Saravanan666/pypi-org huggingface dataset:
Package: nl-service-metadata-generator
nl-service-metadata-generatorCLI applicatie om service metadata records te genereren die voldoen aan hetNederlands profiel op ISO 19119 voor services versie 2.1.0.CLI applicatie genereert metadata en voert schema validatie uit. Applicatie voertgeenschematron validatie uit (validatie opNederlands profiel op ISO 19119 voor services versie 2.1.0).Indien schema validatie faalt op de gegenereerde metadata wordt het metadata bestand weggeschreven naar${file-destination}.invalid(dus toevoeging van.invalidextensie) en zal de nl-service-metadata-generator de schema validatie foutmelding naar stdout printen en een returncode van1teruggeven.Service TypesDe nl-service-metadata-generator ondersteunt de volgende service types:geen INSPIRE serviceINSPIRE network serviceINSPIRE other serviceSpatial Data Service (SDS) - invocableSDS - interoperableN.B. SDS harmonized wordt dus niet ondersteund door de nl-service-metadata-generatorInstallationInstalleernl-service-metadata-generatorals pip package (uitvoeren vanuit root van repository):pip3 install .Nu moet het cli commandnl-service-metadata-generatorbeschikbaar zijn inPATH.UsageUsage:nl-service-metadata-generatorgenerate[OPTIONS]{csw|wms|wmts|wfs|wcs|sos|atom|tms|oaf|oat}{network|other|none}CONSTANTS_CONFIG_FILESERVICE_CONFIG_FILEOUTPUT_FILEGenerateservicemetadatarecordbasedon**NederlandsprofielopISO19119voorservicesversie2.1.0**.CONSTANTS_CONFIG_FILE:JSONfilethatcontainsvaluesforconstantfieldsSERVICE_CONFIG_FILE:JSONfilethatcontainsvaluesforfieldsthatareuniqueforeachserviceSee`show-schema`commandforhelponconfigfiles.

Options:--csw-endpointTEXTReferencestodatasetmetadatarecordswillusethisCSWendpoint(defaultval:https://nationaalgeoregister.nl/geonetwork/srv/dut/csw)--sds-type[invocable|interoperable]onlyapplieswheninspire-type='other'--helpShowthismessageandexit.Bijvoorbeeld (uitvoeren in root directory van dit repository):nl-service-metadata-generatorgenerateatomnetworkexample_json/constants.jsonexample_json/inspire.jsonatom.xmlJSON schema voor deCONSTANTS_CONFIG_FILEenMETADATA_CONFIG_FILEkunnen worden opgevraagd middels hetshow-schemacommand, zienl-service-metadata-generator show-schema --helpvoor help.DevelopmentVoor het formatteren van code installeerblacken draai vanuit de root van het repo:black.Verwijderen van ongebruikte imports metautoflake:autoflake--remove-all-unused-imports-i-r.Organiseren en orderen imports metisort:isort-m3.DockerBouw docker image met:dockerbuild.-tnl-service-metadata-generatorDan container starten met (n.b.-u rootargument, is nodig voor priviliges Docker container om bestanden weg te schrijven in folder mount - niet op deze manier gebruiken voor productie doeleinden):dockerrun--userroot-v$(pwd)/example_json:/datanl-service-metadata-generatorgenerateatomnetwork/data/constants.json/data/inspire.json/data/atom.xml
Package: nlsic
NLSIC stands for “Non-linear Least Squares with Inequality Constraints”. This algorithm was first published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>. His advantage is in combination of the following features:Jacobi matrixJis not squared, i.e.J^tJis not calculated, thus preserving the solution from condition number degradation. Instead, QR decomposition is used;taking into account inequality constraints is based on a well known NNLS algorithm by Lawson and Hanson;convergence in non-linear iterations is globalized by back-tracking method. I.e. with enough iterations, the process will reach a convergence point but no guaranty is given that it will be a global minimum;ifJis rank deficient, a least norm (also known as “minimum norm”) increment is used. To find such increment exactly (not just a regularized one) in presence of inequality constraints is not a trivial task. We use our own algorithm implemented inlsi_ln(...)function;if an initial approximation for a solution vector is outside of feasibility domain (FD) defined by inequality constraints then it is first projected on the FD (cf.ldp(...)) and the non-linear iterations start afterward.This python implementation is an adaptation of the R one which is distributed withinflux_sisoftware. The original R version is in advance on this one. So users desiring to catch with the latest developments are invited to switch to the R version.The main function to call isnlsic(...).
Package: nlterm
nlterm: Integrate ChatGPT in your terminalInstallpip install nltermExampleExample 1> nlterm
Instruction: reorgnize this folder to like a pypi project. That is I need to put scripts in `src` folder, and have a basic `pyproject.toml`This is the output:```
mkdir src
mv *.py src
touch pyproject.toml
```
Execute? (y/n):Example 2> nlterm
Instruction: Install the whl fileThis is the output:```
pip install nlterm-0.0.2-py3-none-any.whl
```
Execute? (y/n):
Package: nltkjsonnlp
NLTK-JSON-NLP(C) 2019 byDamir Cavar,Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili, Boli FangBrought to you by theNLP-Lab.org!IntroductionNLTKwrapper toJSON-NLP.NLTKhas a wide variety of capabilities, but for our purposes
we are limiting it toWordNet,VerbNet, andFrameNet. Other packages such asspaCyandFlairare more accurately able to annotate things like part of speech tags and dependency
parses. See below for instruction on how to unify outputs from multiple packages.MicroserviceTheJSON-NLPrepository provides a Microservice class, with a pre-built implementation of [Flask]. To run it, execute:python nltkjsonnlp/server.pySinceserver.pyextends the [Flask] app, a WSGI file would contain:from nltkjsonnlp.server import app as applicationPipelineJSON-NLPprovides a simplePipelineinterface that we implement asNltkPipeline:pipeline = nltkjsonnlp.NltkPipeline()
print(pipeline.process(text='I am a sentence.'))UnificationTo make the best use of this pipeline, it is best to unify it with a more accurate and complete
pipeline such asspaCy-NLP-Json:class UnifiedPipeline(pyjsonnlp.pipeline.Pipeline):
    def __init__(self):
        super(UnifiedPipeline, self).__init__()
        self.spacy = spacynlpjson.SpacyPipeline()
        self.nltk = nltkjsonnlp.NltkPipeline()

    def process(self, text='', coreferences=True, constituents=False, dependencies=True, expressions=True,
                **kwargs) -> OrderedDict:
        # start with a spacy parse
        spacy_json = self.spacy.process(text, spacy_model='en_core_web_md', constituents=False,
                                        coreferences=coreferences, dependencies=dependencies, expressions=False)
        # the get an nltk parse
        nltk_json = self.nltk.process(text)

        # unify the parses
        return pyjsonnlp.unification.unifier.add_annotation_to_a_from_b(a=spacy_json, 
                                                                        b=nltk_json, annotation='tokens')
Package: nltk_tgrep
TGrep search implementation for NTLK trees.Copyright (c) 16 March, 2013 Will Roberts <wildwilhelm@gmail.com>.Licensed under the MIT License (see source file tgrep.py for details).This module supports TGrep2 syntax for matching parts of NLTK Trees.
Note that many tgrep operators implemented here require the tree
passed to be aParentedTree.Tgrep tutorial:http://www.stanford.edu/dept/linguistics/corpora/cas-tut-tgrep.htmlTgrep2 manual:http://tedlab.mit.edu/~dr/Tgrep2/tgrep2.pdfTgrep2 source:http://tedlab.mit.edu/~dr/Tgrep2/Requirements:Python 2.6 or better, or Python 3.2 or betterfuture(for Python 2)NLTK, version 3.0.0 or betterpyparsing$ sudo pip install nltk_tgrepUsage:>>> from nltk.tree import ParentedTree
>>> import nltk_tgrep
>>> tree = ParentedTree.fromstring('(S (NP (DT the) (JJ big) (NN dog)) (VP bit) (NP (DT a) (NN cat)))')
>>> nltk_tgrep.tgrep_nodes(tree, 'NN')
[ParentedTree('NN', ['dog']), ParentedTree('NN', ['cat'])]
>>> nltk_tgrep.tgrep_positions(tree, 'NN')
[(0, 2), (2, 1)]
>>> nltk_tgrep.tgrep_nodes(tree, 'DT')
[ParentedTree('DT', ['the']), ParentedTree('DT', ['a'])]
>>> nltk_tgrep.tgrep_nodes(tree, 'DT $ JJ')
[ParentedTree('DT', ['the'])]This implementation adds syntax to select nodes based on their NLTK
tree position.  This syntax isNplus a Python tuple representing
the tree position.  For instance,N(),N(0,),N(0,0)are
valid node selectors.  Example:>>> tree = ParentedTree.fromstring('(S (NP (DT the) (JJ big) (NN dog)) (VP bit) (NP (DT a) (NN cat)))')
>>> tree[0,0]
ParentedTree('DT', ['the'])
>>> tree[0,0].treeposition()
(0, 0)
>>> nltk_tgrep.tgrep_nodes(tree, 'N(0,0)')
[ParentedTree('DT', ['the'])]Caveats:Link modifiers: “?” and “=” are not implemented.Tgrep compatibility: Using “@” for “!”, “{” for “<”, “}” for “>” are
not implemented.The “=” and “~” links are not implemented.Known Issues:There are some issues with link relations involving leaf nodes
(which are represented as bare strings in NLTK trees).  For
instance, consider the tree:(S (A x))The search string* !>> Sshould select all nodes which are not
dominated in some way by anSnode (i.e., all nodes which are
not descendants of anS).  Clearly, in this tree, the only node
which fulfills this criterion is the top node (since it is not
dominated by anything).  However, the code here will find both the
top node and the leaf nodex.  This is because we cannot recover
the parent of the leaf, since it is stored as a bare string.A possible workaround, when performing this kind of search, would be
to filter out all leaf nodes.
Package: nltk-theme
NLTK ThemeNLTK Theme is aSphinxtheme forked from the wonderfulInsegel, designed for theNLTKdocumentation. Feel free to have a look at thetheme documentationor look at the theme in action over atwww.nltk.org.InstallationThis theme is not necessarily intended for reuse. I would advise looking atInsegelif you'd like to use a similar theme.That said, you may install it via pip:pip install nltk_themeThen, you can tell Sphinx to use the theme by adding:html_theme = 'nltk_theme'to your project'sconf.py.
Package: nltokeniz
# nltokeniz.py[![PyPI version](https://badge.fury.io/py/nltokeniz.svg)](https://badge.fury.io/py/nltokeniz)
[![Python versions](https://img.shields.io/pypi/pyversions/nltokeniz.svg)](setup.py)
[![Build Status](https://travis-ci.org/raviqqe/nltokeniz.py.svg?branch=master)](https://travis-ci.org/raviqqe/nltokeniz.py)
[![License](https://img.shields.io/badge/license-unlicense-lightgray.svg)](https://unlicense.org)Natural language tokenizer for English and Japanese documents in Python## License[The Unlicense](https://unlicense.org)
Package: nltp
ï»¿## Natural Language Text Preprocessor (nltp)A simplified package for automating text preprocessing activities such as lemmatization, tokenization, removal of stop words, removal of certain pattern from text using regular expression. Working under the hood, this package makes use of the NLTK library for its text cleaning activities.InstallationRequirements:Python 3.7 or higherNLTKInstall latest release:pip install nltpInstall from source:git clone https://github.com/izzyx6/nltp.git
cd nltp
pip install .Usage: the basicsHere's how to perform text cleaning with nltpFirst, we pass text in a list to the instantiated Preprocessor object as it takes an argument text.This lines of code returns a tokenized version of the text passed on instantiating the text Preprocessor .fromnltpimportPreprocessortext=["I like eat delicious food","That's I'm cooking food myself, case '10 Best Foods' helps lot, also 'Best Before (Shelf Life)'"]output=Preprocessor(text)output.token()You can retrive the text with their index (default set to 0):output.token(1)Next, you can get the cleaned version of the text passed in a list with lemmatization, stop word and unwanted patterns in text removed.Available parameters to modify arestop_wordsandpatterns.output=Preprocessor(text,stop_words=[USERDEFINED],pattern=[USERDEFINED])Note:the purpose of having these parameters are to by pass the defualt parameters that remove non alphabets, repeted sequence of words, and users name (identified with the @User).output=Preprocessor(text)output.text_cleaner()Note:Using the output. you can get the default stop word, patterns, and text passedoutput=Preprocessor(text)output.patternsoutput.stop_wordsoutput.textCitationBibTex entry:@misc{omalley2019kerastuner,title={Natural Language Text Preprocessor {nltp}},author={ Ufumaka Isreal},year=2020,howpublished={\url{https://github.com/izzyx6/nltp}}}
Package: nluanalizer
IntroductionThis package will help the user to extract ngrams, nouns, verbs,
adverbs and more features from an Intent Dataset in Spanish. This
will help the user to balance the dataset by comparing number of
ocurrences of each feature in each intent.The entry format is an excel (.xlsx). Each column would be an intent
and in the first row it has to be informed the intent name, then
in the next rows the sentences for each intent training.The output format is a json. It will have one dictionary per intent
including all the features extracted.How to use it?Installationpip install nluanalyzerUsageUsagenluanalyzer -h|--help
nluanalyzer -v|--version
nluanalyzer <excel_path>Options-h --help                    Show help screen.
-v --version                 Show version.Example$ nlu_analyzer  intents.xlsx
'Successfully saved intents analyzed file in /home/project/location/Formatted_Metrics.xlsx'Package Requirementsnltk==3.4.5spacy==2.2.4docopt==0.6.2numpy==1.17.4docopt==0.6.2pandas==0.25.3textacy==0.10.0scikit_learn==0.23.1History1.0.0 (2020-05-26)Spanish Analysis Implemented.
Package: nlu-inference
🪐 项目: nlu模型推理框架基于entry point的nlu多后端模型推理框架📋 project.ymlproject.yml定义了项目所有的命令以及由命令组成的流程.⏯ 命令以下是项目中的命令. 它们都可以通过project run [name]来运行.命令描述publish发布到普强源📦 安装pipinstallnlu-inference--upgradepipinstallnlu-inference--upgrade-ihttp://192.168.130.5:5002/simple/--trusted-host192.168.130.5--extra-index-urlhttps://mirrors.aliyun.com/pypi/simple🚀 使用nlu-inference--help
Package: nlup
nlupcontains some base libraries I use in natural language processing projects. Some highlights:confusion.py: classifier evaluation objectsdecorators.py: clever decorators for various purposesjsonable.py: a mix-in which allows the state of most objects to be serialized to (and deserialized from) compressed JSONperceptron.py: perceptron-like classifiers (binary and multiclass), including some forms of structured predictionreader.py: classes and readers for tagged and dependency-parsed datatimer.py: awith-block that logs wall clock time elapsedAll have been tested on CPython 3.4.1 and PyPy 3.2.5 (PyPy version 2.3.1). They will not work on Python 2 without modification.Some projects usingnlup:Detector Morse: simple sentence boundary detectionPerceptronix Point Never: simple part of speech taggingWhere's Yr Head At: simple transition-based dependency parsing
Package: nlx-cli
InstallationInstall via pippipinstallnlx-cliConfigurationYou can configure cli settings with a .env file and/or environment variables.
The only configuration you will need to modify out of the box isNLX_API_KEY.
To list the current active configuration, you can use theconfigcommand:nlxconfig|jq{"NLX_ENV_PATH":".env","NLX_SUPPRESS_ENV_NOTICE":true,"NLX_LOG_LEVEL":"INFO","NLX_API_KEY":"<redacted>","NLX_API_URL":"https://api.nlxresearchhub.org","NLX_REPORT_HISTORY_STORAGE":"nlx.pickle","NLX_REPORT_DOWNLOAD_DIR":".reports"}UsageYou can use any access methods defined directly by the CLI or you can define a custom
runner module. For details on CLI methods, you can runnlx --help.Custom Runner ModuleCustom runner modules allow you to specify a client class and a list of operations to perform with
that client class. Custom runner modules must define the following:RUNNER_CLIENTpython module style import path to your Client class definitionRUNNER_OPSlist of operations which will be performed by your clientRUNNER_OP_ERROR_HANDLERfunction which wraps each operation and handles thrown errorsBelow is an example of a custom runner module (the current revision of which can be found in./examples/example_run_config.py).
To run this example, copy the contents of the runner into aexample_run_config.pyin you current working directory
and execute the commandnlx run example_run_config. To preview the operations that this will run, you can usenlx show_ops example_run_config"""This runner will create, await, and download async reports for all job listingscompiled in the years 2017-2022 for Kansas.You want to place this file in your current working directory or a location thatis importable from your current python path."""importloggingfromnlx.helpersimporthelpersfromnlx.utils.miscimportbasic_loggerlogger=basic_logger(__name__,logging.DEBUG)# python module style import path of the Client class to be executed by the runner.RUNNER_CLIENT="nlx.client.AsyncReport"# years 2017-2022, inclusiveYEARS=[*range(2017,2023)]# KansasSTATES=["KS"]RUNNER_OPS=[]forstateinSTATES:foryearinYEARS:# generate twelve months of the arguments for start, end, state, autoforgenerated_kwargsinhelpers.generate_year_kwargs(year,state=state,auto=True):# indicate that the kwargs will be passed to RUNNER_CLIENT.create, e.g.# nlx.client.AsyncReport().create(**generated_kwargs)RUNNER_OPS.append(("create",generated_kwargs))deferror_handler(func,*args,**kwargs):try:returnfunc(*args,**kwargs)exceptKeyboardInterrupt:raiseexcept:# noqalogger.exception(f"Something unexpected happened when executing func={func}args={args}, kwargs={kwargs}")RUNNER_OP_ERROR_HANDLER=error_handlerProgrammatic UsageIf custom runner modules don't provide enough flexibility for your use-case, you can always import anything
available in the nlx-cli package for use as you see fit.fromnlx.clientimportAsyncReportassertAsyncReport().is_authorizedBuild & Publish# asks for credentialspoetrypublish--build# ORpipinstalltwine
rm-rf./dist/
poetrybuild# uses ~/.pypirctwineupload-rtestpypidist/*
twineupload-rpypidist/*
Package: nlx-url-rewriter
1Welcome to nlx-url-rewriter’s documentation!Version:0.1.0Source:https://github.com/maykinmedia/nlx-url-rewriterKeywords:<keywords>PythonVersion:3.7Plumbing to rewrite URLs in forward and reverse directionContents1Welcome to nlx-url-rewriter’s documentation!2Features3Installation3.1Requirements3.2Install4Usage2FeaturesManage rewrites in Django adminRewriter that can be used in a variety of places3Installation3.1RequirementsPython 3.6 or abovesetuptools 30.3.0 or aboveDjango 2.2 or above3.2Installpipinstallnlx-url-rewriter4UsageTODO
Package: nlzss
Python bindings for the Nintendo GBA/DS LZSS compression algorithm.LZSS algorithm and C code from CUE’s tools:http://www.romhacking.net/utilities/826/Installationpip install nlzssEncode>> import nzlss
>> nzlss.encode_file('in_file.txt', 'out_file.txt')Decode>> import nzlss
>> nzlss.decode_file(in_path='in_file.txt', out_path='out_file.txt')That’s it!
Package: nlzss11
nlzss11Library for data compression using Nintendo's variant of the lzss algorithmReference implementation:nlzssUsagenlzss11can be installed withpip3 install nlzss11.nlzss11.get_uncompressed_size(data)Returns the size in bytes of the file if uncompressed or None if the bytes are not a valid Nintendo LZSS 11 compressed filenlzss11.decompress(data)Decompresses nlzss11-compressed data from a bytes-like objectdata. Returns a bytes object containing the uncompressed data.nlzss11.decompress_unsafe(data)Decompresses nlzss11-compressed data from a bytes (notbytes-like) objectdata. Returns a bytes object containing the uncompressed data.Unlike nlzss11.decompress, this function assumes that the input data is well-formed. In exchange for slightly improved performance, no sanity checks are performed.Warning: Do not use on untrusted data.nlzss11.compress(data, level=7)Compresses a bytes-like objectdata. Returns a bytes-like object containing the compressed data.levelis the compression level (6-9). 6 is fastest and 9 is slowest. Higher compression levels result in better compression. 7 is a good compromise between compression ratio and performance.Project informationThis project is a fork from the amazingsyaz0 Project, just modified to match the algorithm found in The Legend of Zelda: Skyward Sword.Building from sourceBuildingnlzss11from source requires:CMake 3.10+A compiler that supports C++17Everything needed to buildzlib-ngpybind11 2.4+ (including CMake config files)setuptoolsWhen no binary build is available, pip will automatically build from source during the install process.To build and install from source runpip3 install ..Building for manylinuxUse the manylinux docker container in this directory:docker run -v $PWD:/home/build/nlzss11 -ti quay.io/pypa/manylinux2010_x86_64 bashRunbash build-on-manylinux.shrunsetup.py bdist_wheelwith every cpython version you want from/opt/pythonChangelog1.3decompress support for files > 0xFFFFFF (found in SSHD)LicenseThis software is licensed under the terms of the GNU General Public License, version 2 or later.
Package: nma
NotifyMeAfterAboutNotifyMeAfter (nma) is a python package that sends a desktop notification whenever a shell process or decorated function finishes execution. This repository provides a python shell script for general use and a decorator for use within python code.Requirements:Linux or MacOSIf running Linux,send-notifyalso needs to be installedGoogle it for your distro. It's probably already installed, unless you're running something like arch with no desktop enviroment.If running MacOS,terminal-notifieralso needs to be installedbrew install terminal-notifierpython 3.6+Installationenter thenmadirectory and runpython3 -m pip install .ExamplesIn shell scriptsnma "echo test"nma "sudo pacman -Syu"nma "sleep 1; echo test"sleep 1; echo test | nma -my_func(){sleep.1;}my_func|nma-Decoratorfromnma.notify_decoratorimportnotify_after@notify_afterdeftest():print("basic test")Unimplemented FeaturesCall script via piping (ex:sleep 1 | nma -)Email notificationsText notifications
Package: nmail
# nmail### SMTP mail sender.With this package you will able to send mails in 1 line.There is two operations mode:### __Config mode__:Use `config.yml` to specify next variables:smtp_server: '10.0.0.1'smtp_port: 587login: 'username@corp.com'password: 'password'Send email with specifying `config.yml` file:import nmailnmail.send_mail(to=['mybro@example.com','someone@example.com'],cc=['copy@example.com'],bcc=['hidden_copy@example.com'],subject='No subject',text='No Text',attachments=['/tmp/test.txt'],send_as='delegated_mailbox@example.com',config='/tmp/config.yml')> If you will not specify __config__ variable - by default nmail will pick config.yml __from package folder__### __Inline mode__:Send Email inline:import nmailnmail.send_mail(to=['mybro@example.com','someone@example.com'],cc=['copy@example.com'],bcc=['hidden_copy@example.com'],subject='No subject',text='No Text',attachments=['/tmp/test.txt'],send_as='delegated_mailbox@example.com',smtp_server='10.0.0.1'smtp_port=587login='username@corp.com'password='password')> Also, you may override some of parameters by __specifying__ it in the `send_mail` function call.Other params will be gathered from __config file__