Saravanan666/pypi-org huggingface dataset:
Package: nafes
nafesA python package for prototype-based feature selectionNafes is a prototype-based feature selection package designed as a wrapper centered on the highly interpretable and powerful Generalized Matrix Learning Vector Quantization (GMLVQ) classification algorithm and its local variant (LGMLVQ). Nafes utilizes the learned relevances evaluated by the mutation validation scheme for Learning Vector quantization (LVQ), which iteratively converges to selected features that relevantly contribute to the prototype-based classifier decisions.Installationnafes can be installed using pip.pipinstallnafesIf you have installed nafes before and want to upgrade to the latest version, you can run the following command in your terminal:
Prosemble can be installed using pip.pipinstall-UnafesTo install the development version from GitHub using Git, run the following command in your terminal:pipinstallgit+https://github.com/naotoo1/nafesBibtexIf you would like to cite the package, please use this:@misc{Otoo_nafes_2023,author={Otoo,NanaAbeka},title={nafes},year={2023},publisher={GitHub},journal={GitHubrepository},howpublished={\url{https://github.com/naotoo1/nafes}},}
Package: naff
What is this?This isNAFF, a python API wrapper for Discord.
NAFF is intended to be fast, easy to use, and easily modified to suit your needs.Features:✅ 100% coverage of the application commands API✅ Dynamic cache with TTL support✅ Modern and Pythonic API✅ Proper rate-limit handling✅ Feature parity with most other Discord API wrappersIs this just anotherDiscord.pyfork?While this library shares features and some stylistic choices withdiscord.py, it is completely separate from them. We thinkdiscord.pyis a fantastic library, but we disagree with the direction and design decisions that were made by it.How do I use this?Here is a basic example:fromnaffimportClient,Button,ButtonStyles,CommandTypes,context_menu,prefixed_command,listenbot=Client(sync_interactions=True)@listen()asyncdefon_startup():print("Ready")print(f"This bot is owned by{bot.owner}")@prefixed_command()asyncdeftest_button(ctx):awaitctx.send("Blurple button example!",components=Button(ButtonStyles.BLURPLE,"Click me"))@context_menu(name="User menu",context_type=CommandTypes.USER,scopes=[931832853770149918])asyncdefuser_context(ctx):awaitctx.send("Context menu example!",ephemeral=True)bot.start("TOKEN")For more examples check outour examples repoor thedocs. You also canexplore projects with theNAFFtopicor olderdis-snektopic.If you get stuck join ourDiscord server."Can I contribute to this project?"Of course, we welcome all contributions to this library. Just ensure you follow ourrequirements.
If youre stuck for things to contribute, check out ourGitHub Projectsfor inspiration.Links:Support Server:https://discord.gg/hpfNhH8BsYDocumentation:https://naff-docs.readthedocs.io/en/latest/
Package: naff-audio
A collection of useful audio scripts forNAFFpip install naff_audioNAFF YTAudioA NAFF audio object for playing audio from YT_DLP.fromnaff_audioimportYTAudioaudio=awaitYTAudio.from_url("https://www.youtube.com/watch?v=ldyqHK0tbSc")awaitvoice_state.play(audio)NAFF QueueA basic queue system.importosfromnaff_audioimportNaffQueuefromnaffimportAudioVolumevc=awaitchannel.connect()queue=NaffQueue(vc)# enqueue all songs in a given folderpath=r"some_dir_of_music/"forsonginos.listdir(path):queue.put(AudioVolume(f"{path}/{song}"))queue.start()# start playing through the queue
Package: naff-ytaudio
NAFF YTAudioA NAFF audio object for playing audio from YT_DLP.Usagefromnaff_yt_audioimportYTAudioaudio=awaitYTAudio.from_url("https://www.youtube.com/watch?v=ldyqHK0tbSc")awaitvoice_state.play(audio)And that's it, audio will be streamed from YouTube to NAFF.If you'd rather download the audio first, simply addstream=False.audio=awaitYTAudio.from_url("https://www.youtube.com/watch?v=ldyqHK0tbSc",stream=False)
Package: nafparserpy
nafparserpyis a lightweight python XML wrapper forNAF.The parser wrapslxmlto handle NAF trees, providing convenience classes for NAF layers and elements.
The resulting objects are decoupled from the underlyinglxmltree: the user is responsible for creating
and handling NAF objects, while the parser handles tree manipulation.To install the parser, run:pip install nafparserpyUSAGE
Package: naga-gwas
Network Assisted Genomic Analysis (NAGA)Network Assisted Genomic Analysis(NAGA) re-prioritizes significant single
nucleotide polymorphisms (SNPs) to genes using network diffusion methods
including random walk and heat diffusion.A companion website and REST API can be found athttp://nbgwas.ucsd.edu/.DocumentationA readthedocs page will be coming soon! In the mean time, you can view
the documentations by building the sphinx documentation in the docs
directory. Simply run the following in the docs foldermakedocsand open the index.html in the docs/build/html directory.InstallationIt is recommended that NAGA be run underAnacondawithpython-igraphmanually installed usingcondaand to create a new conda environmentTo create a newcondaenvironment and activate it:condacreate-nnagaenvsourceactivatenagaenvIf you would like to usenagain a Jupyter Notebook, you will need to add the Jupyter kernel. To do so:# Make sure to activate the environment first!condainstallipykernel# or pip install ipykernelpython-mipykernelinstall--user--namenagaenv--display-name"Python (Naga)"To installpython-igraphviaconda:condainstall-cconda-forgepython-igraphTo install NAGA via pip:pipinstallnaga-gwasTutorialnotebooks/tutorial.ipynbdemonstrates how to use this package once it
is installed.Citing NAGAPublication to come…History0.4.2.post2 (2019-01-30)Added instructions to add Jupyter kernel0.4.1.post1 (2019-01-24)Added link to tutorial notebook in long description0.4.1 (2019-01-09)First release on PyPI
Package: nagao
NagaoAn implementation of the paper:A new method of n-gram statistics for large number of n and automatic extraction of words and phrases from large text data of JapaneseInstallTwo ways to install Nagao:Install Nagao from Pypi:pip install nagaoInstall Nagao from the Github source:git clone https://github.com/Chiang97912/nagao.git
cd nagao
python setup.py installUsageYou can use Nagao in Python file:fromnagaoimportNagaonagao=Nagao(lang='en',min_ngram=2,max_ngram=6,min_freq=5,min_lrc=2,min_lre=0.5,min_pmi=0,min_eta=0,threshold=0,\use_disk=True,use_db=True,lower=True,clean=True,verbose=True)ts=time.time()nagao.process('path/to/corpus/file')nagao.save('path/to/output/file')print('total spend:',time.time()-ts)From the command line, you can run:nagao-c"path/to/corpus/file"-o"path/to/output/file"-lzh--clean--verboseYou can usenagao --helpto find the usage of nagao cli:Options:
  -c, --corpus TEXT           Corpus file path.
  -o, --output TEXT           Output file path.
  -l, --lang TEXT             Corpus language.
  -minn, --min_ngram INTEGER  Minimum n-gram size.
  -maxn, --max_ngram INTEGER  Max n-gram size.
  --min_freq INTEGER          Minimum frequency of word.
  --min_lrc INTEGER           Minimum count between left and right neighbor.
  --min_lre FLOAT             Minimum entropy between left and right neighbor.
  --min_pmi FLOAT             Minimum pmi(pointwise mutual information).
  --min_eta FLOAT             Minimum balanced value for left and right
                              neighbor count.

  --threshold FLOAT           Minimum word probability.
  --stopwords TEXT            Stopword file path.
  --punctuations TEXT         Punctuation file path.
  --lower                     If use lower option, keep lowered dictionary.
  --clean                     If use clean option, the cache file will be
                              cleaned at the end of the program.
  --verbose                   If use verbose option, logs will be displayed on
                              the terminal.

  --help                      Show this message and exit.DependenciesPythonversion 3.6nltkversion 3.5References@newword-py
Package: nagaral26project1
All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Description: UNKNOWN
Platform: UNKNOWN
Package: nagare
FeaturesNagare is a components based framework: a Nagare application is a composition
of interacting components each one with its own state and workflow kept on the
server. Each component can have one or several views that are composed to
generate the final web page. This enables the developers to reuse or write
highly reusable components easily and quickly.Thanks to Stackless Python, Nagare is also a continuation-based web framework
which enables to code a web application like a desktop application, with no
need to split its control flow in a multitude of controllers and with the
automatic handling of the back, fork and refresh actions from the browser.Its component model and use of the continuation come from the famousSeasideSmalltalk framework.Python is a great language and Nagare makes full use of it:the views of the components are Python methods rendering a DOM tree,
built in programmatic XHTML or from a template,the control flow of the components is coded in Python, thanks to the
continuation,data management in DBMS using SQLAlchemy or its declarative layer Elixir,if a Python function is given instead of an expected Javascript code, Nagare
automatically generates a translation from Python to Javascript.Furthermore Nagare integrates the best tools and standard from the Python world.
For example:WSGI: binds the application to several possible publishers,lxml: generates the DOM trees and brings to Nagare the full set of
XML features (XSL, XPath, Schemas …),setuptools: installs, deploys and extends the Nagare framework and the
Nagare applications too,PEAK Rules: generic methods are heavily used in Nagare, to associate views
to components, to define security rules, to translate Python
code to Javascript …WebOb: for its Request and Response Objects.Using Nagare frees the developer from the classic burden of web programming:No more manual URL mapping. Nagare directly associates a Python callback
to a link or a field of a form.No global session object. All the states of all components are kept on
the server.Easy development with Nagare own Python multi-threaded server including
automatic reloading and possible clustering deployment using a memcached
server.Automatic conversion of the parameters received to Unicode and automatic
generation of UTF-8 encoded web pages. Nagare is a full Unicode framework.Possible automatic generation of, always 100% valid, XHTML or HTML according
to the browser capability.Transparent Ajax rendering of a component, without any Python or Javascript
code to add.InstallationFor a standard installation, read thequickstartdocument.Readframework installationto run the
latest development version from theGithub repositoryor to create a Nagare developer installation.
Package: nagare-jquery
AjQueryNagareRenderer for async.Usagefromnagareimportwsgifromnagare.contrib.jqueryimportwsgiclassWSGIApp(wsgi.WSGIApp):"""
    My custom WSGIApp must inherit from nagare-jquery
    """RequirementsPython 2.6+ with nagare latestStackless Python 2.6+ with Nagare >= 0.4.1LicenseBSDRunning Tests$gitclonehttps://github.com/Alzakath/nagare-jquery.git$cdnagare-jquery$nosetests-vChangelogDevv0.0.1Initial release
Package: nagata
nagataWhat is nagata?This readme file offers a basic outline of hownagataworks. If you would prefer to jump right into the full documentation, gohere.Why use nagata?Getting startedInstallationTo installnagata, usepip:pipinstallnagataUsageHow does nagata work?ContributingSimilar ProjectsAcknowledgementsPDMandMkDocsmade mynagatadevelopment workflow better and easier. I'd also like to extend a special thanks topawamoywhose excellent PDM and Mkdocs extensions and utlities are incorporated intonagata. The scripts, documentation, configuration files, and other CI code were all adapted (or simply copied) from pawamoy's repositories.I would also like to thank the University of Kansas School of Law for tolerating and supporting this law professor's coding efforts, an endeavor which is well outside the typical scholarly activities in the discipline.
Package: nagato
local proxy for bypassing warning.or.krInstallYou can install, upgrade, remove nagato usingpip:$pipinstall--usernagato$pipinstall--user--upgradenagato$pipuninstallnagatoUsageYou just execute nagato in your terminal.$nagato-Hlocalhost-p8080-vvthen, you should be config your browser’s config to use nagato proxy: localhost:8080You can see help message with this command.$nagato--help
Package: nagato-ai
🌸 NagatoNagato is a framework that enables any developer to streamline the creation of fine-tuned embedding and language models specifically tailored to a given corpus of dataQuick Start Guide•Features•Key benefits•How it worksFeaturesData ingestion from various formats such as JSON, CSV, TXT, PDF, etc.Data embedding using pre-trained or finetuned models.Storage of embedded vectorsAutomatic generation of question/answer pairs for model finetuningBuilt in code interpreterAPI concurrency for scalalbility and performanceWorkflow management for ingestion pipelinesKey benefitsFaster inference: Generic models often bring overhead in terms of computational time due to their broad-based training. In contrast, our fine-tuned models are optimized for specific domains, enabling faster inference and more timely results.Lower costs: Utilizing fine-tuned models tailored for a specific corpus minimizes the number of tokens needed for accurate understanding and response generation. This reduction in token count translates to decreased computational costs and thus lower operational expenses.Better results: Fine-tuned models offer superior performance on specialized tasks when compared to generic, all-purpose models. Whether you're generating embeddings or answering complex queries, you can expect more accurate and contextually relevant outcomes.How it worksNagato utilizes distinct strategies to process structured and unstructured data, aiming to produce fine-tuned models for both types. Below is a breakdown of how this is accomplished:Unstructured data:Selection of Embedding Model: The first step involves a careful analysis of the textual content to select an appropriate text-based embedding model. Based on various characteristics of the corpus such as vocabulary, context, and domain-specific jargon, Nagato picks the most suitable pre-trained text-based model for embedding.Fine-Tuning the Embedding Model: Once the initial text-based model is selected, it is then fine-tuned to align more closely with the specific domain or subject matter of the corpus. This ensures that the embeddings generated are as accurate and relevant as possible.Fine-Tuning the Language Model: After generating and storing embeddings, Nagato creates question-answer pairs for the purpose of fine-tuning a GPT-based language model. This yields a language model that is highly specialized in understanding and generating text within the domain of the corpus.Structured data:Sandboxed REPL: Nagato features a secure, sandboxed Read-Eval-Print Loop (REPL) environment to execute code snippets against the structured text data. This facilitates flexible and dynamic processing of structured data formats like JSON, CSV or XML.Evaluation/Prediction Using a Code Interpreter: Post-initial processing, a code interpreter evaluates various code snippets within the sandboxed environment to produce predictions or analyses based on the structured text data. This capability allows the extraction of highly specialized insights tailored to the domain or subject matter.
Package: nagcat
nagcatA helpful cat which nags you from the tmux statusbar... because she loves you!(Cat name and pronoun can be customized.)nagcat doesn't actively notify you at a specific time. If a reminder is set for 9 in the morning, she might not notice for a while; so it's not an alarm clock! nagcat will put a little=u.u=in the statusbar sometime after your reminder is set to trigger, as a friendly nag! :)Usenagcat petto dismiss the reminder. For a reminder of what the reminder is, saynagcat why🐈Written with pure Python 3.8+ for Linux or WSL, although it probably works anywhere with modern versions of Python, Bash, and tmux. Please submit an issue or pull request if you have any problems or suggestions for improvement.Quick overviewBy default, nagcat will return=^.^=if you have no reminders pendingBy default, nagcat will return=u.u=if you should drink water, after 14:00, until younagcat petherReminders are stored in a simple JSON file editable withnagcat configTrynagcat -hfor a full list of "commands" (nagcat likes to think of them as "suggestions")Easily customize nagcat's name, pronoun, and appearance -- seenagcat config -hTo remove cat theming:nagcat config -face '' -alert [\!]Installationpip install nagcatto get the latest stable version from PyPIUse nagcat manually by runningnagcatfrom a terminalTo put nagcat output in tmux statusbar:Addrun-shell nagcat.tmuxto the bottom of.tmux.confAdd#{nagcat}to your tmuxstatus-rightorstatus-leftExample setup~/.config/nagcat/reminders.json(safely edit withnagcat config){
    "09:00": "Time for breakfast",
    "14:00": "You should drink water",
}nagcat currently only supports 24-hour time and daily reminders.~/.tmux.conf(nagcatandnagcat.tmuxmust be on $PATH)set -g status-right '#{nagcat}'
run-shell nagcat.tmuxUpdatingpip install --upgrade nagcatshould be sufficient!If something goes wrong, try a "factory reset":nagcat config --reset
Package: nagerapi
Welcome to Nager Public Holiday API’s documentation!OverviewUnofficial Python bindings for theNager Public Holiday API. The goal is to make interaction with the API as easy as possible.Installation & DocumentationpipinstallnagerapiDocumentationcan be found at Read the Docs.Connecting to NagerGetting a NagerObjectAPI InstanceTo connect to theNager Public Holiday APIyou use the~nagerapi.NagerObjectAPIobject.fromnagerapiimportNagerObjectAPInager=NagerObjectAPI()importnagerapinager=nagerapi.NagerObjectAPI()Usage ExamplesExample: List all 2022 US Holidays.In this one we get theUS~nagerapi.CountryObject and callpublic_holidaysfrom that object.fromnagerapiimportNagerObjectAPInager=NagerObjectAPI()country=nager.country("US")forholidayincountry.public_holidays(2022):print(f"{holiday.name}is on{holiday.date.strftime('%Y-%m-%d')}")Alternatively you can callpublic_holidaysfrom the~nagerapi.NagerObjectAPIobject directly providing the country code.fromnagerapiimportNagerObjectAPInager=NagerObjectAPI()forholidayinnager.public_holidays(2022,"US"):print(f"{holiday.name}is on{holiday.date.strftime('%Y-%m-%d')}")HyperlinksNager API DocsUsage & ContributionsSource is available on theGithub Project Page.Contributors to NagerAPI own their own contributions and may distribute that code under
theMIT license.
Package: nagi
I heard a call:This is somewhat of a digression, but since we’re in a place of mass
renaming for most people I was wondering if there was a utility out
there on box that would to that auto, for example, on tvt.com they
have a utility where you select the .torrent file, select a folder,
then the programs matches the files in the torrent to those in the
folder by file size, (highlight those that have the same file size)
and then gives one the option to rename them according to the names
in the torrent, unfortunately it only works for tvt torrents. but if
someone would like to take a look at it and perhaps tweak it i could
upload it.so I stole part ofpyroscopeand hacked up a simple script.I have not tested this on any .torrent/directory combinations it should
do any renaming on, so I’m just assuming it works! No testing on
different OSs or versions of Python, either, so don’t expect much.I think it likely I’ll make a super-simple gui for it, but the cli will
remain.Name unrelated:Nagiis a
character from Hayate no Gotoku.
Package: nagios2trac
nagios2tracLet Nagios create or comment TRAC tickets for youPrivileges needed for Trac userTICKET_MODIFYTICKET_CREATETICKET_VIEWXML_RPCInstallationThe easiest way to install nagios2trac and its dependencies:$ pip install nagios2tracAlternatively install it from source$ git clone git@github.com:Jimdo/nagios2trac.git
$ cd nagios2trac/
$ python setup.py installUsageDefine these notification commands in nagiosservice check:nagios2trac.py --service-state "$SERVICESTATE$ $NOTIFICATIONTYPE$" --host-name "$HOSTNAME$" --description "$SERVICEDESC$" --longoutput "$SERVICEOUTPUT$"host check:nagios2trac.py --service-state "$HOSTSTATE$ $NOTIFICATIONTYPE$" --host-name "$HOSTNAME$" --description "" --longoutput ""Changelog0.5.1use setup from setuptools so the tarball contains all files needed by fpm-cookery0.5fix rst formattingraising to 0.5 because the prior version introduced a new param and
therefore should have increased minor version0.4.1default description_template is more generic nowBugfix: flapping state notifications don’t create a new ticket
anymoreNew param: ticket-owner. (optional) specify the trac ticket owner via
commandline0.4exclude service state when matching full summaryoutsource the description template into an own file in order to make
the code better readablenew config option: description_templatedont create a new ticket when a host or service was acknowledged0.3refactored script in order to make it testableadded unit tests0.2.2BUGFIX: order descending when querying for open trac tickets in order
to always match the latest ticket for comparison0.2.1BUGFIX: do not create a new ticket if a service or host recoversmake it possible to provide newlines in parameters with (this is
useful for multiline longoutput)0.2new param: new_ticket_threshold (minutes)if an open ticket with a different description for one host is found
only post to it if last edit is time is < new_ticket_threshold
minutes. otherwise create a new ticket0.1.1initial release
Package: nagios-audit
A Nagios tool for auditing purpose. This tool reads the nagios status file (status.dat) and provides a report of servers and services which needs to be taken care. It reports the servers and services which are not in OK state, notifications disabled, acknowledged and are in scheduled downtime.Installationpip install nagios-auditUsagenagios-audit --input_file ~/Downloads/status.dat  --output_file ~/tmp/test.csv