metador framework. It provides:an interface for managing structured and validated metadata (MetadorContainer)an API to manage immutable (but still "patchable") HDF5 files (IH5Record)an extensible entry-points based plugin system for defining plugin groups and pluginscore plugin group types and interfaces (schemas, packers, widgets, ...)general semantically aligned schemas that can be used and extendedvisualization widgets for common data types based on Bokeh and Panelgeneric dashboard presenting (meta)data for which suitable widgets are installedInstallationYou can install the current stable version of Metador from PyPI:pip install metador-coreGetting StartedIf you successfully installed the package, check out thetutorial notebooks.These are intended to showcase what Metador has to offer and get you started with usage
and development of your own schemas, widgets or other plugins.To explore the notebooks interactively, clone this repo, install it, and then run:pip install notebook
jupyter notebook ./docs/notebooksYou can use themetador-extension-cookiecuttertemplate to generate a well-structured Python package repository that you can use to
quickly get started with Metador plugin development.Compatibility and Known IssuesCurrently this package supports Python>=3.8.We will try to support all still officially updated versions of Python,
unless forced to drop it for technical reasons.You can find more information on using and contributing to this repository in thedocumentation.How to CiteIf you want to cite this project in your scientific work,
please use thecitation filein therepository.AcknowledgementsWe kindly thank allauthors and contributors.This project was developed at the Institute for Materials Data Science and Informatics
(IAS-9) of the Jülich Research Center and funded by the Helmholtz Metadata Collaboration
(HMC), an incubator-platform of the Helmholtz Association within the framework of the
Information and Data Science strategic initiative.
Package: metadownload
metadownload此脚本为下载NCBI中biosample相关字段信息的python脚本Installationpip3 install metadownloadDependencyE-utilitiesyou should add E-utilities in your PATHE-utilities install:sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh)"

sh -c "$(wget -q ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh -O -)"Usageusage: metadownload -list <biosample_acc_list> -outdir <output>

Author: Qingpo Cui(SZQ Lab, China Agricultural University)

optional arguments:
  -h, --help      show this help message and exit
  -list LIST      <file>: biosample_acc_list, one accession per line
  -outdir OUTDIR  <path>: output directory name
  -v, --version   Display versionOutputYou will get text file contains metainfo for each biosample accession named with the accession number and a csv format summary file
Package: metadynminer
metadynminer.pyMetadynminer is a package designed to help you analyse output HILLS files from PLUMED metadynamics simulations.It is based on Metadynminer package for R programming language, but it is not just a port from R to Python, as it is updated and improved in many aspects. It supports HILLS files with one, two or three collective variables.All these functions can be easily customized with many parameters. You can learn more about that later in the documentation. There are also other predefined functions allowing you to for example to enhance your presentation with animations of your 3D FES or remove a CV from existing FES.Installation:pipinstallmetadynminerorcondainstall-cjan8bemetadynminerSample code:Load your HILLS file:hillsfile=metadynminer.Hills(name="HILLS",periodic=[True,True])Compute the free energy surface using the fast Bias Sum Algorithm:fes=metadynminer.Fes(hillsfile)You can also use slower (but exact) algorithm to sum the hills and compute the free energy surface
with the option original=True. This algorithm was checked and it gives the same result
(to the machine level precision) as the PLUMED sum_hills function (for plumed v2.8.0).fes2=metadynminer.Fes(hillsfile,original=True)Visualize the free energy surface and save the picture to a file:fes.plot(png_name="fes.png")Find local minima on the FES, print them and save FES with minima as a picture:minima=metadynminer.Minima(fes)print(minima.minima)minima.plot(png_name="fes.png")You can also plot free energy profile to see, how the differences between each minima were evolving
during the simulation. Convergence in the free energy profile suggests, that the resulting free energy surface converged to correct values.fep=metadynminer.FEProfile(minima,hillsfile)fep.plot(png_name="FEProfile.png")
Package: meta-edc
META EdcMetformin treatment in Africa- METAhttps://www.lstmed.ac.uk/research/departments/international-public-health/respond-africa/metapy 3.11 / DJ 4.2.7 / edc 0.5.37 (META3)This codebase is used for two randomized clinical trials:META PHASE II:(final version0.1.77)Metformin Treatment for Diabetes Prevention in Africa: META Trial
TASO, MRC/UVRI/LSHTM, NIMR – TZ and Liverpool School of Tropical Medicine (ISRCTN76157257)http://www.isrctn.com/ISRCTN76157257META PHASE III:A randomised placebo-controlled double-blind phase III trial to determine the effects of metformin versus placebo on the incidence of diabetes in HIV-infected persons with pre-diabetes in Tanzania.Liverpool School of Tropical MedicineEDCTP grant number: RIA2018CO-2513Trial registration: ISCRTN 77382043https://ico.org.uk/ESDWebPages/Entry/Z4763134See alsohttps://github.com/clinicedc/edcTo setup a demo system, seehttps://github.com/meta-trial/meta3-sample
Package: metaendpoints
# Meta EndpointsСборка и запуск api на основе GRPC + Google Cloud Endpoint# Области примененияХорошо подходит почти для всех api.Ограничения:Не может отдавать через себя ничего кроме JSON, а значит файлы на скачивание# Структура каталоговdocker: по папкам образы для для работы соответствующих toolsmetaendpoints.tools: Скрипты для упрощения работы с ESP в непосредственных проектах, использующих Meta Endpoints
Package: metaextract
metaextract is a tool to collect metadata about a python module. For example
you may have a sdist tarball from thePython Package Indexand you want to
know it’s dependencies. metaextract can collect theses dependencies.
The tool was first developed inpy2packbut is now it’s own module to be
useful for others, too.DocumentationYou can find the documentation onreadthedocs.Hacking and contributingForkthe repositoryon Github to start making your changes to themasterbranch (or branch off of it). Don’t forget to write a test for fixed issues or
implemented features whenever appropriate. You can invoke the testsuite from
the repository root directory viatox:$toxBugsIf you run into bugs, you can file them in theissue tracker.
Package: metafanstatic
create depenency configdownload repository files via dependency configcreate config for generating package via scannig downloaded repositorygenerating fanstatic packages$metafanstaticsearchbootstrap-timepicker# 1. create depenency config$metafanstaticdependency--recursivebootstrap-timepicker>config.json# 2. download repository files via dependency config$metafanstaticdownload--configconfig.json# 3. create config for generating package via scannig downloaded repository$metafanstaticscan`ls|grep-v*.json`>generate.config.json# 4. generating fanstatic packages$metafanstaticcreategenerate.config.json.after above instraction$lsautotypegenerate.config.jsonmeta.js.bootstrap-timepickerbootstrap-2.3.2jquery-2.0.3meta.js.jquerybootstrap-timepicker-0.2.6meta.js.autotypeconfig.jsonmeta.js.bootstrapafter install package# for i in meta.*; do popd $i; python setup.py develop; popd; done$metafanstaticlistfromjs.autotypeimportjquery_autotype_js;jquery_autotype_js.need()fromjs.bootstrapimportbootstrap_css;bootstrap_css.need()fromjs.bootstrapimportbootstrap_js;bootstrap_js.need()fromjs.bootstrap_timepickerimportbootstrap_timepicker_min_css;bootstrap_timepicker_min_css.need()fromjs.bootstrap_timepickerimportbootstrap_timepicker_min_js;bootstrap_timepicker_min_js.need()fromjs.jqueryimportjquery_js;jquery_js.need()
Package: metafid-data
metafidاین پکیج برای پوششِ بخشی از داده‌های موردِ نیاز در تصمیم‌هایِ مالی-اقتصادی توسعه داده شده استinstallmac , linuxpython3-mpipinstallmetafid-datawindowspython-mpipinstallmetafid-datadataise (Iran Stock Exchange)TSETMC (tsetmc.com)frommf_data.iseimportTSETMCtsetmc=TSETMC()# ------------- market watch -------------stock_mw=tsetmc.market_watch(stock=True)#stoockifb_paye_mw=tsetmc.market_watch(ifb_paye=True)# پایه‌یٍ فرابورسmortgage_mw=tsetmc.market_watch(mortgage=True)# اوراقٍ مسکن# cum_right, bond, option, futures, etf, commodity دیگر پارامترها شامل اینها است که می‌توان با-هم هم افزوده شوندm_mw=tsetmc.market_watch(stock=True,cum_right=True,etf=True)# ------------- option market watch -------------tsetmc.option_market_watch()# ------------- instrument info -------------tsetmc.stock_info()tsetmc.option_info()tsetmc.etf_info()# ------------- history price -------------tsetmc.hist_price(symbol_far="شبندر")# اگر میخواهید بر اساس کد نماد داده دریافت کنیدtsetmc.hist_price(ins_code="35366681030756042")# ------------- adjust history price -------------tsetmc.adj_hist_price(symbol_far="شبندر")# اگر میخواهید بر اساس کد نماد داده دریافت کنیدtsetmc.adj_hist_price(ins_code="35366681030756042")Codal(codal.ir)beta version!frommf_data.iseimportCodalcodal=Codal()codal.symbol="شبندر"# ------------- income-statements -------------income_statements=codal.income_statements()# صورتِ سود یا زیانbalance_sheet=codal.balance_sheet()# صورت-وضعیتِ مالیRahavardfrommf_data.iseimportRahavardrah=Rahavard()# ------------- balance-sheet -------------rah.balance_sheet("شبندر")econTGJU (tgju.org)frommf_data.econimportTGJUtgju=TGJU()# ------------- sekke -------------sekke_emami=tgju.sekke_emami()# امامیnim_sekke=tgju.nim_sekke()# نیمrob_sekke=tgju.rob_sekke()# رُبعons=tgju.ons()# xauusd (اُنس جهانی)# ------------- currency -------------usd_irr=tgju.usd_irr()ICB (Iran Central Bank)
Package: metafile-sdk
Metafile Python SDKSDK demoimporttimefrommetafile_sdkimportMetafilefrommetafile_sdk.bitsvimportPrivateKeyfrommetafile_sdk.api.metasvimportMetasvApifrommetafile_sdk.api.sensible_queryimportSensibleQueryApi# metafile protocol node of your metaidmetafile_protocol=''# wif that can add child nodes under your metafile protocol nodewif=''# path to the filefile_path=''defwhatsonchain_demo():metafile=Metafile()pk:PrivateKey=PrivateKey(wif)print(pk.address)print(pk.get_balance())t1=time.time()txid=metafile.upload_metafile_from_path(pk,metafile_protocol,file_path)t2=time.time()print('main txid',txid)print('main txid',f'https://metafile.id/download/{txid}')print('main txid',t2-t1)defsensible_query_demo():metafile=Metafile()pk:PrivateKey=PrivateKey(wif)# use sensible query apisensible_query_api=SensibleQueryApi()pk.network_api=sensible_query_apiprint(pk.address)print(pk.get_balance())t1=time.time()txid=metafile.upload_metafile_from_path(pk,metafile_protocol,file_path)t2=time.time()print('main txid',txid)print('main txid',f'https://metafile.id/download/{txid}')print('main txid',t2-t1)defmetasv_demo():metafile=Metafile()pk:PrivateKey=PrivateKey(wif)# use metasv apimetasv_api_key=''metasv=MetasvApi(metasv_api_key)pk.network_api=metasvprint(pk.address)print(pk.get_balance())t1=time.time()txid=metafile.upload_metafile_from_path(pk,metafile_protocol,file_path)t2=time.time()print('main txid',txid)print('main txid',f'https://metafile.id/download/{txid}')print('main txid',t2-t1)defmain():#  pip install metafile-sdk# whatsonchain_demo()sensible_query_demo()# metasv_demo()if__name__=='__main__':main()
Package: metafinder
MetaFinder - Metadata search through Search EnginesInstallation:> pip3 install metafinderUpgrades are also available using:> pip3 install metafinder --upgradeUsageMetaFinder can be used in 2 ways:CLImetafinder -d domain.com -l 20 -o folder [-t 10] -go -bi -baParameters:d: Specifies the target domain.l: Specify the maximum number of results to be searched.o: Specify the path to save the report.t: Optional. Used to configure the threads (4 by default).v: Show Metafinder version.go: Optional. Search in Google. (Default)bi: Optional. Search in Bing.ba: Optional. Search in Baidu. (Experimental)In Codeimport metafinder.extractor as metadata_extractor

documents_limit = 5
domain = "target_domain"
result = metadata_extractor.extract_metadata_from_google_search(domain, documents_limit)
# result = metadata_extractor.extract_metadata_from_bing_search(domain, documents_limit)
# result = metadata_extractor.extract_metadata_from_baidu_search(domain, documents_limit)
authors = result.get_authors()
software = result.get_software()
for k,v in result.get_metadata().items():
    print(f"{k}:")
    print(f"|_ URL: {v['url']}")
    for metadata,value in v['metadata'].items():
        print(f"|__ {metadata}: {value}")

document_name = "test.pdf"
try:
    metadata_file = metadata_extractor.extract_metadata_from_document(document_name)
    for k,v in metadata_file.items():
        print(f"{k}: {v}")
except FileNotFoundError:
    print("File not found")AuthorThis project has been developed by:Josué Encinar García--https://twitter.com/JosueEncinarContributorsFélix Brezo Fernández--https://twitter.com/febrezoDisclaimer!The software is designed to leave no trace in the documents we upload to a domain. The author is not responsible for any
illegitimate use.
Package: metaflow
MetaflowMetaflow is a human-friendly library that helps scientists and engineers build and manage real-life data science projects. Metaflow wasoriginally developed at Netflixto boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.For more information, seeMetaflow's websiteanddocumentation.From prototype to production (and back)Metaflow provides a simple, friendly API that covers foundational needs of ML, AI, and data science projects:Rapid local prototyping,support for notebooks, andbuilt-in experiment tracking and versioning.Horizontal and vertical scalability to the cloud, utilizing both CPUs and GPUs, andfast data access.Managing dependenciesandone-click deployments to highly available production orchestrators.Getting startedGetting up and running is easy. If you don't know where to start,Metaflow sandboxwill have you running and exploring Metaflow in seconds.Installing Metaflow in your Python environmentTo install Metaflow in your local environment, you can install fromPyPi:pipinstallmetaflowAlternatively, you can also install fromconda-forge:condainstall-cconda-forgemetaflowIf you are eager to try out Metaflow in practice, you can start with thetutorial. After the tutorial, you can learn more about how Metaflow workshere.Deploying infrastructure for Metaflow in your cloudWhile you can get started with Metaflow easily on your laptop, the main benefits of Metaflow lie in its ability toscale out to external compute clustersand todeploy to production-grade workflow orchestrators. To benefit from these features, follow thisguideto
configure Metaflow and the infrastructure behind it appropriately.ResourcesSlack CommunityAn activecommunityof thousands of data scientists and ML engineers discussing the ins-and-outs of applied machine learning.TutorialsIntroduction to MetaflowNatural Language Processing with MetaflowComputer Vision with MetaflowRecommender Systems with MetaflowAnd more advanced contenthereGenerative AI and LLM use casesInfrastructure Stack for Large Language ModelsParallelizing Stable Diffusion for Production Use CasesWhisper with Metaflow on KubernetesTraining a Large Language Model With Metaflow, Featuring DollyGet in touchThere are several ways to get in touch with us:Slack CommunityGithub IssuesContributingWe welcome contributions to Metaflow. Please see ourcontribution guidefor more details.
Package: metaflowbot
Metaflowbot - Slack Bot for your Metaflow flows!Metaflowbot makes it fun and easy to monitor your Metaflow runs, past and present. Imagine starting a training run that lasts for hours - you can now monitor it anywhere using Slack on your mobile device!The bot iseasy to deploy: It is just a Python process with few external dependencies - no databases needed. Itssecurity footprint is smallas it uses only a tightly scoped set of Slack calls. During development you can run the bot on any workstation, so it is quick toiterate on custom actionsand extend it to suit your needs. For production deployments the bot ships with aCloudFormation templatefor automating your deployments to AWS.Communicating with the botThere are two ways interact with the Metaflow bot. You can invite the bot on achannelor directly speak to it viadirect message.@flowey help: Help@flowey tell me a joke@flowey how to inspect: How to inspect@flowey inspect HelloFlow: InspectRuns of a particularFlow@flowey inspect savin's HelloFlow: InspectRuns of a particularFlow@flowey inspect savin's HelloFlow tagged some_tag: InspectRuns of a particularFlow@flowey inspect HelloFlow/12: Inspect an individualRuninstance@flowey inspect the latest run of HelloFlow: Inspect an individualRuninstance@flowey inspect savin's latest run of HelloFlow: Inspect an individualRuninstanceIf you require some customization for your deployment or need additional help, please feel free to reach out to us athttp://slack.outerbounds.co. We are very happy to help!
Package: metaflowbot-actions-jokes
Metaflow bot custom action (Tell me a joke)This repository is an example of a custom action for theMetaflow bot.Installing this actionAfter doingpip install metaflowbot, you can install thejokes-commandby doing apip install metaflowbot-actions-jokes.Jokes CommandThe Jokes command can be installed from this repository.Any custom action needs to follow same folder structure as given by this command.TheMANIFEST.inhelps ensure therules.ymlfile also present when the action is installed.