Saravanan666/pypi-org huggingface dataset:
Package: mdninja
mdninja + markdown + jinja2 = beautiful HTML documentsmdninjais a document processor that converts markdown documents into HTML document by applying Jinja2 templating.Installationpip install mdninjaUsageSimple use:mdninja doc.md -o doc.htmlIf you want to use a different template:mdninja doc.md -o doc.html --template=stylish.htmlThe default template is:<!DOCTYPE html>
<html>
        <head>
                <title>{% for title in meta.title %}{{title}} {% endfor %}</title>
        </head>
        <body>
                <h1>{% for title in meta.title %}{{title}} {% endfor %}</h1>
                {{ body }}
                <hr/>
        </body>
</html>Metadata (liketitleabove) is added to the document by adding metadata headers like this at the top of the fileTitle: A simple documentAlternatively, you can specify the meatadata using a YAML style header too:---
title: A simple document
---
Package: mdns
mDNS protocol implementationThe library allows you to use mDNS/Zeroconf protocol in
your applications.Also, it contains DNSSEC and Heartbeat extensions.Linkshome:https://github.com/svinota/mdnsbugs:https://github.com/svinota/mdns/issuespypi:http://pypi.python.org/pypi/mdns/Changes1.0.0initial pypi release
Package: mdns-beacon
mDNS BeaconMulticast DNS (mDNS) Beacon to announce multiple CNAME aliases across your local network. Under development. Use by your own risk❗GitHub repo:https://github.com/fedejaure/mdns-beacon.gitDocumentation:https://mdns-beacon.readthedocs.ioFree software: MITFeatures✅ Announce multiple aliases on the local network.✅ Listening utility to discover services during development.❌ Configuration file.❌ Windows support.QuickstartInstallmdns-beaconfrom thePython Package Index:$ pip install mdns-beaconUsage$ mdns-beacon --help
Usage: mdns-beacon [OPTIONS] COMMAND [ARGS]...

  Simple multicast DNS (mDNS) command line interface utility.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  blink   Announce aliases on the local network.
  listen  Listen for services on the local network.Announce an example service:$ mdns-beacon blink example --alias sub1.example --address 127.0.0.1 --type http --protocol tcp
⠋ Announcing services (Press CTRL+C to quit) ...Listen to a specific service type:$ mdns-beacon listen --service _http._tcp.local.

                                       🚨📡 mDNS Beacon Listener 📡🚨
┏━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━┓
┃ # ┃ Type              ┃ Name                           ┃ Address IPv4 ┃ Port ┃ Server              ┃ TTL ┃
┡━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━┩
│ 0 │ _http._tcp.local. │ example._http._tcp.local.      │ 127.0.0.1    │ 80   │ example.local.      │ 120 │
│ 1 │ _http._tcp.local. │ sub1.example._http._tcp.local. │ 127.0.0.1    │ 80   │ sub1.example.local. │ 120 │
└───┴───────────────────┴────────────────────────────────┴──────────────┴──────┴─────────────────────┴─────┘

⠧ Listen for services (Press CTRL+C to quit) ...CreditsThis package was created withCookiecutterand thefedejaure/cookiecutter-modern-pypackageproject template.
Package: mdns-lookup
mdns-lookupPython mDNS lookupThis is based on the Stackoverflow answerhttp://stackoverflow.com/a/35853322/185510.UsageLookup on multiple hostnames:mdnslookupraspberrypi.localhprinter.localOutput:raspberrypi.local 192.168.1.24
hprinter.local 192.168.1.42Installpipinstallmdns-lookupTeststox
Package: mdn-sphinx-theme
This is a version of the Mozilla Developer Network theme, for
theSphinx documentation engine. It is used for theKuma development documentation.Here is how I use itTo use it, you need to clone it somewhere in your Sphinx’s architecture:$ pip install mdn-sphinx-themeand to configure Sphinx to use it. In yourconf.pyfile:import mdn_theme

html_theme_path = [mdn_theme.get_theme_dir()]
html_theme = 'mdn'Take care and remove thepygments_styleconfiguration, as it may not be of
the better taste with the mozilla’s theme.Any contributions are of course welcome!ReadthedocsAdd a pip requirements file to your project and have the following
packages listed there:Sphinx
mdn-sphinx-themeThen configure your Readthedocs project to use that requirement file
before rendering your project’s documentation.
Package: mdns-publisher
ABOUTThis service/library publishes CNAME records pointing to the local host over multicast DNS using theAvahidaemon found in all major Linux distributions. Useful as a poor-man’s service discovery or as a helper for named virtual-hosts in development environments.Since Avahi is compatible with Apple’s Bonjour, these names are usable from MacOS X and Windows too.See also:http://www.multicastdns.orghttps://www.avahi.orghttps://www.apple.com/support/bonjourDEPENDENCIESBesides a running Avahi daemon, this service requires thedbus-pythonbindings which, in turn, requires the development packages for D-Bus and D-Bus Glib (eg.dbus-develanddbus-glib-develin CentOS 7).INSTALLATIONThis package can be installed from source by running:python setup.py build
python setup.py installOr, you can install the latest stable release from PyPI:pip install mdns-publisherNote:If you’re using Python 2.7 and you get errors installingdbus-python, you may need to upgrade yourpipversion:pip install --upgrade pipRUNNINGPass (one or more) CNAMEs as command-line arguments tomdns-publish-cname:mdns-publish-cname name01.local name02.localNames must use the.localdomain but can have arbitrary sub-domains:mdns-publish-cname name01.local name02.local name03.mysubdomain.localIf the server runningmdns-publish-cnameis being announced over mDNS asmyserver.local, all of these names will be answered by Avahi as CNAMEs formyserver.local, regardless of any sub-domains they might have. They remain available as long asmdns-publish-cnameis running.Runmdns-publish-cnamewith no arguments to find out about the available options.Note:You can find a samplemdns-publish-cname.servicefile forsystemdin the source repository.INTEGRATINGTheAvahiPublisherclass in thempublishermodule can be integrated into your application to have it publish its own CNAMEs.
Package: mdoc
mdocModular documentsWith mdoc you can create recursive, modular documents. It has four main features:Include entire files in other documents.Include snippets of files in other documents.Use externally defined variables throughout your documents.Evaluate Python expressions inside your documents.That's the 'modular' part. The 'recursive' part means that the included documents can themselves include other documents, ad infinitum.For example, this readme was generated from the various pieces sitting in thereadmedirectory.mdoc tagsmdoc accomplishes all of this by parsing your input files for mdoc tags. These tags look like:{mdoc include file.ext}, to include the filefile.ext{mdoc snippet eq1 from file.ext}, to include the snippet calledeq1from the filefile.ext{mdoc var1}, to insert the variable calledvar1{mdoc eval expression}, to evaluate the Python expressionexpressionYou might be wondering how, if this readme is generated using mdoc, I was able to type {mdoc ...} above without it being parsed. This is thanks to thestaticoption, which prevents included files from being parsed and includes them verbatim:{mdoc include file.ext static}includesfile.extbut does not parse it for mdoc tags{mdoc include snippet eq1 from file.ext static}includes snippeteq1fromfile.extbut does not parse it for mdoc tagsThere is no static option for variables or evals, since that wouldn't make any sense.Snippets are defined as follows:# Inside file.ext
{mdoc snip snippet_name}
...
snippet contents
...
{mdoc unsnip snippet_name}You can then reference the snippet name and the file it lives in to include it in another document:# Inside main file
{mdoc snippet snippet_name from file.ext}This is very handy for including snippets of code that may change over time, as well as other fluctuating content.Of course, the wordsinclude,snippet,snip,unsnipandevalare reserved and cannot be used as mdoc variables names.usageUsing mdoc is simple, once you've installed it. Simply navigate into the folder where your main document template lives and run:mdoc --input INPUT_DOCUMENT --output OUTPUT_DOCUMENT --variables VARIABLES_JSONThis will parseINPUT_DOCUMENT, insert any variables defined inVARIABLES_JSON, and spit outOUTPUT_DOCUMENT.If you do not wish to generate an output file and simply want to see what the output would look like, you can replace the--output OUTPUT_DOCUMENTwith--dryrun.If you've lost track of all the variables needed throughout your documents, you can use--showvariablesrather than--outputor--dryrunand it will spit out a JSON-formatted list of all the variables you need. You can pipe this into a file to make things really easy!mdoc --input INPUT_DOCUMENT --showvariables > VARIABLES_JSON
Package: mdocfile
mdocfileSerialEM mdoc files as pandas dataframes.mdocfileis Python package for working withSerialEMmdoc files.Quickstartmdocfile.read()will return the contents of an mdoc file as a pandas
dataframe.importmdocfiledf=mdocfile.read('my_mdoc_file.mdoc')For writing valid mdoc files, please seewriting mdoc files.Installationpip:pipinstallmdocfile
Package: mdocker
mdockerAn easy-to-use wrapper for multiplatform Docker image builds.ContentsmdockerContentsDescriptionUsageInstallationFrom PyPI (recommended)Local from sourceNo installation, direct run from sourceDescriptionmdocker is a simple wrapper over Docker Buildx, which can be used for easy image building targeted for multiple platforms.This tool was originally designed as a workaround to alimitationthat Buildx has with the--loadparameter.Note: There are, however, someworkarounds and progresstowards this issue.So, the amount of target platforms specified for this wrapper will be equal to the amount of tags generated in local cache.E.g., iflinux/arm64andlinux/amd64were specified as target platforms for ademoimage, you will getdemo:arm64anddemo:amd64built and stored within your local Docker cache.Usagemdocker requires an installation of Python 3.10+.Below is a help message with the description of arguments.$ python3 -m mdocker --help
usage:  [-h] [--context CONTEXT] [--file FILE] [--platform PLATFORM] [--push]
        name

positional arguments:
  name                 specify a name for the image

options:
  -h, --help           show this help message and exit
  --context CONTEXT    specify a path to build context
  --file FILE          specify a path to Dockerfile
  --platform PLATFORM  specify target platforms (e.g., --platform linux/amd64,linux/arm64)
  --push               push image to remote registryInstallationFrom PyPI (recommended)To install latest mdocker package from PyPI, use:python3-mpipinstallmdockerLocal from sourceTo install and debug mdocker locally, in the root of repository use:python3-mpipinstall-e.No installation, direct run from sourceTo run mdocker without any installation into local cache, in the root of repository use:exportPYTHONPATH=$(pwd)python3-mpoetryinstall--no-root
python3mdocker<arguments>
Package: mdocspoofer
mdocspooferCreate dummySerialEMmdoc files for a set of tomograms collected as dose fractionated movies in Tomo5.This serves to allow processing of cryo-ET data collected in Tomo5 usingWarp.InstallationPython version 3.6+pip install mdocspooferUsageFrom the command line runmdocspooferThis will start an interactive command-line interface, provide the path to the folder containing
the dose-fractionated micrographs and the applied electron dose per tilt image in electrons per square angstrom.To run in non-interactive mode, supply options at the command line. Details can be found usingmdocspoofer --help
Package: mdocument
MDocument is a simple ORM for MongoDB with addition of relations.UsageThere are two ways of using mdocument:
1. Specify database, colleciton and client in class.
2. Use modified mongo motor client.Specifying document parameters in classimportasyncioimportmotor.motor_asynciofrommdocumentimportMDocument,relations,modelclient=motor.motor_asyncio.AsyncIOMotorClient()classVideo(MDocument):__collection__="videos"__database__="mdocument"__client__=clientclassModel(MDocument.Model):title=model.Field(str)views_count=model.Field(int)public_id=model.Field(str,unique=True)classComment(MDocument):__collection__="comments"__database__="mdocument"__client__=clientclassModel(MDocument.Model):text=model.Field(str)video=model.FieldSync(Video,relation=relations.RelationOneToMany,synced_fields=["_id","title"])asyncdefmain():video=awaitVideo.create({"title":"Test"})comment1=awaitComment.create(video=video._id,message="First!",)comment2=awaitComment.create(video=video._id,message="Second!")loop=asyncio.get_event_loop()loop.run_until_complete(main())Now we can easily access our comments using our related documentsTODO: REWRITE DOCUMENTATION!
Package: mdof
mdofFast and friendly system identification for structures.Learn MoreConventional dynamic eigenanalysisis well-known to structural engineers -- from system properties and a given excitation, the goal is to determine the system's dynamic response.output_motion=eigen(M,C,K,input_motion)Themdofsystem id packageallows structural engineers to solveinverse eigenanalysisand related problems -- from structural vibrations, the goal is to identify the system properties.Inverse eigenanalysis:eigvecs,eigvals=eigid(input_motion,output_motion)State space system identification:A,B,C,D=sysid(input_motion,output_motion)Getting StartedClickJupyterLab on DataHub(UC Berkeley users) or(non-UC Berkeley users) to access and experiment with example Jupyter notebooks.Support
Package: mdolab-baseclasses
baseclassesbaseclassescontains, well, base classes that are used together with the rest of MDO Lab tools.
It includes the variousproblemsto be defined by the user in order to perform certain analyses, such asAeroProblemStructProblemAeroStructProblemIt also contains some class definitions shared by various solvers, such asAeroSolver.
Finally, it also contains a class,BaseRegTest, which is used as part of the testing toolchain.
Package: mdopt
mdoptmdopt is a python package built on top of numpy for discrete optimisation in the tensor-network (specifically, MPS-MPO) language. The code is hosted on github, so please feel free to submit issues and pull requests.InstallationUse the package managerpipto install mdopt.pipinstallmdoptUsageFor usage, see the examples folder.Cite@software{mdopt2022,
  author = {Aleksandr Berezutskii},
  title = {mdopt: Discrete optimization in the tensor-network (specifically, MPS-MPO) language.},
  url = {https://github.com/quicophy/mdopt},
  year = {2022},
}
Package: mdorrst
mdorrstTell appart Markdown and reStructuredText.Free software: MIT licenseUsageThe package exposes a single function,sniff(content), trying to
deduce the format used, returning it as a string:md,rstortxt:>>> import mdorrst
>>> mdorrst.sniff("[hey](http://example.com)")
'md'
>>> mdorrst.sniff("`hey <http://example.com>`__")
'rst'CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.0 (2017-04-27)Simplify API.0.3.0 (2017-04-26)Python 2.7 support.0.2.0 (2017-04-26)Exposefrom_text().0.1.0 (2017-04-26)First release on PyPI.
Package: mdown
Installation$[sudo]pipinstallmdownExamplesheader>>>importmdown>>>mdown.header("title",6)'###### Title'code>>>mdown.code("$ [sudo] pip install readme_md","bash")# look Install section :)image>>>mdown.image("https://domain.com/image.png",title="title text",alt="alt text")'![alt text](https://domain.com/image.png "title text")'table>>>mdown.table(("header1","header2"),(("cell11","cell12"),("cell21","cell22")))'header1|header2-|-cell11|cell12cell21|cell22'readme42.com
Package: mdpd
mdpdmdpdis a simpler tool for convert markdown table to pandas.
This tool is a lightweight tool for testing a code, so note that we are not validating the user's input.installpipinstallmdpdusageimportmdpddf=mdpd.from_md("""+------------+-------+| id         | score |+------------+-------+| 1          | 15    || 2          | 11    || 3          | 11    || 4          | 20    |+------------+-------+""")print(df)#   id score# 0  1    15# 1  2    11# 2  3    11# 3  4    20# the header can be overwritten if the header existsimportmdpddf=mdpd.from_md("""| 1          | 15    || 2          | 11    || 3          | 11    || 4          | 20    |""",header=["id","score"])print(df)#   id score# 0  1    15# 1  2    11# 2  3    11# 3  4    20accepted table patterns| Syntax    | Description |
| --------- | ----------- |
| Header    | Title       |
| Paragraph | Text        |+------------+-------------+
| Syntax     | Description |
+------------+-------------+
| Header     | Title       |
| Paragraph  | Text        |
+------------+-------------+| Syntax    | Description |
| :-------- | ----------: |
| Header    | Title       |
| Paragraph | Text        || Header    | Title       |
| Paragraph | Text        |contributeIf you have suggestions for features or improvements to the code, please feel free to create an issue or PR.
Package: mdpdf
mdpdfPython command line application to convert Markdown to PDF.Project FeaturesBare-bones: Only supports basicCommonMark"One-size-fits-all" style: Left-aligned, PDF-base14 fonts. Reasonably pretty, but if you want more control, see alternatives below.Headings are transformed to PDF bookmarks.File links are transformed into attachments with PDF links.Images links are transformed into embedded images with optional captions and width specifier.Minimal requirementscommonmarkPyMuMDFclickAlternativesThere are several projects that can be considered if you need something with more features.  Of note:pandoc+latexrst2pdfrinohtypeInstallation$ pip install mdpdfUsage$ mdpdf [OPTIONS] [INPUTS]...Options:-o, --output FILEDestination for file output.  [required]-h, --header <template>Sets the header template.-f, --footer <template>Footer template.-t, --title TEXTPDF title.-s, --subject TEXTPDF subject.-a, --author TEXTPDF author.-k, --keywords TEXTPDF keywords.-p, --paper [letter|A4]Paper size (default letter).--versionShow the version and exit.--helpShow this message and exit.Templates:The<template>is a quoted, comma-
delimited string, containing the left, centre,
and right, fields for the header/footer. Format is"[left],[middle],[right]".Possible values to put here are:Empty stringArbitrary textSpecial variables:{page}current page number{header}current top-level body text heading{date}current dateExample:$ mdpdf -o article.pdf article.md

$ mdpdf -o article.pdf --footer "{date},{heading},{page}" article.md
Package: md-planning
md-planningpackage to define, visualize with gantt and budget projects using yaml syntax.The tasks in the projects can containuncertaintiesin which case the pert beta estimate is used to define the expected duration of the task.A user should be able to integrate with extensions like markdown-preview-enhanced for automated report creation.sources:https://pypi.org/project/python-gantt-csv/https://pypi.org/project/python-gantt/https://github.com/nofaralfasi/PERT-CPM-graphhttps://github.com/andrewdieken/pert_estimatorFree software: MIT licenseDocumentation:https://md-planning.readthedocs.io.Featuresdefine a project with yaml syntaxdraw project task PERT diagramget project task critical pathdraw project resource usage diagramdraw project gantt diagram on daily, weekly, monthly and quarterly basismake a budget estimate from tasks’ resource usesupport for nested tasks (nested milestones/yaml syntax)CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.0DONE: (feature) nested milestones and tasksDONE: (refactor) consider remove get_task_type registration for List typeDONE: (feature) get_critical_path implementation in GanttDrawer (consider refactoring PertDrawer out?)DONE: (feature) when task depends on milestone, replace milestone dependency with the last critical task in this milestone but do not make this task critical globally in the projectrelease on pypi (2022-09-29)0.3.0getting used to bumpversion0.2.0getting used to pypi0.1.0 (2022-09-20)botched First release on PyPI.
Package: md-plot
This function creates a MD-plot for each column of the dataframe. The MD-plot is a visualization
for a boxplot-like Shape of the PDF published in [Thrun/Ultsch, 2019]. It is an improvement of
violin or so-called bean plots and posses advantages in comparison to the conventional well-known
box plot [Thrun/Ultsch, 2019]. This is the Python implementation of the function MD-Plot contained
in R package [DataVisualizations](https://cran.r-project.org/web/packages/DataVisualizations/index.html)Basic Usagefrom md_plot import MDplot, load_examplesdctExamples = load_examples()MDplot(dctExamples[“BimodalArtificial”])
Package: mdplus
MDPlus: Python tools for molecular modelling.IntroductionMDPlusbrings together a number of tools related to the setup and analysis of molecular simulations:PCA - the Principal Component Analysis library as used bypyPcazip.Glimps - a machine learning method for backmapping coarse-grained structures to finer-grained ones.ENM - an elastic network approach to the refinement of approximate molecular models.Installation:Easiest via pip:pip install mdplusGetting started:API overviewFor maximum compatibility with other Python-based MD simulation processing packages, All tools operate on simple arrays of coordinate data (typically [n_frames, n_atoms, 3]numpyarrays).All tools have a similar API, modelled on thetransformerobject approach that used by many of the utilities inscipyandscikit-learn.PCAAn instance of a PCA transformer is fit to an ensemble of structures, to obtain the eigenvectors, eigenvalues and mean. This trained transformer can then be used to transform further coordinate sets of the same system into the PCA space, and vice-versa:from mdplus.pca import PCA

pca_transformer = PCA()
pca_transformer.fit(traj) # traj should be an [n_frames, n_atoms, 3] numpy array
scores = pca_transformer.transform(traj) # scores will be an [n_frames, n_components] numpy array
reconstituted_traj = pca_transformer.inverse_transform(scores)GlimpsWhile tools to transform high-resolution models to lower resolution ones (e.g. atomistic to coarse-grained) are relatively available and/or easy to implement, the reverse - "back-mapping" - is typically much harder. Given a training set of high-resolution structures and their low-resolution counterparts obtained by application of a forward-mapping tool, Glimps learns the reverse transform from the low resolution dataset to the higher resolution one, and once trained can back-map further low-resolution models.from mdplus.multiscale import Glimps

backmapper = Glimps()
backmapper.fit(cg_training_traj, fg_training_traj) # matched pairs of low and high resolution structures
fg_structure = backmapper.transform(cg_structure)Command-line tools for back mapping are also available - see theglimps_examplefolder.ENMENM implements an elastic network restraints-based refinement of approximate molecular structures. It can be a useful post-processor for structures obtained from PCA inverse transforms or from Glimps. ENM learns the set of restraints from a training set of "good" molecular structures, and can then refine further approximate structures:from mdplus.refinement import ENM

refiner = ENM()
refiner.fit(training_traj) # A diverse collection of good-quality structures
refined_traj = refiner.transform(crude_traj)Who do I talk to?charles.laughton@nottingham.ac.uk