Saravanan666/pypi-org huggingface dataset:
Package: nfnets-pytorch
PyTorch implementation of Normalizer-Free Networks and Adaptive Gradient ClippingPaper:https://arxiv.org/abs/2102.06171.pdfOriginal code:https://github.com/deepmind/deepmind-research/tree/master/nfnetsBlog post:https://tourdeml.github.io/blog/posts/2021-03-31-adaptive-gradient-clipping/. Feel free to subscribe to the newsletter, and leave a comment if you have anything to add/suggest publicly.Do star this repository if it helps your work, anddon't forget to citeif you use this code in your research!InstallationInstall from PyPi:pip3 install nfnets-pytorchor install the latest code using:pip3 install git+https://github.com/vballoli/nfnets-pytorchUsageWSConv2dUseWSConv1d, WSConv2d, ScaledStdConv2d(timm)andWSConvTranspose2dlike any othertorch.nn.Conv2dortorch.nn.ConvTranspose2dmodules.importtorchfromtorchimportnnfromnfnetsimportWSConv2d,WSConvTranspose2d,ScaledStdConv2dconv=nn.Conv2d(3,6,3)w_conv=WSConv2d(3,6,3)conv_t=nn.ConvTranspose2d(3,6,3)w_conv_t=WSConvTranspose2d(3,6,3)Generic AGC (recommended)importtorchfromtorchimportnn,optimfromtorchvision.modelsimportresnet18fromnfnetsimportWSConv2dfromnfnets.agcimportAGC# Needs testingconv=nn.Conv2d(3,6,3)w_conv=WSConv2d(3,6,3)optim=optim.SGD(conv.parameters(),1e-3)optim_agc=AGC(conv.parameters(),optim)# Needs testing# Ignore fc of a model while applying AGC.model=resnet18()optim=torch.optim.SGD(model.parameters(),1e-3)optim=AGC(model.parameters(),optim,model=model,ignore_agc=['fc'])SGD - Adaptive Gradient ClippingSimilarly, useSGD_AGCliketorch.optim.SGD# The generic AGC is preferable since the paper recommends not applying AGC to the last fc layer.importtorchfromtorchimportnn,optimfromnfnetsimportWSConv2d,SGD_AGCconv=nn.Conv2d(3,6,3)w_conv=WSConv2d(3,6,3)optim=optim.SGD(conv.parameters(),1e-3)optim_agc=SGD_AGC(conv.parameters(),1e-3)Using it within any PyTorch modelreplace_convreplaces the convolution in your model with the convolution class and replaces the batchnorm with identity. While the identity is not ideal, it shouldn't cause a major difference in the latency.importtorchfromtorchimportnnfromtorchvision.modelsimportresnet18fromnfnetsimportreplace_conv,WSConv2d,ScaledStdConv2dmodel=resnet18()replace_conv(model,WSConv2d)# This repo's original implementationreplace_conv(model,ScaledStdConv2d)# From timm"""class YourCustomClass(nn.Conv2d):...replace_conv(model, YourCustomClass)"""DocsFind the docs atreadthedocsCite Original WorkTo cite the original paper, use:@article{brock2021high,
  author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
  title={High-Performance Large-Scale Image Recognition Without Normalization},
  journal={arXiv preprint arXiv:},
  year={2021}
}Cite this repositoryTo cite this repository, use:@misc{nfnets2021pytorch,
  author = {Vaibhav Balloli},
  title = {A PyTorch implementation of NFNets and Adaptive Gradient Clipping},
  year = {2021},
  howpublished = {\url{https://github.com/vballoli/nfnets-pytorch}}
}
Package: nfog
nfogScriptable Database-Driven NFO Generator for Movies and TV.Installationpip install --user nfogBuildingDependenciesPython(v3.7 or newer)Poetry(latest recommended)Installationgit clone https://github.com/rlaphoenix/nfogcd nfogpoetry config virtualenvs.in-project true(optional, but recommended)poetry installnfo -hCreating TemplatesWe use Template's to define the structure and logic that creates your NFO file. Your Template file may
create NFOs of any kind of encoding or style, including ASCII, ANSI, and such. You don't have to conform
to any specifications of any kind, but are encouraged to if possible.To create a Template file, you simply need to inherit theTemplateclass innfog.template, fill out
the various abstract methods/properties, and create annfoproperty that returns a final string.Take a look at theExample Templatesfor pre-made examples for various NFO
usage scenarios. You may modify these Templates in any way you like.Note: While you have complete freedom with what Python code you run from within the template, this also
means you should not immediately trust template file as they are after all still Python files.Creating ArtworkJust like Templates, we use Artwork files to define the look and style of the surrounding NFO.
You may also do introspection of the NFO output to merge style within the contents of the NFO as well.To create an Artwork file, inherit theArtworkclass innfog.artwork, fill out any abstract methods
and properties, and create thewith_templatefunction that returns the final string containing both
the NFO output (fromtemplateargument) and the Artwork.Take a look at theExample Artworkto see how these are used. However, you cannot
re-use these, or make derivative works. Please see theArtwork Licensefor more information.Using Templates and ArtworkTo use Templates and Artwork, callingnfo(ornfo generate) will ask you for various information, but
one of them is a Template to use. The Templates it makes available to use are loaded from the user templates
directory which can be found by typingnfo version.To use an Artwork, specify the name of the Artwork file (case-sensitive) to-a/--artwork.
Using an Artwork is completely optional.For more information on usingnfog, see the usage help by callingnfo --help.LicenseApache License, Version 2.0
Package: nfogen
Installation:
git clonehttps://github.com/borisbabic/nfogen.gitsudo pip install nfogen/ #don’t forget the /orsudo pip install nfogen #without the /Usage:
nfogen directoryWhat it does:Walks through subdirectories in directory and creates a tvshow.nfo with the url to tvdb.
The user is prompted for which show to use
Package: nfoursid
NFourSIDImplementation of the N4SID algorithm for subspace identification [1], together with Kalman filtering and state-space
models.State-space models are versatile models for representing multi-dimensional timeseries.
As an example, the ARMAX(p,q,r)-models - AutoRegressive MovingAverage with eXogenous input -
are included in the representation of state-space models.
By extension, ARMA-, AR- and MA-models can be described, too.
The numerical implementations are based on [2].InstallationReleases are made available on PyPi.
The recommended installation method is viapip:pipinstallnfoursidFor a development setup, the requirements are indev-requirements.txt.
Subsequently, this repo can be locallypip-installed.Documentation and code exampleDocumentation is providedhere.
An example Jupyter notebook is providedhere.ReferencesVan Overschee, Peter, and Bart De Moor. "N4SID: Subspace algorithms for the identification of combined
deterministic-stochastic systems." Automatica 30.1 (1994): 75-93.Verhaegen, Michel, and Vincent Verdult.Filtering and system identification: a least squares approach.Cambridge university press, 2007.
Package: nfp
Neural fingerprint (nfp)Keras layers for end-to-end learning on molecular structure. Based on Keras, Tensorflow, and RDKit. Source code used in
the studyMessage-passing neural networks for high-throughput polymer screeningRelated WorkConvolutional Networks on Graphs for Learning Molecular FingerprintsNeural Message Passing for Quantum ChemistryRelational inductive biases, deep learning, and graph networksNeural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials(Main) RequirementsrdkittensorflowGetting startedThis library extends Keras with additional layers for handling molecular structures (i.e., graph-based inputs). There a
strong familiarity with Keras is recommended.An overview of how to build a model is shown inexamples/solubility_test_graph_output.ipynb. Models can optionally
include 3D molecular geometry; a simple example of a network using 3D geometry is found
inexamples/model_3d_coordinates.ipynb.The current state-of-the-art architecture on QM9 (published in [4]) is included inexamples/schnet_edgeupdate.py. This
script requires qm9 preprocessing to be run before the model is evaluated withexamples/preprocess_qm9.py.
Package: nf_parser
nf-parserParse and inspect Nextflow DSL scripts to extract information from Nextflow workflows, processes, channels, and more.FeaturesDSL Parsing:Parse Nextflow DSL scripts to extract structured information.Workflow Analysis:Retrieve details about workflows, processes, channels, and other components.Channel Operators:Parse and understand Nextflow channel operators for further analysis.DAG Generation:Generate a Directed Acyclic Graph (DAG) of the workflow for visualization and analysis.Syntax Validation:Validate Nextflow syntax to catch errors early in the development process.Installationpipinstallnf-parserDependeciespython >=3.9larkUsagefrom nf_parser import NextflowParser

# Example usage
script = """
workflow example {
    input:
    path data

    script:
    """
    cat ${data} > output.txt
    """
}
"""

# Parse the script
parser = NextflowParser()
parsed_data = parser.parse(script)

# Access parsed information
workflow_name = parsed_data["workflows"][0]["name"]
print(f"Workflow Name: {workflow_name}")

# Generate a DAG
dag = NextflowDAG(parsed_data)
dag.generate_dag()
dag.visualize_dag("workflow_dag.png")

# Validate syntax
is_valid_syntax = parser.validate_syntax(script)
print(f"Is Syntax Valid: {is_valid_syntax}")DocumentationDetailed documentation, including available methods and data structures, can be found in thedocumentation.TestingpytestContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Credits
Package: nfractal
Neural FractalA Library for Visual Exploration of Dynamical Systems defined by Neural NetworksHome PageThe dynamical system that generated the above fractal has been explained in the documentation.FeaturesDefine Dynamical Systems Using Complex-Valued Neural NetworksGPU Support for Accelerated Sampling and RenderingPseudo-Coloring UtilitiesBuilt on Top of PyTorchAbout the packageNeural Fractal has been developed for exploring the properties of dynamical systems defined by neural networks and specially complex-valued neural networks. Fractals are visualizations of Chaos. They have infinite self-similar patterns. One way to generate fractals is by applying a function repeatedly on a set of points and keeping the points that do not diverge to infinity. Interestingly, Even dynamical systems constructed by simple functions in this way can generate amazing fractals. But what happens if instead of a simple function we use a neural network? Repeatedly applying a neural network is equivalent to a recurrent neural network which is able to model complicated non-linear dynamical systems. Reservoir computing has demonstrated even completely random RNNs can construct strange and interesting dynamics. This package is an attempt to explore the strange and beautiful world of fractalsInstallationpip install nfractalQuick StartSee theHome PageFeatures Under DevelopmentAutomatic pseudo-coloringGnerating zoom animationsMain ContributorsAmirabbas Asadi, Independet AI and computer science researcher
Package: nfs-creator
wimex-nfsNameWIMEX NFS creator.Test and Deploy❌ : exportfs failed!. exportfs: duplicated export entries:
exportfs:       *:/var/data
exportfs:       *:/var/dataTo fix the above error, edit this file and remove the newly added entries:sudo vi /etc/exportsRemove the lines added at the bottom. Both of the duplicate entries.Tested on Ubuntu and CentOS Stream 8.DescriptionWimex NFS creator (creates an NFS export/share/dir) is a utility package that can be installed via pip. It's used to test the host system of installation of nfs utility tools and check if the services are running. It's responsible to install NFS if it's not already installed and also responsible to create an NFS export.Installationsudo pip3 install nfs-creatorUsagefrom nfs_creator import nfs_creator
nfs_creator() # by default the nfs export would be created at /var/data/

# provide a custom path
nfs_creator('/var/application')Authors and acknowledgment@mujeebishaqueLicenseClosed SourceProject statusComplete - Open for improvements.
Package: nfselib.barueri
Python Library to genereate Barueri NFS-eFree software: MIT licenseInstallationpip install nfselib.barueriYou can also install the in-development version with:pip install https://github.com/erpbrasil/nfselib.barueri/archive/main.zipDocumentationhttps://nfselibbarueri.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2023-04-07)First release on PyPI.
Package: nfselib.ginfes
Python Library to genereate GINFES NFS-EBiblioteca Python para ler e gerar NFSe’s / XML para o webservice GINFES;Esta biblioteca faz parte do projeto:https://erpbrasil.github.io/Para transmitir aconselhamos a biblioteca Python Zeep, ou então por exemplohttps://github.com/erpbrasil/erpbrasil.edoc.Ela foi gerada automaticamente através do generateDS e da ferramentahttps://github.com/erpbrasil/erpbrasil.edoc.genVerificar o conteúdo da pastascripte o manual do processo de relase emhttps://erpbrasil.github.io/docs/A bibliotca permite de:Gerar os XMLs dos documentos fiscais;Validar os dados comas mesmas validações dos XSD’s ao montar os objetos, o que evita detectar os erros apenas ao transmitir o XML;Importar XMLs e transforma-los em objetos Python. Usando um sistema de sub-classes, fica fácil mapear esses objetos em outros objetos ou adicionar qualquer método customizado;A biblioteca é:Simples e confiável. O código é gerado pelo generateDS a partir dos XSD’s do GINFES. Elereflete exatamente a especificação fiscalda versão do esquema escolhida sem que você deva se perguntar qual é o grau de aderência do código;Compatível comPython 3(e com Python 2 se botar patches no generateDS e usar uma versao anterior);Capaz de carregarvárias versões dos esquemas. Isso pode ser bem útil ao receber um documento fiscal com um leiaute antigo.Além disso, usando outros recursos do GenerateDS, é possível ir além dessa biblioteca e gerar automaticamente o modelo de dados do ERP.Pelo menos no ERP Odoo que tem um framework bastante poderoso. Sendo assim, é possivel montar dinamicamente as telas do usuário, a geração do XML ou a importação do XML quase que sem escrever código (apenas relacionar os campos mapeados com os campos já existentes do ERP).Fica então bem mais razoável para manter quando tem que atualizar os esquemas e assim também fica finalmente possível manter os dados do SPED dentro do ERP com um custo de manutenção compatível com o modelo open source.Você pode aprender mais sobre o generateDSaquie sobre o conjunto de tecnologias que estamos utilizando para facilitar o desenvolvimento de ERPs no Brasil:https://erpbrasil.github.io/docs/Instalaçãopip install nfselib.ginfesYou can also install the in-development version with:pip install https://github.com/erpbrasil/nfselib.ginfes/archive/master_gen.zipBranchsmaster - Script de geração e outros arquivos importantes (Customizações, Testes e Readme);master_gen - Versão final da bibliotaca para uso;Documentaçãohttps://erpbrasil.github.io/CréditosEsta é uma biblioteca criada atravês do esforço de das empresas:Akretionhttps://akretion.com/pt-BR/KMEEhttps://www.kmee.com.brLicençaFree software: MIT licenseDesenvolvimentoTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxAuthorsKMEE:Luis Felipe Mileo <mileo@kmee.com.br>Gabriel Cardoso de Faria <gabriel.cardoso@kmee.com.br>Luis Otávio Malta <luis.malta@kmee.com.br>Changelog0.1.0 (2020-11-08)First release on PyPI.
Package: nfselib.issnet
Python Library to genereate NFS-E IssnetBiblioteca Python para ler e gerar NFSe’s / XML para o webservice do ISSNETEsta biblioteca faz parte do projeto:https://erpbrasil.github.io/Para transmitir aconselhamos a biblioteca Python Zeep, ou então por exemplohttps://github.com/erpbrasil/erpbrasil.edoc.Ela foi gerada automaticamente através do generateDS e da ferramentahttps://github.com/erpbrasil/erpbrasil.edoc.genVerificar o conteúdo da pastascripte o manual do processo de relase emhttps://erpbrasil.github.io/docs/A bibliotca permite de:Gerar os XMLs dos documentos fiscais;Validar os dados comas mesmas validações dos XSD’s ao montar os objetos, o que evita detectar os erros apenas ao transmitir o XML;Importar XMLs e transforma-los em objetos Python. Usando um sistema de sub-classes, fica fácil mapear esses objetos em outros objetos ou adicionar qualquer método customizado;A biblioteca é:Simples e confiável. O código é gerado pelo generateDS a partir dos XSD’s do issnet. Elereflete exatamente a especificação fiscalda versão do esquema escolhida sem que você deva se perguntar qual é o grau de aderência do código;Compatível comPython 3(e com Python 2 se botar patches no generateDS e usar uma versao anterior);Capaz de carregarvárias versões dos esquemas. Isso pode ser bem útil ao receber um documento fiscal com um leiaute antigo.Além disso, usando outros recursos do GenerateDS, é possível ir além dessa biblioteca e gerar automaticamente o modelo de dados do ERP.Pelo menos no ERP Odoo que tem um framework bastante poderoso. Sendo assim, é possivel montar dinamicamente as telas do usuário, a geração do XML ou a importação do XML quase que sem escrever código (apenas relacionar os campos mapeados com os campos já existentes do ERP).Fica então bem mais razoável para manter quando tem que atualizar os esquemas e assim também fica finalmente possível manter os dados do SPED dentro do ERP com um custo de manutenção compatível com o modelo open source.Você pode aprender mais sobre o generateDSaquie sobre o conjunto de tecnologias que estamos utilizando para facilitar o desenvolvimento de ERPs no Brasil:https://erpbrasil.github.io/docs/Instalaçãopip install nfselib.issnetYou can also install the in-development version with:pip install https://github.com/erpbrasil/nfselib.issnet/archive/master_gen.zipBranchsmaster - Script de geração e outros arquivos importantes (Customizações, Testes e Readme);master_gen - Versão final da bibliotaca para uso;Documentaçãohttps://erpbrasil.github.io/CréditosEsta é uma biblioteca criada atravês do esforço de das empresas:Akretionhttps://akretion.com/pt-BR/KMEEhttps://www.kmee.com.brEscodoohttps://www.escodoo.com.brLicençaFree software: MIT licenseDesenvolvimentoTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxAuthorsKMEE:Luis Felipe Mileo <mileo@kmee.com.br>Escodoo:Marcel Savegnago <marcel.savegnago@escodoo.com.br>Changelog0.1.0 (2020-11-08)First release on PyPI.
Package: nfselib.paulistana
Python Library to genereate NFS-E PaulistanaBiblioteca Python para ler e gerar NFSe’s / XML para o webservice da prefeitura de São Paulo:Esta biblioteca faz parte do projeto:https://erpbrasil.github.io/Para transmitir aconselhamos a biblioteca Python Zeep, ou então por exemplohttps://github.com/erpbrasil/erpbrasil.edoc.Ela foi gerada automaticamente através do generateDS e da ferramentahttps://github.com/erpbrasil/erpbrasil.edoc.genVerificar o conteúdo da pastascripte o manual do processo de relase emhttps://erpbrasil.github.io/docs/A bibliotca permite de:Gerar os XMLs dos documentos fiscais;Validar os dados comas mesmas validações dos XSD’s ao montar os objetos, o que evita detectar os erros apenas ao transmitir o XML;Importar XMLs e transforma-los em objetos Python. Usando um sistema de sub-classes, fica fácil mapear esses objetos em outros objetos ou adicionar qualquer método customizado;A biblioteca é:Simples e confiável. O código é gerado pelo generateDS a partir dos XSD’s do paulistana. Elereflete exatamente a especificação fiscalda versão do esquema escolhida sem que você deva se perguntar qual é o grau de aderência do código;Compatível comPython 3(e com Python 2 se botar patches no generateDS e usar uma versao anterior);Capaz de carregarvárias versões dos esquemas. Isso pode ser bem útil ao receber um documento fiscal com um leiaute antigo.Além disso, usando outros recursos do GenerateDS, é possível ir além dessa biblioteca e gerar automaticamente o modelo de dados do ERP.Pelo menos no ERP Odoo que tem um framework bastante poderoso. Sendo assim, é possivel montar dinamicamente as telas do usuário, a geração do XML ou a importação do XML quase que sem escrever código (apenas relacionar os campos mapeados com os campos já existentes do ERP).Fica então bem mais razoável para manter quando tem que atualizar os esquemas e assim também fica finalmente possível manter os dados do SPED dentro do ERP com um custo de manutenção compatível com o modelo open source.Você pode aprender mais sobre o generateDSaquie sobre o conjunto de tecnologias que estamos utilizando para facilitar o desenvolvimento de ERPs no Brasil:https://erpbrasil.github.io/docs/Instalaçãopip install nfselib.paulistanaYou can also install the in-development version with:pip install https://github.com/erpbrasil/nfselib.paulistana/archive/master_gen.zipBranchsmaster - Script de geração e outros arquivos importantes (Customizações, Testes e Readme);master_gen - Versão final da bibliotaca para uso;Documentaçãohttps://erpbrasil.github.io/CréditosEsta é uma biblioteca criada atravês do esforço de das empresas:Akretionhttps://akretion.com/pt-BR/KMEEhttps://www.kmee.com.brLicençaFree software: MIT licenseDesenvolvimentoTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxAuthorsKMEE:Luis Felipe Mileo <mileo@kmee.com.br>Luis Otávio Malta <luis.malta@kmee.com.br>Changelog0.1.0 (2020-11-08)First release on PyPI.
Package: nfsnapi
A simple Python package with a couple of functions (and an exception
class) that make working with theNearlyFreeSpeech.NET APIeasier.nfsnapisupports Python 2.7 and later, and Python 3.2 and later. For
general usage there are no prerequisites other than a supported version
of Python, however to testnfsnapiwith Python 3.2 and earlier
versions themocklibrary is required.Synopsis>>> import nfsnapi
>>> # Replace USERNAME, API_KEY, and so on with actual values.
>>> nfsnapi.run_request("USERNAME", "API_KEY",
... "/account/ACCOUNT_NUMBER/balance")
u'10.56'
>>> nfsnapi.run_request("USERNAME", "API_KEY",
... "/dns/DOMAIN/listRRs", "type=A")
(A bunch of JSON not shown.)
>>> # And so on...InstallationThe usualpython setup.py installdance will installnfsnapiif
grabbing thesource distribution. Alternatively,nfsnapiis
available fromPyPIvia your Python package manager of choice.Documentation and TestingThenfsnapimodule is heavily documented. Runningnfnsapi-tests.pywill, funnily enough, run some tests. The tests
require a configuration file with API credentials and what-not (seenfnsapi-tests.py’s docstring for more information), and themocklibrary if testing with Python 3.2 or earlier.There’s support forTox, which will run the tests on all available
and supported versions of Python.Licencenfsnapiwas written by Damien Dart, <damiendart@pobox.com>. This is
free and unencumbered software released into the public domain. For more
information, please refer to the accompanying “UNLICENCE” file.