Saravanan666/pypi-org huggingface dataset:
Package: minchin.pelican.plugins.post-stats
Post Statsis a plugin forPelican,
a static site generator written in Python.Post Statscalculates various statistics about a post and store them in
an article.stats dictionary:wc: how many wordsread_mins: how many minutes would it take to read this article, based
on 250 wpm
(source)word_counts: frquency count of all the words in the article; can be
used for tag/word cloudsfi: Flesch-Kincaid Index/Reading Ease
(more info)fk: Flesch-Kincaid Grade LevelInstallationThe easiest way to installPost Statsis through the use of pip. This
will also install the required dependencies automatically.pipinstallminchin.pelican.plugins.post_statsOn Pelican versions 4.5 and later, the plugin will automatically activate
itself!You may also need to configure your template to make use of the statistics
generated.RequirementsPost Statsdepends on (and is really only useful with) Pelican. The
plugin also requries Beautiful Soup 4 to process your content. If the plugin
is installed from pip, these will automatically be installed. These can also
be manually installed with pip:pipinstallpelicanpipinstallbeautifulsoup4Configuration and UsageThis plugin calculates various statistics about a post and store them in
an article.stats dictionary.Example:{'wc':2760,'fi':'65.94','fk':'7.65','word_counts':Counter({u'to':98,u'a':90,u'the':83,u'of':50,...}),'read_mins':12}This allows you to output these values in your templates, like this, for
example:<ptitle="~{{article.stats['wc']}}words">~{{article.stats['read_mins']}}min read</p><ul><li>Flesch-kincaid Index/ Reading Ease:{{article.stats['fi']}}</li><li>Flesch-kincaid Grade Level:{{article.stats['fk']}}</li></ul>Theword_countsvariable is a pythonCounterdictionary and looks
something like this, with each unique word and it’s frequency:Counter({u'to':98,u'a':90,u'the':83,u'of':50,u'karma':50,.....and can be used to create a tag/word cloud for a post.There are no user-configurable settings.CreditsOriginal pluginby Duncan Lock (@dflock) and
posted to thePelican-Plugins repo.LicenseThe plugin code is assumed to be under the AGPLv3 license (this is the
license of the Pelican-Plugins repo).
Package: minchin.pelican.plugins.wikilinks
Support Wikilinks when generating Pelican sites.Wikilinksis a plugin forPelican,
a static site generator written in Python.InstallationThe easiest way to installWikilinksis through the use of pip. This
will also install the required dependencies automatically.pipinstallminchin.pelican.plugins.wikilinksFurther configuration will depend on the version of Pelican you are running. On
version 4.5 or newer and you haven’t definedPLUGINSin yourpelicanconf.py, nothing more in needed. On earlier versions of Pelican, or
if you’ve definedPLUGINS, you’ll need to add the autoloader to your list
of plugins in yourpelicanconf.pyfile:# pelicanconf.pyPLUGINS=[# ...'minchin.pelican.plugins.wikilinks',# ...]Usage NotesIn basic usage, this allow links of the form[[ my work ]]or[[ my work | is finished ]]. Both of these will create a link to a file
namedmy work(e.g.my work.md). By default, the name displayed for the
link will be the filename; alternately add a title to the link by using a bar
and anything after the bar will be used as the displayed name
(e.g.| is finished).Known IssuesThe plugin relies on each link target having a unique filename; non-unique
filenames may result in links not going where you were expecting.Perhaps this should be added as token of a Markdown reader, but the link target
list is only available after all sources have been rendered. Because the plugin
is run after Markdown (or ReStructured) is rendered, there currently isn’t a
way to make sure that this isn’t run on links within code blocks, etc.Future To Dossupport link anchors (e.g.my work#heading)
Package: minchin.pelican.themes.minchindotca
Minchin dot CAis a theme forPelican,
a static site generator written in Python.TheMinchin dot CAtheme is based on Bootstrap 3, and was first used atMinchin.ca.InstallationThe easiest way to install theMinchin dot CAtheme is through the use
of pip. This will also install the required dependencies automatically.pipinstallminchin.pelican.themes.minchindotcaThen, in yourpelicanconf.pyfile, import the modele, use the
built in function to specify your theme location, set the default
colour scheme, set the image processing patterns used, and add some Jinja
filters that the theme uses:fromminchin.pelican.themesimportminchindotcaTHEME=minchindotca.get_path()BOOTSTRAP_THEME='minchindotca'IMAGE_PROCESS={'article-feature':["scale_in 848 848 True"],'index-feature':["scale_in 263 263 True"],}# Jijna2 filtersdefdatetimefilter(value,format='%Y/%m/%d%H:%M'):"""convert a datetime to a different format."""returnvalue.strftime(format)defarticle_date(value):"""Converts a date to the format we want it displayed on the article
       template.
    """returnvalue.strftime('%A, %B%-d, %Y')defbreaking_spaces(value):"""Converts non-breaking spaces to regular spaces."""returnvalue.replace('\u00A0',' ')JINJA_FILTERS={'datetimefilter':datetimefilter,'article_date':article_date,'breaking_spaces':breaking_spaces,}You will may also need to configure the theme through the use of additional
settings (see below).RequirementsMinchin dot carequires Pelican and theimage_processplugin.
This can be manually installed with pip:pipinstallpelicanminchin.pelican.plugins.image_processAdditional SettingsDetails coming. In the meantime, refer to the settings on theBootstrap 3
theme.CreditsOriginal theme developed byDaan Debie.The idea that a theme could be installed as a Python package byJeff
Forcier’sAlabaster themefor Sphinx.
Package: minchoc
minchocMinimalChocolatey-compatible NuGet server in a Django app.InstallationpipinstallminchocInsettings.py, add'minchoc'toINSTALLED_APPS. SetALLOW_PACKAGE_DELETIONtoTrueif you
want to enable this API.INSTALLED_APPS=['minchoc']ALLOW_PACKAGE_DELETION=TrueADELETEcall to/api/v2/package/<id>/<version>will be denied even with authentication unlessALLOW_PACKAGE_DELETIONis set toTrue.Addpath('', include('minchoc.urls'))to your rooturls.py. Example:fromdjango.urlsimportinclude,pathurlpatterns=[path('admin/',admin.site.urls),path('',include('minchoc.urls')),]Run./manage.py migrateor similar to install the database schema.NotesWhen a user is created, aNugetUseris also made. This will contain the API key for pushing.
It can be viewed in admin.Add your source to ChocolateyAs administrator:chocosourceadd-s'https://your-host/url-prefix'chocoapikeyadd-s'https://your-host/url-prefix'-k'your-key'On non-Windows platforms, you can use mypychocopackage, which
also supports the above commands.Supported commandschoco installchoco pushchoco search
Package: mincluster
minclustermincluster is a package that allows to minimize the maximum intercluster distanceOverviewThe problem of clustering a set of points so as to minimize the maximum intercluster distance is studied. An O(kn) approximation algorithm, where n is the number of points and k is the number of clusters, that guarantees solutions with an objective function value within two times the optimal solution value is presented. This approximation algorithm succeeds as long as the set of points satisfies the triangular inequality. We also show that our approximation algorithm is best possible, with respect to the approximation bound, if P ≠ NP.InstallationpipinstallminclusterUsagefrommincluster.clusterimportClusterMinimizationk=3data=[231,22,73,54,60,29,10,192,115]c=ClusterMinimization(k,data)clusters,cluster_heads=c.expand_clusters(c.clusters,c.cluster_heads)try:foriinrange(k):print(f'Cluster{i+1}: Head ={cluster_heads[i]}, Elements ={clusters[i]}')exceptIndexErrorase:print(e)OutputCluster 1: Head = 60, Elements = {192, 231, 73, 10, 115, 54, 22, 29}Cluster 2: Head = 231, Elements = {192, 231, 73, 10, 115, 54, 22, 60, 29}Cluster 3: Head = 115, Elements = {192, 231, 73, 10, 115, 54, 22, 60, 29}LicenseThis project is licensed under the MIT License - see theLICENSEfile for detailsReferencesGonzalez, Teofilo F. “Clustering to Minimize the Maximum Intercluster Distance.” Theoretical Computer Science, vol. 38, Elsevier BV, Jan. 1985, pp. 293–306,https://doi.org/10.1016/0304-3975(85)90224-5. Accessed 26 Oct. 2023.
Package: mincss
Clears the junk out of your CSS by finding out which selectors are
actually not used in your HTML.By Peter Bengtsson, 2012-2018Tested in Python 2.7, 3.3, 3.4 and 3.5Example$ mincss https://github.comInstallationFrom pip:$ pip install mincssWhy?With the onslaught of Twitter Bootstrap upon the world it’s very
tempting to just download their whole fat 80+Kb CSS and serve it up even
though you’re not using half of the HTML that it styles.There’s also the case of websites that have changed over time but
without the CSS getting the same amount of love refactoring. Then it’s
very likely that you get CSS selectors that you’re no longer or never
using.This tool can help you get started reducing all those selectors that
you’re not using.Whitespace compression?No, that’s a separate concern. This tool works independent of whitespace
compression/optimization.For example, if you have a build step or a runtime step that converts
all your CSS files into one (concatenation) and trims away all the
excess whitespace (compression) then the output CSS can still contain
selectors that are never actually used.What about AJAX?If you have a script that creates DOM elements in some sort ofwindow.onloadevent thenmincsswill not be able to know this
because at the momentmincssis entirely static.So what is a web developer to do? Simple, use/* no mincss */like
this for example:.logged-in-info {
    /* no mincss */
    color: pink;
}That tellsmincssto ignore the whole block and all its selectors.Ignore CSSBy default,mincsswill find all<linkrel="stylesheet"...and<style...>tags and process them. If you have a link or an inline
tag that you don’t wantmincssto even notice, simply add this attribute
to the tag:data-mincss="ignore"Leave CSS as isOne technique to have a specific CSS selector be ignored bymincssis to
put in a comment like/* no mincss */inside the CSS selectors
block.Another way is to leave the whole stylesheet as is. The advantage of doing
this is if you have alinkorstyletag that you wantmincssto ignore but still find and include in the parsed result.
Package: mincss3k
Clears the junk out of your CSS by finding out which selectors are
actually not used in your HTML.This is an unofficial fork (ofhttps://pypi.python.org/pypi/mincss) that runs
on both Python 2 and 3.Example$ mincss https://github.comInstallationFrom pip:$ pip install --upgrade mincss3kWhy?With the onslaught of Twitter Bootstrap upon the world it’s very
tempting to just download their whole fat 80+Kb CSS and serve it up even
though you’re not using half of the HTML that it styles.There’s also the case of websites that have changed over time but
without the CSS getting the same amount of love refactoring. Then it’s
very likely that you get CSS selectors that you’re no longer or never
using.This tool can help you get started reducing all those selectors that
you’re not using.Whitespace compression?No, that’s a separate concern. This tool works independent of whitespace
compression/optimization.For example, if you have a build step or a runtime step that converts
all your CSS files into one (concatenation) and trims away all the
excess whitespace (compression) then the output CSS can still contain
selectors that are never actually used.What about AJAX?If you have a script that creates DOM elements in some sort ofwindow.onloadevent thenmincsswill not be able to know this
because at the momentmincssis entirely static.So what is a web developer to do? Simple, use/* no mincss */like
this for example:.logged-in-info {
    /* no mincss */
color: pink;
}That tellsmincssto ignore the whole block and all its selectors.
Package: mindai
mindaimindai is a package has alot of ai tools like chat with ai or code generate and more.ExamplesChat with ai (Offline)+:import mindai

from mindai import Ai



ai = Ai()



chat = ai.chat(message="Hello! How are you?")



print(chat)

#print(chat['reply'])Text from image (OCR) :import mindai

from mindai import Ai



ai = Ai()



ocr = ai.image_to_text(url="https://telegra.ph//file/3381da14831562cb2dd0e.png")



print(ocr)

#print(ocr['text'])Code Generatetor :import mindai

from mindai import Ai



ai = Ai()



code = a.code_gen(prompt="hello world",lang="python")



print(code)

#print(code['code'])Word mean :import mindai

from mindai import Ai



ai = Ai()



means = ai.word_mean(word="men")



print(means)

#print(means['mean'])Audio  to Text (STT) :import mindai

from mindai import Ai



ai = Ai()



tt = ai.audio_to_text(url="https://ttsmp3.com/created_mp3/2e14d53d4285fe4b0d43245902171fcf.mp3")



print(tt)

#print(tt['text'])Explain Code :import mindai

from mindai import Ai



ai = Ai()



ex = ai.explain_code("""

d = 0

print(d)

exit()

""",lang='ar')

## lang None or english or arabic.

print(ex)

#print(ex['explained'])Image Search Engine (Ai Make, Images.) :import mindai

from mindai import Ai



ai = Ai()



eng = ai.image_search(query="Duck")



print(eng) 

#print(eng['images'])Dalle-2 Text-To-Image (New!) :import mindai

from mindai import Ai



ai = Ai()



eng = ai.dalle_2("Duck eat sandwich")



print(eng) 

#print(eng['url'])Complete Setenece  :import mindai

from mindai import Ai



ai = Ai()



eng = ai.complete_setnece("The Goal of life is *") # Must add (*) To complete your setnece.



print(eng) 

#print(eng['setneces'])Text-To-Anime Image!  :import mindai

from mindai import Ai



ai = Ai()



eng = ai.text_to_anime("A Iraqi Men")



print(eng) 

#print(eng['url'])Prompt Extend :import mindai

from mindai import Ai



ai = Ai()



eng = ai.prompt_extend("Elon Musk")



print(eng)

#print(eng['text'])Get Verbs and Names and Company Names :import mindai

from mindai import Ai



ai = Ai()



eng = ai.get_verbs("Hello, my name is Elon and i CEO of SpaceX and Tesla Motors")



print(eng) 

#print(eng['verbs'])Thats all thank you.
Package: mindaudio
MindAudioIntroduction|Installation|Get StartedIntroductionMindAudio is a toolbox of audio models and algorithms based onMindSpore. It provides a series of API for common audio data processing,data enhancement,feature extraction, so that users can preprocess data conveniently. Also provides examples to show how to build audio deep learning models with mindaudio.data processing# read audio>>>importmindaudio.data.ioasio>>>audio_data,sr=io.read(data_file)# feature extraction>>>importmindaudio.data.featuresasfeatures>>>feats=features.fbanks(audio_data)InstallationInstall with PyPIThe released version of MindAudio can be installed viaPyPIas follows:pipinstallmindaudioInstall from SourceThe latest version of MindAudio can be installed as follows:gitclonehttps://github.com/mindspore-lab/mindaudio.gitcdmindaudio
pipinstall-rrequirements/requirements.txt
pythonsetup.pyinstallGet started with audio data analysismindaudio provides a series of commonly used audio data processing apis, which can be easily invoked for data analysis and feature extraction.>>>importmindaudio.data.ioasio>>>importmindaudio.data.spectrumasspectrum>>>importnumpyasnp>>>importmatplotlib.pyplotasplt# read audio>>>audio_data,sr=io.read("./tests/samples/ASR/BAC009S0002W0122.wav")# feature extraction>>>n_fft=512>>>matrix=spectrum.stft(audio_data,n_fft=n_fft)>>>magnitude,_=spectrum.magphase(matrix,1)# display>>>x=[iforiinrange(0,256*750,256)]>>>f=[i/n_fft*srforiinrange(0,int(n_fft/2+1))]>>>plt.pcolormesh(x,f,magnitude,shading='gouraud',vmin=0,vmax=np.percentile(magnitude,98))>>>plt.title('STFT Magnitude')>>>plt.ylabel('Frequency [Hz]')>>>plt.xlabel('Time [sec]')>>>plt.show()Result presentation:What's New2023/06/24: version 0.1.1, bug fix and readme update2023/03/30: version 0.1.0, including 50+ data processing APIs, 5 models supported.2022/09/30: beta, 33 data APIs + 3 modelsContributingWe appreciate all contributions to improve MindSpore Audio. Please refer toCONTRIBUTING.mdfor the contributing guideline.LicenseThis project is released under theApache License 2.0.CitationIf you find this project useful in your research, please consider citing:@misc{MindSpore Audio 2022,
    title={{MindSpore Audio}:MindSpore Audio Toolbox and Benchmark},
    author={MindSpore Audio Contributors},
    howpublished ={\url{https://github.com/mindspore-lab/mindaudio}},
    year={2022}}
Package: mindbox-sdk
Mindbox Python APIWrapper for working with Mindbox service APIInstallationYou can install or upgrade package with:$ pip install mindbox-sdk --upgradeOr you can install from source with:$ git clone https://github.com/RTHeLL/mindbox-sdk
$ cd mindbox-sdk
$ python setup.py install...or install from source buth with pip$ pip install git+https://github.com/RTHeLL/mindbox-sdkExamplefrommindbox.clientimportMindboxClientdefmain():mindbox_client=MindboxClient(endpoint_id="YOUR_ENDPOINT_ID",secret_key="YOUR_SECRET_KEY",type_="async")client=mindbox_client.operations.get_client("Getclient",**{"customer":{"mobilePhone":"+79374134388"}})print(f'Client:{client}')main()BugsIf you have any problems, please create Issueshere
Package: mindconnectiot
A small client to connect with MindConnect IoT using the MindConnect IoT ExtensionInstallationThe easiest way to get started is by usingpip, to get started run:pip install mindconnectiotHow to useFirst initialise a newMindConnectIotobject for your device:frommindconnectiotimportMindConnectIotmindconnect=MindConnectIot(device_name,mindconnect_region,tenant_name,mcio_extension_username,password)Afterwards you can send data to the cloud, e.g. to send a measurement:mindconnect.sendMeasurement('Temperature','Celsius',23.4,'*C')
Package: mindcontrol
===========mindcontrol===========mindcontrol provides an api that allows python programs to capture data thatis emitted by the NeuroSky MindWave headsets. It uses a socket to connect tothe ThinkGearConnector application that acts as a server for the headset data::#!/usr/bin/env pythonfrom mindcontrol.userbrain import Brainfrom time import sleepSLEEP_TIME = 5 # secondsCONCENTRATE_TIME = 5 # secondsmy_brain = Brain()while not my_brain.isConnected():print 'Not connected to brain yet. Will try again in %d seconds' % SLEEP_TIMEsleep(SLEEP_TIME)# brain is now connected, get some stuffprint 'Concentrate really hard for the next %d seconds!' % CONCENTRATE_TIME# sample once each secondconc_vals = []for idx in xrange(CONCENTRATE_TIME):conc_vals.append(my_brain.getProperty('attention'))sleep(1)print 'Your recorded concentration values were:\n\t[%s]' % ', '.join([str(x) for x in conc_vals])collaboration=========This project is GPL licensed so do whatever you want with the code. This module is currently being source controlledon GitHub, so if you want to check out the project you can find it here: https://github.com/georgeBoole/mindcontrol.This module is mostly experimental at the moment, but expect more development soon.
Package: minddatapack
Mind DataPack Python SDKAn Python implementation for Mind DataPackDescriptionMind Network is a permissionless and scalable zero-trust data lake. Its core feature is to compute over encrypted data and allow that data to be stored in various Web3 storage protocols.DataPack, contributed by the Mind Network Team, is to enable data transformation and transportation between Mind Network and storage protocols, like Arweave. It is an adapter that facilitates the smooth conversion of data between plaintext and ciphertext to be stored in Mind Network or Arweave. This module empowers users to retrieve their infrequently processed data, known as "cold data," from Mind Network and store it in local or decentralized storage. When the need arises to perform computing on the encrypted data again, users can effortlessly load it back into Mind Network for processing.Getting StartedDependenciesPython >= 3.8pipmindlakesdkarseedingInstallingpip install minddatapackImportfrom minddatapack import DataPack
...More examplesuse case of arweave in jupyteruse case of IPFS in jupytercodemind-datapack-python
|-- minddatapack # source code
|   |-- __init__.py
|   |-- arweaveconnector.py
|   |-- ipfsconnector.py
|   |-- localfileconnector.py
|   |-- mindlakeconnector.py
|   └-- utils.py
|-- examples # use case examples
|-- README.md
└--- LICENSEHelpFull doc:https://mind-network.gitbook.io/mind-lake-sdkAuthorsDennis@NuIlPtrGeorge@georgemindnetVersion Historyv1.0Initial Releasev1.0.1Add IPFS supportv1.0.3Change connectors to be classLicenseThis project is licensed under the [MIT] License - see the LICENSE.md file for details
Package: minder
MinderException guard capture utility library.InstallationpipinstallminderDemoTheMindercontext manager keeps failure modescontainedwith minimallegibilitycost:fromminderimportMinderdefsucceed()->dict:withMinder()asguard:withguard.duty("winning"):guard.result=100returnguard.report()response=succeed()print(response){'result':100,'success':True}When an error is encountered, we get the same interface.fromminderimportMinderdeffail()->dict:withMinder()asguard:withguard.duty("greet"):print("Hello world")withguard.duty("division"):guard.result=1/0returnguard.report()response=fail()print(response)Helloworld{'result':{'error':'division by zero','where':'division'},'success':False}In this example we expose a reliable interface of aresultandsuccessboolean.We could also returnguard(theMinderinstance) and handle success/failure at the call site,
but the assumption is we would rather have this prepared for us.
Package: minder.dataset-manager
Dataset ManagerLibrary to pre-process CSV files from Research Portal into usable datasets.Installationpipinstallminder.dataset-managerExampleimportloggingimportasyncioimportsysfromtypingimportOptionalfromminder.dataset_manager._utilsimportDatasetfromminder.dataset_manager.datasetsimportLabelledUtiDatasetfromminder.research_portal_clientimportConfiguration,JobManagerlogging.basicConfig(level=logging.INFO)Configuration.set_default(Configuration(access_token="---REDACTED---",))asyncdefexample1():job_ids=["c25249e0-82ff-43d1-9676-f3cead0228b9"]asyncwithJobManager()asjob_manager:files=Dataset.download(job_ids,job_manager)dataset=LabelledUtiDataset.create(job_ids,files)dataset.save("./my-dataset.npz")asyncdefexample2():job_ids=["c25249e0-82ff-43d1-9676-f3cead0228b9"]existing_dataset="./my-dataset.npz"asyncwithJobManager()asjob_manager:download_task=Dataset.download(job_ids,job_manager)try:previous_dataset:Optional[Dataset]=Noneifexisting_dataset.exists():previous_dataset=LabelledUtiDataset.load(existing_dataset)finally:files=awaitdownload_tasknew_dataset=LabelledUtiDataset.create(job_ids,files)dataset=(awaitprevious_dataset.update(new_dataset,job_manager=job_manager)ifprevious_datasetisnotNoneelsenew_dataset)dataset.save("./my-dataset.npz")asyncdefmain():awaitexample1()awaitexample2()ifsys.platform=="win32"::asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())asyncio.run(main())DevelopmentUseful commandsSetuppoetryinstallRun testspoetryrunpytestCode CoverageThis command consists of 2 parts:running tests with coverage collectionformatting the report:report(text to stdout),xml(GitLab compatible: cobertura),html(visual)poetryruncoveragerun-mpytest&&poetryruncoveragereport-mLintingpoetryrunflake8Formattingpoetryrunblack.Type Checkingpoetryrunmypy.