Saravanan666/pypi-org huggingface dataset:
Package: nlbsg
Python wrapper for theNLB Open Web Services.Currently supports theCatalogue Service.Installationpip install nlbsgUsage exampleCreating a client:from nlbsg import Client
from nlbsg.catalogue import STAGING_URL

API_KEY = 'secret_api_key'

client = Client(STAGING_URL, API_KEY)Searching the catalogue:>>> from nlbsg import MediaCode
>>> results = client.search('lord of the rings', author='tolkien', media_code=MediaCode.BOOKS, limit=3)
>>> for title in results.titles:
...     print(f'Title: {title.title_name}\nISBN: {title.isbn}\nPublished: {title.publish_year}\n')
...
Title: Beren and Lúthien / by  J.R.R. Tolkien ; edited by Christopher Tolkien ; with illustrations by  Alan Lee.
ISBN: 1328915336 (paperback)
Published: 2018

Title: Beren and l℗♭©ʻthien [electronic resource]. J. R. R Tolkien.
ISBN: 9780008214210 (electronic bk)
Published: 2017

Title: The fall of arthur [electronic resource]. J. R. R Tolkien.
ISBN: 9780007489954 (electronic bk)
Published: 2013Getting title details:>>> details = client.get_title_details(isbn='1328915336')
>>> details.title_detail.summary
"The epic tale of Beren and Lúthien became an essential element in the evolution of The Silmarillion, the myths and legends of J.R.R. Tolkien's First Age of the World. Always key to the story is the fate that shadowed their love: Beren was a mortal man, Lúthien an immortal Elf. Her father, a great Elvish lord, imposed on Beren an impossible task before he might wed Lúthien: to rob the greatest of all evil beings, Melkor, of a Silmaril.Painstakingly restored from Tolkien's manuscripts and presented for the first time as a continuous and standalone story, Beren and Lúthien reunites fans of The Hobbit and The Lord of the Rings with Elves and Men, along with the rich landscape and creatures unique to Tolkien's Middle-earth. Christopher Tolkien tells the story in his father's own words by giving its original form as well as prose and verse passages from later texts that illustrate the narrative as it changed. -- from back cover."Getting title availability:>>> availability = client.get_availability_info(isbn='1328915336')
>>> for item in availability.items:
...     print(f'Branch: {item.branch_name}\nStatus: {item.status_desc}\n')
...
Branch: Ang Mo Kio Public Library
Status: Not On Loan

Branch: Bukit Batok Public Library
Status: On Loan

Branch: Bedok Public Library
Status: On Loan

Branch: Bishan Public Library
Status: Not On Loan

Branch: Bukit Panjang Public Library
Status: Not On Loan

Branch: Choa Chu Kang Public Library
Status: On Loan

Branch: Central Public Library
Status: Not On Loan

Branch: Clementi Public Library
Status: On Loan

Branch: Cheng San Public Library
Status: On Loan

Branch: Geylang East Public Library
Status: Not On Loan
Package: nlb-tools
NLB Codepack (nlb_tools)Python tools for participating in Neural Latents Benchmark '21.OverviewNeural Latents Benchmark '21 (NLB'21) is a benchmark suite for unsupervised modeling of neural population activity.
The suite includes four datasets spanning a variety of brain areas and experiments.
The primary task in the benchmark is co-smoothing, or inference of firing rates of unseen neurons in the population.This repo contains code to facilitate participation in NLB'21:nlb_tools/has code to load and preprocess our dataset files, format data for modeling, and locally evaluate resultsexamples/tutorials/contains tutorial notebooks demonstrating basic usage ofnlb_toolsexamples/baselines/holds the code we used to run our baseline methods. They may serve as helpful references on more extensive usage ofnlb_toolsInstallationThe package can be installed with the following command:pip install nlb-toolsHowever, to run the tutorial notebooks locally or make any modifications to the code, you should clone the repo. The package can then be installed with the following commands:git clone https://github.com/neurallatents/nlb_tools.git
cd nlb_tools
pip install -e .This package requires Python 3.7+ and was developed in Python 3.7, which is the Python version we recommend you use.Getting startedWe recommend reading/running throughexamples/tutorials/basic_example.ipynbto learn how to usenlb_toolsto load and
format data for our benchmark. You can also find Jupyter notebooks demonstrating running GPFA and SLDS for the benchmark inexamples/tutorials/.Other resourcesFor more information on the benchmark:ourmain webpagecontains general information on our benchmark pipeline and introduces the datasetsourEvalAI challengeis where submissions are evaluated and displayed on the leaderboardour datasets are available on DANDI:MC_Maze,MC_RTT,Area2_Bump,DMFC_RSG,MC_Maze_Large,MC_Maze_Medium,MC_Maze_Smallourpaperdescribes our motivations behind this benchmarking effort as well as various technical details and explanations of design choices made in preparing NLB'21ourSlack workspacelets you interact directly with the developers and other participants. Please emailfpei6 [at] gatech [dot] edufor an invite link
Package: nlcalc
IntroductionThis Package is an example project for thebotifyframework. It can be
used to parse mathematical expressions expressed in natural language
and evaluate the result.Usage>>>fromnlcalcimportNLCalculator>>>my_calc=NLCalculator()>>>result=my_calc.calculate("what is two plus twenty five")>>>print(result)27>>>result=my_calc.calculate("3 square plus four square")>>>print(result)25Installationpip install nlcalcLinksDocumentationSourceAPI ReferenceLicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details.
Package: nlcc
nlccInstallpipinstallnlccYou must have either an OpenAI Codex key or HuggingFace API key (minimum plan level is Community Pro). Make sure they are set viaexport OPENAI_API_KEY=<your key here>orexport HUGGINGFACE_API_KEY=<your key here>thennlcccitationPlease citeour preprint@article{hocky2022natural,title={Natural language processing models that automate programming will transform chemistry research and teaching},author={Hocky, Glen M and White, Andrew D},journal={Digital Discovery},year={2022},publisher={Royal Society of Chemistry}}key bindingsctrl-wcopy to clipboard (Note, you may need to install xsel on linux)ctrl-qhelpctrl-oreset contextctrl-zexecute python codectrl-tadjust temperaturectrl-ustatusctrl-cquitctrl-xwrite to file (same output as copy to clipboard)ctrl-lload from filectrl-nupdate number of code responsesctrl-downEnter multiline.escape-enterto enter prompt orctrl-downto leave[insert]Add this to your multiline prompt to have completion inserted in a specific location
Package: nlcLatex
nlcLatexThis this the very first version. I removed logging to keep this very light and small.
Logging and error responses will be added later.Tools to create Latex Documents.Processes files with extension.pytexinside any include / input is checked if.pytexexists -> process this file.pyexists -> run as python. The script is expected to write a.texfile with the same name.Process:load file and create / return documentdoc = nlcLatex.load(<filename>)process the includes within documentdoc.process()write LaTeX filedoc.write()runpdflatexdoc.run_latex()nlcLatex.processtakes the name of a.pytex-file and executes the steps mentioned above.Providing Parameters (for Python scripts)Parameters can be provided with in the respective\inclucdeor\inputstatement in two formats:\input[p1][p2][with space]{script-name}\input[p1 p2 p3]{script-name}The first form allows parameters with spaces, the second form is split at the whitespace to
separate the parameters.Note: I typically use the first parameter to signify the LaTeX section level, numerical from -1 to 5, or using the
respective names (e.g. 'chapter').Public InterfaceFunctions:nlcLatex.load(<file-name>)ornlcLatex.Document(<file-name>)Expects a.pytex-file that conforms to a regular LaTeX document with the possible
exception of\includeand\inputformat.Returns an instance ofnlcLatex.DocumentnlcLatex.get_level(<level>, delta=0)Returns the respective Latex-Name of the sectioning level, can be provided as integer or
name. Delta allows to calculate up or down.intname-1part0chapter1section2subsection3subsubsection4paragraph5subparagraphTip: Might be used like this:f'\\{get_level(current, 1)}[Toc-Entry]{{Text}}nlcLatex.latex_safe(<string>, visiblespace=False)Returns a string that has special Latex-characters replaced with their respective escape sequences:charreplacementcharreplacement&\&%\%$\$#\#_\_{\{}\}Unicode Character 'ZERO WIDTH SPACE'[U+200B]●[U+25CF]×[U+00D7]→$\rightarrow$~\textasciitilde{}^\textasciicircum{}Ifvisiblespace=True, spaces will be replaced with\textvisiblespace{}(␣)nlcLatex.index_page(level=1)Returns string to insert toc-entry for Index and issues\printindexnlcLatex.ArgParserWrapper aroundargparse.ArgumentParserto remove double quotes around parameters (they are required to protect spaces).ExampleThe following files can be stored in the same directory and show an example:example01.pyimportpathlibfromnlcLateximportget_level,latex_safe,ArgParserdefmain(args):path=pathlib.Path(__file__)withopen(path.with_suffix(".tex"),"w",encoding="utf8")asf:f.write("\\"f"{get_level(args.level)}""{Test}\n\n")f.write(latex_safe(args.text,visiblespace=True))if__name__=="__main__":parser=ArgParser()parser.add_argument("level")parser.add_argument("text")main(parser.parse_args())report.pytex\documentclass{article}\usepackage[utf8]{inputenc}\begin{document}\title{Example Article}\author{Automated with\texttt{nlcLatex}}\maketitle\tableofcontents\vfill\section{Executive Summary}\input[subsection][A parameter]{example01}\section{Background}\end{document}
Package: nlcodec
NLCodec📕 Docs:https://isi-nlp.github.io/nlcodecA set of (low level) Natural Language Encoder-Decoders (codecs), that are useful in preprocessing stage of
NLP pipeline. These codecs include encoding of sequences into one of the following:CharacterWordBPE based subwordClassIt provides python (so embed into your app) and CLI APIs (use it as stand alone tool).There are many BPE implementations available already, but this one provides differs:Pure python implementation that is easy to modify anything to try new ideas.
(other implementations require c++/rust expertise to modify the core)An easily shareable and inspectable model file. It is a simple text that can be inspected withlessorcut. It includes info on which pieces were put together and what frequencies etc.Reasonably faster than the other pure python implementations. Under the hood  tries, doubly linked lists, max-heaps, hash maps etc data-structures to boost performance.PySpark backend for extracting term frequencies from large datasets.InstallationPlease run only one of these# Install from pypi (preferred)$pipinstallnlcodec--ignore-installed# Clone repo for development modegitclonehttps://github.com/isi-nlp/nlcodeccdnlcodec
pipinstall--editable.pip installer registers these CLI tools in your PATH:nlcodec-- CLI  for learn, encode, decode. Same aspython -m nlcodecnlcodec-learn-- CLI  for learn BPE, with PySpark backend. Same aspython -m nlcodec.learnnlcodec-db-- CLI for bitextdb.python -m nlcodec.bitextdbnlcodec-freq-- CLI for extracting word and char frequencies using spark backend.Docs are available atHTML format:https://isi-nlp.github.io/nlcodec(recommended)Locally atdocs/intro.adocCitationRefer tohttps://arxiv.org/abs/2104.00290To-appear: ACL 2021 Demos@article{DBLP:journals/corr/abs-2104-00290,author={Thamme Gowda andZhao Zhang andChris A. Mattmann andJonathan May},title={Many-to-English Machine Translation Tools, Data, and Pretrained Models},journal={CoRR},volume={abs/2104.00290},year={2021},url={https://arxiv.org/abs/2104.00290},archivePrefix={arXiv},eprint={2104.00290},timestamp={Mon, 12 Apr 2021 16:14:56 +0200},biburl={https://dblp.org/rec/journals/corr/abs-2104-00290.bib},bibsource={dblp computer science bibliography, https://dblp.org}}AuthorsThamme Gowda
Package: nlcpy
NLCPy : NumPy-like API accelerated with SX-Aurora TSUBASANLCPyis a library for accelerating performance of Python scripts usingNumPyon SX-Aurora TSUBASA. Python programmers can use this library on Linux/x86 of SX-Aurora TSUBASA. NLCPy's API is designed based on NumPy's one. The current version provides a subset of NumPy's API.RequirementsBefore the installation, the following components are required to be installed on your x86 Node of SX-Aurora TSUBASA.NEC SDKrequired NEC C/C++ compiler version: >= 5.0.0required NLC version: >= 3.0.0Alternative VE Offloading (AVEO)required version: >= 3.0.2If you install NLCPy from wheel, the runtime packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo veoffload-aveorun-ve1 veoffload-aveorun-ve3If you install NLCPy from source, the development packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develveosinfo3# yum install veosinfo3Python- required version: 3.6, 3.7, or 3.8NumPy- required version: >= v1.17Install from wheelYou can install NLCPy by executing either of following commands.Install from PyPI$ pip install nlcpyInstall from your local computerDownloadthe wheel packagefrom GitHub.Put the wheel package to your any directory.Install the local wheel package via pip command.$ pip install <path_to_wheel>The shared objects for Vector Engine, which are included in the wheel package, are tested by using NEC C/C++ Compiler Version 5.0.0 and NumPy v1.19.5.Install from source (with building)Before building source files, please install following packages.$ pip install numpy cython wheel
$ sudo yum install veosinfo3-devel
$ sudo yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develAnd, entering these commands in the environment wheregccandncccommands are available.$ git clone https://github.com/SX-Aurora/nlcpy.git
$ cd nlcpy
$ python3 setup.py build_ext --targ ve1,ve3,vh
$ python3 setup.py intall --targ ve1,ve3,vhDocumentationNLCPy User's GuideLicenseThe BSD-3-Clause license (seeLICENSEfile).NLCPy is derived from NumPy, CuPy, PyVEO, and numpydoc (seeLICENSE_DETAIL/LICENSE_DETAILfile).
Package: nlcpy-ve1-kernel
NLCPy : NumPy-like API accelerated with SX-Aurora TSUBASANLCPyis a library for accelerating performance of Python scripts usingNumPyon SX-Aurora TSUBASA. Python programmers can use this library on Linux/x86 of SX-Aurora TSUBASA. NLCPy's API is designed based on NumPy's one. The current version provides a subset of NumPy's API.RequirementsBefore the installation, the following components are required to be installed on your x86 Node of SX-Aurora TSUBASA.NEC SDKrequired NEC C/C++ compiler version: >= 5.0.0required NLC version: >= 3.0.0Alternative VE Offloading (AVEO)required version: >= 3.0.2If you install NLCPy from wheel, the runtime packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo veoffload-aveorun-ve1 veoffload-aveorun-ve3If you install NLCPy from source, the development packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develveosinfo3# yum install veosinfo3Python- required version: 3.6, 3.7, or 3.8NumPy- required version: >= v1.17Install from wheelYou can install NLCPy by executing either of following commands.Install from PyPI$ pip install nlcpyInstall from your local computerDownloadthe wheel packagefrom GitHub.Put the wheel package to your any directory.Install the local wheel package via pip command.$ pip install <path_to_wheel>The shared objects for Vector Engine, which are included in the wheel package, are tested by using NEC C/C++ Compiler Version 5.0.0 and NumPy v1.19.5.Install from source (with building)Before building source files, please install following packages.$ pip install numpy cython wheel
$ sudo yum install veosinfo3-devel
$ sudo yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develAnd, entering these commands in the environment wheregccandncccommands are available.$ git clone https://github.com/SX-Aurora/nlcpy.git
$ cd nlcpy
$ python3 setup.py build_ext --targ ve1,ve3,vh
$ python3 setup.py intall --targ ve1,ve3,vhDocumentationNLCPy User's GuideLicenseThe BSD-3-Clause license (seeLICENSEfile).NLCPy is derived from NumPy, CuPy, PyVEO, and numpydoc (seeLICENSE_DETAIL/LICENSE_DETAILfile).
Package: nlcpy-ve3-kernel
NLCPy : NumPy-like API accelerated with SX-Aurora TSUBASANLCPyis a library for accelerating performance of Python scripts usingNumPyon SX-Aurora TSUBASA. Python programmers can use this library on Linux/x86 of SX-Aurora TSUBASA. NLCPy's API is designed based on NumPy's one. The current version provides a subset of NumPy's API.RequirementsBefore the installation, the following components are required to be installed on your x86 Node of SX-Aurora TSUBASA.NEC SDKrequired NEC C/C++ compiler version: >= 5.0.0required NLC version: >= 3.0.0Alternative VE Offloading (AVEO)required version: >= 3.0.2If you install NLCPy from wheel, the runtime packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo veoffload-aveorun-ve1 veoffload-aveorun-ve3If you install NLCPy from source, the development packages of Alternative VE Offloading(AVEO) are required.# yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develveosinfo3# yum install veosinfo3Python- required version: 3.6, 3.7, or 3.8NumPy- required version: >= v1.17Install from wheelYou can install NLCPy by executing either of following commands.Install from PyPI$ pip install nlcpyInstall from your local computerDownloadthe wheel packagefrom GitHub.Put the wheel package to your any directory.Install the local wheel package via pip command.$ pip install <path_to_wheel>The shared objects for Vector Engine, which are included in the wheel package, are tested by using NEC C/C++ Compiler Version 5.0.0 and NumPy v1.19.5.Install from source (with building)Before building source files, please install following packages.$ pip install numpy cython wheel
$ sudo yum install veosinfo3-devel
$ sudo yum install veoffload-aveo-devel veoffload-aveorun-ve1-devel veoffload-aveorun-ve3-develAnd, entering these commands in the environment wheregccandncccommands are available.$ git clone https://github.com/SX-Aurora/nlcpy.git
$ cd nlcpy
$ python3 setup.py build_ext --targ ve1,ve3,vh
$ python3 setup.py intall --targ ve1,ve3,vhDocumentationNLCPy User's GuideLicenseThe BSD-3-Clause license (seeLICENSEfile).NLCPy is derived from NumPy, CuPy, PyVEO, and numpydoc (seeLICENSE_DETAIL/LICENSE_DETAILfile).
Package: nldb
nldbNatural Language Databasing. Talk to your data in English, via CLI, API or a simple web interface.InstallationpipinstallnldbAn OpenAI API key should be available as theOPENAI_API_KEYenvironment variable, e.g. usingexport OPENAI_API_KEY=sk-etcor adding it to your project.envfile.Preparing your databaseNLDB can talk to SQLite and DuckDB databases. Call yoursnldb.dbor specify the name with aDATABASEenvironment variable.You may need to simplify and denormalise your database for NLDB to work well. Your columns should have names whose meaning is obvious. While NLDB can join across tables, combining your data into a single table will reduce your prompt size and improve the accuracy of NLDB's query generation.Initialise NLDBnldbinitThis creates three files:index.html,Dockerfileandprompt.txt. You can ignore the first two for now.Edit your promptEditprompt.txt, following the example and instructions in the generated file. You can test the effectiveness of your prompt by pasting it into ChatGPT, replacing%swith some example questions.Ask questions from the command linenldb"What were the most watched videos by Japanese users in 2022?"Start the API servernldbserveThis starts an API server, on port 8080, with a single endpoint at/api/ask. This expects a GET request with aqparameter for the natural language query. It returns a JSON object containing the SQL statement, the result of executing the statement, a plain English answer and some timing and cost information.There is a simple web interface to the API at/. If you don't want this, just deleteindex.html.AdjustEditprompt.txtto refine the quality of the generated queries, restarting the server to see changes. Editindex.htmlor replace it with your own front-end.DeployGenerate instructions for Fly.io and Google Cloud Runnldbdeploy
Package: nldcsc
NLDCSC packageThis package contains generic re-usable code.Install the full package:pip install nldcsc[all]Package has several modules which can be installed separately by specifying them
as an extra requirement. To install the loggers module only, specify:pip install nldcsc[loggers]Or for multiple modules:pip install nldcsc[loggers, flask_managers]ModulesThe following modules are available in the nldcsc package:authdatatablesflask_managersflask_middlewareflask_pluginshttp_apisloggerssql_migrationsLoggersThere are two loggers provided:AppLogger (nldcsc.loggers.app_logger.AppLogger)GunicornLogger (nldcsc.loggers.app_logger.GunicornLogger)The AppLogger is intended to be used as a loggerClass to be used for the
standard python logging module.importloggingfromnldcsc.loggers.app_loggerimportAppLoggerlogging.setLoggerClass(AppLogger)mylogger=logging.getLogger(__name__)The 'mylogger' instance has all the proper formatting and handlers
(according to the desired config) to log messages.The Gunicorn logger is intended to be used for as a loggerClass for the
gunicorn webserver; it enables the FlaskAppManager to set the necessary
formatting and handles according to the AppLogger specs and a custom format
for the gunicorn access logging.Flask app managerThe FlaskAppManager is intended to be used to 'run' flask applications in
both test, development as in production environments.fromYADAimportappfromnldcsc.flask_managers.flask_app_managerimportFlaskAppManagerfam=FlaskAppManager(version="1.0",app=app)fam.run()Depending on the configuration the FlaskAppManager uses a werkzeug (DEBUG == True)
or a gunicorn webserver. TLS could be set for both webservers iaw the module specific
README.md.HTTP apisBaseclass for http api communication is present under
nldcsc.http_apis.base_class.api_base_class.ApiBaseClassSQL MigrationsThe sql migrations can be used to facilitate migration between different
versions of sql models / versions. It relies on flask migrate to perform
the different migrations. It has a CLI as well as an python class based API.Check the command line helppython3 -m nldcsc.sql_migrations.flask_sql_migrate -a /path/to/script_with_flask_app.py -i
python3 -m nldcsc.sql_migrations.flask_sql_migrate -a /path/to/script_with_flask_app.py -m
python3 -m nldcsc.sql_migrations.flask_sql_migrate -a /path/to/script_with_flask_app.py -uOr initiate the FlaskSqlMigrate as a class and initiate the migration
process from there:fromnldcsc.sql_migrations.flask_sql_migrateimportFlaskSqlMigratefsm=FlaskSqlMigrate(app_ref="/path/to/script_with_flask_app.py")fsm.db_init()fsm.db_migrate()fsm.db_update()
Package: nldesa
NLDESA - Nonlinear Differential Equation Stability AnalysisThis is a Python package for the stability analysis of nonlinear differential equations using JAX [1] and pyDMD [2] [3].
The package is still in development and is not yet ready for use.Authors: João Biu (joaopedrofbiu@tecnico.ulisboa.pt), Miguel Lameiras (miguel.lameiras@tecnico.ulisboa.pt)[1]https://github.com/google/jax[2] Demo et al., (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530,https://doi.org/10.21105/joss.00530(https://github.com/PyDMD/PyDMD.git)InstallationUsing pip:pip install nldesaFor developersClone the repositorycd NLDESA && pip install -e .